EpiExp-NBER.bib

@inproceedings{bauckhage2011insights,
  author =        {Bauckhage, Christian},
  booktitle =     {Proceedings of the International AAAI Conference on
                   Web and Social Media},
  title =         {Insights into internet memes},
  volume =        {5},
  year =          {2011},
}

@article{simon_behavioral_1984,
  author =        {Simon, Herbert A.},
  journal =       {Journal of Economic Behavior \& Organization},
  month =         mar,
  number =        {1},
  pages =         {35--55},
  series =        {A special {Issue} on {The} {Dynamics} of {Market}
                   {Economies} in commemoration of the birth of {Joseph}
                   {A}. {Schumpeter} in 1883},
  title =         {On the behavioral and rational foundations of
                   economic dynamics},
  volume =        {5},
  year =          {1984},
  abstract =      {Existing uncertainties about the correct explanations
                   for economic growth and business cycles cannot be
                   settled by aggregative analysis within the
                   neoclassical framework. Current disputes in theory
                   rest largely on ad hoc, casually empirical,
                   assumptions about departures from perfect rationality
                   under uncertainty. Such disputes can only be settled
                   by painstaking microeconomic empirical study of human
                   decision making and problem solving. Microeconomic
                   research of the kinds that are required can receive
                   powerful guidance from the theories of human thinking
                   that have been developed and tested over the past
                   twenty five years by cognitive psychologists.},
  doi =           {10.1016/0167-2681(84)90025-8},
  issn =          {0167-2681},
  language =      {en},
  url =           {http://www.sciencedirect.com/science/article/pii/
                  0167268184900258},
}

@article{shiller2017narrative,
  author =        {Shiller, Robert J},
  journal =       {American Economic Review},
  number =        {4},
  pages =         {967--1004},
  title =         {Narrative economics},
  volume =        {107},
  year =          {2017},
}

@article{arrow_classificatory_1969,
  author =        {Arrow, Kenneth J.},
  journal =       {The American Economic Review},
  number =        {2},
  pages =         {29--35},
  title =         {Classificatory {Notes} on the {Production} and
                   {Transmission} of {Technological} {Knowledge}},
  volume =        {59},
  year =          {1969},
  issn =          {0002-8282},
  url =           {https://www.jstor.org/stable/1823650},
}

@techreport{Inception2010,
  author =        {Nolan, Christopher},
  institution =   {Producers: Christopher Nolan, Emma Thomas; Director:
                   Christopher Nolan; Screenwriter: Christopher Nolan;
                   Studios: Legendary Pictures, Syncopy},
  publisher =     {Insight Editions},
  title =         {Inception [Motion Picture]: the Shooting Script},
  year =          {2010},
}

@book{humeenquiry,
  author =        {Hume, David},
  publisher =     {Andrew Millar},
  title =         {An Enquiry Concerning Human Understanding},
  year =          {1748},
}

@book{smithwealth,
  author =        {Smith, Adam},
  month =         {March},
  publisher =     {W. Strahan and T. Cadell, London},
  title =         {The Wealth of Nations (1776)},
  year =          {1776},
}

@book{rasmussen2017infidel,
  author =        {Rasmussen, Dennis C},
  publisher =     {Princeton University Press},
  title =         {The infidel and the professor},
  year =          {2017},
}

@article{nedic2019graph,
  author =        {Nedi{\'c}, Angelia and Olshevsky, Alex and
                   Uribe, C{\'e}sar A},
  journal =       {Scientific reports},
  number =        {1},
  pages =         {1--16},
  publisher =     {Nature Publishing Group},
  title =         {Graph-theoretic analysis of belief system dynamics
                   under logic constraints},
  volume =        {9},
  year =          {2019},
}

@article{sims2003implications,
  author =        {Sims, Christopher A},
  journal =       {Journal of monetary Economics},
  number =        {3},
  pages =         {665--690},
  publisher =     {Elsevier},
  title =         {Implications of rational inattention},
  volume =        {50},
  year =          {2003},
}

@article{bordalo2018diagnostic,
  author =        {Bordalo, Pedro and Gennaioli, Nicola and
                   Shleifer, Andrei},
  journal =       {The Journal of Finance},
  number =        {1},
  pages =         {199--227},
  publisher =     {Wiley Online Library},
  title =         {Diagnostic expectations and credit cycles},
  volume =        {73},
  year =          {2018},
}

@article{daniel1998investor,
  author =        {Daniel, Kent and Hirshleifer, David and
                   Subrahmanyam, Avanidhar},
  journal =       {the Journal of Finance},
  number =        {6},
  pages =         {1839--1885},
  publisher =     {Wiley Online Library},
  title =         {Investor psychology and security market under-and
                   overreactions},
  volume =        {53},
  year =          {1998},
}

@article{gabaix2020behavioral,
  author =        {Gabaix, Xavier},
  journal =       {American Economic Review},
  number =        {8},
  pages =         {2271--2327},
  title =         {A behavioral New Keynesian model},
  volume =        {110},
  year =          {2020},
}

@techreport{ilut2020economic,
  author =        {Ilut, Cosmin L and Valchev, Rosen},
  institution =   {National Bureau of Economic Research},
  title =         {Economic agents as imperfect problem solvers},
  year =          {2020},
}

@article{hommes2021behavioral,
  author =        {Hommes, Cars},
  journal =       {Journal of Economic Literature},
  number =        {1},
  pages =         {149--219},
  title =         {Behavioral and experimental macroeconomics and policy
                   analysis: A complex systems approach},
  volume =        {59},
  year =          {2021},
}

@article{kermack_contribution_1927,
  author =        {Kermack, William Ogilvy and McKendrick, A. G.},
  journal =       {Proceedings of the Royal Society of London. Series A,
                   Containing Papers of a Mathematical and Physical
                   Character},
  month =         aug,
  number =        {772},
  pages =         {700--721},
  title =         {A contribution to the mathematical theory of
                   epidemics},
  volume =        {115},
  year =          {1927},
  abstract =      {(1) One of the most striking features in the study of
                   epidemics is the difficulty of finding a causal
                   factor which appears to be adequate to account for
                   the magnitude of the frequent epidemics of disease
                   which visit almost every population. It was with a
                   view to obtaining more insight regarding the effects
                   of the various factors which govern the spread of
                   contagious epidemics that the present investigation
                   was undertaken. Reference may here be made to the
                   work of Ross and Hudson (1915-17) in which the same
                   problem is attacked. The problem is here carried to a
                   further stage, and it is considered from a point of
                   view which is in one sense more general. The problem
                   may be summarised as follows: One (or more) infected
                   person is introduced into a community of individuals,
                   more or less susceptible to the disease in question.
                   The disease spreads from the affected to the
                   unaffected by contact infection. Each infected person
                   runs through the course of his sickness, and finally
                   is removed from the number of those who are sick, by
                   recovery or by death. The chances of recovery or
                   death vary from day to day during the course of his
                   illness. The chances that the affected may convey
                   infection to the unaffected are likewise dependent
                   upon the stage of the sickness. As the epidemic
                   spreads, the number of unaffected members of the
                   community becomes reduced. Since the course of an
                   epidemic is short compared with the life of an
                   individual, the population may be considered as
                   remaining constant, except in as far as it is
                   modified by deaths due to the epidemic disease
                   itself. In the course of time the epidemic may come
                   to an end. One of the most important probems in
                   epidemiology is to ascertain whether this termination
                   occurs only when no susceptible individuals are left,
                   or whether the interplay of the various factors of
                   infectivity, recovery and mortality, may result in
                   termination, whilst many susceptible individuals are
                   still present in the unaffected population. It is
                   difficult to treat this problem in its most general
                   aspect. In the present communication discussion will
                   be limited to the case in which all members of the
                   community are initially equally susceptible to the
                   disease, and it will be further assumed that complete
                   immunity is conferred by a single infection.},
  doi =           {10.1098/rspa.1927.0118},
}

@article{gourieroux2020time,
  author =        {Gourieroux, Christian and Jasiak, Joann},
  journal =       {Journal of Econometrics},
  publisher =     {Elsevier},
  title =         {Time varying Markov process with partially observed
                   aggregate data: An application to coronavirus},
  year =          {2020},
}

@incollection{browning_chapter_1999,
  author =        {Browning, Martin and Hansen, Lars Peter and
                   Heckman, James J.},
  booktitle =     {Handbook of {Macroeconomics}},
  month =         jan,
  pages =         {543--633},
  publisher =     {Elsevier},
  title =         {Chapter 8 {Micro} data and general equilibrium
                   models},
  volume =        {1},
  year =          {1999},
  abstract =      {Dynamic general equilibrium models are required to
                   evaluate policies applied at the national level. To
                   use these models to make quantitative forecasts
                   requires knowledge of an extensive array of parameter
                   values for the economy at large. This essay describes
                   the parameters required for different economic
                   models, assesses the discordance between the
                   macromodels used in policy evaluation and the
                   microeconomic models used to generate the empirical
                   evidence. For concreteness, we focus on two general
                   equilibrium models: the stochastic growth model
                   extended to include some forms of heterogeneity and
                   the overlapping generations model enriched to
                   accommodate human capital formation.},
  doi =           {10.1016/S1574-0048(99)01011-3},
  language =      {en},
  url =           {http://www.sciencedirect.com/science/article/pii/
                  S1574004899010113},
}

@article{kmpHandbook,
  author =        {Krueger, Dirk and Mitman, Kurt and Perri, Fabrizio},
  journal =       {Handbook of Macroeconomics},
  pages =         {843--921},
  publisher =     {Elsevier},
  title =         {Macroeconomics and Household Heterogeneity},
  volume =        {2},
  year =          {2016},
  abstract =      {The goal of this chapter is to study how, and by how
                   much, household income, wealth, and preference
                   heterogeneity amplify and propagate a macroeconomic
                   shock. We focus on the U.S. Great Recession of
                   2007-2009 and proceed in two steps. First, using data
                   from the Panel Study of Income Dynamics, we document
                   the patterns of household income, consumption and
                   wealth inequality before and during the Great
                   Recession. We then investigate how households in
                   different segments of the wealth distribution were
                   affected by income declines, and how they changed
                   their expenditures differentially during the
                   aggregate downturn. Motivated by this evidence, we
                   study several variants of a standard heterogeneous
                   household model with aggregate shocks and an
                   endogenous cross-sectional wealth distribution. Our
                   key finding is that wealth inequality can
                   significantly amplify the impact of an aggregate
                   shock, and it does so if the distribution features a
                   sufficiently large fraction of households with very
                   little net worth that sharply increase their saving
                   (i.e. they are not hand-to mouth) as the recession
                   hits. We document that both these features are
                   observed in the PSID. We also investigate the role
                   that social insurance policies, such as unemployment
                   insurance, play in shaping the cross-sectional income
                   and wealth distribution, and through it, the dynamics
                   of business cycles.},
  doi =           {10.3386/w22319},
  url =           {http://www.nber.org/papers/w22319},
}

@misc{violante_marginal_2021,
  author =        {Violante, Gianluca},
  howpublished =  {JJ Laffont Lecture, EEA-ESEM Congress},
  title =         {The {Marginal} {Propensity} to {Consume} in
                   {Macroeconomics}},
  year =          {2021},
}

@article{gmsuBeliefs,
  author =        {Giglio, Stefano and Maggiori, Matteo and
                   Stroebel, Johannes and Utkus, Stephen},
  journal =       {American Economic Review},
  number =        {5},
  pages =         {1481--1522},
  title =         {Five facts about beliefs and portfolios},
  volume =        {111},
  year =          {2021},
}

@unpublished{Velasquez2022,
  author =        {Vel\'asquez-Giraldo, Mateo},
  month =         {January},
  note =          {Unpublished manuscript},
  title =         {Beliefs, Stockholding and Wealth Accumulation
                   Throughout the Life Cycle},
  year =          {2022},
  url =           {https://github.com/Mv77/BeliefsStocks_public},
}

@article{manski2017survey,
  author =        {Manski, CF},
  journal =       {NBER Macro Annual},
  title =         {Survey Measurement of Probabilistic Macroeconomic
                   Expectations: Progress and Promise. forthcoming in
                   NBER Macro Annual},
  year =          {2017},
}

@article{erdos1960evolution,
  author =        {Erdos, Paul and R{\'e}nyi, Alfr{\'e}d and others},
  journal =       {Publ. Math. Inst. Hung. Acad. Sci},
  number =        {1},
  pages =         {17--60},
  publisher =     {Citeseer},
  title =         {On the evolution of random graphs},
  volume =        {5},
  year =          {1960},
}

@book{jackson_social_2010,
  author =        {Jackson, Matthew O.},
  month =         nov,
  note =          {Google-Books-ID: rFzHinVAq7gC},
  publisher =     {Princeton University Press},
  title =         {Social and {Economic} {Networks}},
  year =          {2010},
  abstract =      {Networks of relationships help determine the careers
                   that people choose, the jobs they obtain, the
                   products they buy, and how they vote. The many
                   aspects of our lives that are governed by social
                   networks make it critical to understand how they
                   impact behavior, which network structures are likely
                   to emerge in a society, and why we organize ourselves
                   as we do. In Social and Economic Networks, Matthew
                   Jackson offers a comprehensive introduction to social
                   and economic networks, drawing on the latest findings
                   in economics, sociology, computer science, physics,
                   and mathematics. He provides empirical background on
                   networks and the regularities that they exhibit, and
                   discusses random graph-based models and strategic
                   models of network formation. He helps readers to
                   understand behavior in networked societies, with a
                   detailed analysis of learning and diffusion in
                   networks, decision making by individuals who are
                   influenced by their social neighbors, game theory and
                   markets on networks, and a host of related subjects.
                   Jackson also describes the varied statistical and
                   modeling techniques used to analyze social networks.
                   Each chapter includes exercises to aid students in
                   their analysis of how networks function. This book is
                   an indispensable resource for students and
                   researchers in economics, mathematics, physics,
                   sociology, and business.},
  isbn =          {978-1-4008-3399-3},
  language =      {en},
}

@article{newman2002spread,
  author =        {Newman, Mark EJ},
  journal =       {Physical review E},
  number =        {1},
  pages =         {016128},
  publisher =     {APS},
  title =         {Spread of epidemic disease on networks},
  volume =        {66},
  year =          {2002},
}

@book{easley_kleinberg_2010,
  author =        {Easley, David and Kleinberg, Jon},
  publisher =     {Cambridge University Press},
  title =         {Networks, Crowds, and Markets: Reasoning about a
                   Highly Connected World},
  year =          {2010},
  doi =           {10.1017/CBO9780511761942},
}

@article{wsSmallWorld,
  author =        {Watts, Duncan J and Strogatz, Steven H},
  journal =       {nature},
  number =        {6684},
  pages =         {440--442},
  publisher =     {Nature Publishing Group},
  title =         {Collective dynamics of `small-world' networks},
  volume =        {393},
  year =          {1998},
}

@book{barabasi2016network,
  author =        {Barab{\'a}si, Albert-L{\'a}szl{\'o} and others},
  publisher =     {Cambridge University Press},
  title =         {Network Science},
  year =          {2016},
}

@article{milgram1967small,
  author =        {Milgram, Stanley},
  journal =       {Psychology today},
  number =        {1},
  pages =         {60--67},
  publisher =     {New York},
  title =         {The small world problem},
  volume =        {2},
  year =          {1967},
}

@book{guareSixDegrees,
  author =        {Guare, John},
  publisher =     {Vintage},
  title =         {Six degrees of separation: A play},
  year =          {1990},
}

@article{moore2000epidemics,
  author =        {Moore, Cristopher and Newman, Mark EJ},
  journal =       {Physical Review E},
  number =        {5},
  pages =         {5678},
  publisher =     {APS},
  title =         {Epidemics and percolation in small-world networks},
  volume =        {61},
  year =          {2000},
}

@article{allard2020role,
  author =        {Allard, Antoine and Moore, Cristopher and
                   Scarpino, Samuel V and Althouse, Benjamin M and
                   H{\'e}bert-Dufresne, Laurent},
  journal =       {arXiv preprint arXiv:2005.11283},
  title =         {The role of directionality, heterogeneity and
                   correlations in epidemic risk and spread},
  volume =        {17},
  year =          {2020},
  doi =           {10.1098/rsif.2020.0393},
  issn =          {1742-5689},
}

@article{degroot1974reaching,
  author =        {DeGroot, Morris H},
  journal =       {Journal of the American Statistical association},
  number =        {345},
  pages =         {118--121},
  publisher =     {Taylor \& Francis},
  title =         {Reaching a consensus},
  volume =        {69},
  year =          {1974},
}

@article{golub2010naive,
  author =        {Golub, Benjamin and Jackson, Matthew O},
  journal =       {American Economic Journal: Microeconomics},
  number =        {1},
  pages =         {112--49},
  title =         {Naive learning in social networks and the wisdom of
                   crowds},
  volume =        {2},
  year =          {2010},
}

@article{acemouglu2013opinion,
  author =        {Acemo{\u{g}}lu, Daron and Como, Giacomo and
                   Fagnani, Fabio and Ozdaglar, Asuman},
  journal =       {Mathematics of Operations Research},
  number =        {1},
  pages =         {1--27},
  publisher =     {Informs},
  title =         {Opinion fluctuations and disagreement in social
                   networks},
  volume =        {38},
  year =          {2013},
  doi =           {10.1287/moor.1120.0570},
  issn =          {0364-765X},
}

@article{sikder2020minimalistic,
  author =        {Sikder, Orowa and Smith, Robert E and Vivo, Pierpaolo and
                   Livan, Giacomo},
  journal =       {Scientific reports},
  number =        {1},
  pages =         {1--11},
  publisher =     {Nature Publishing Group},
  title =         {A minimalistic model of bias, polarization and
                   misinformation in social networks},
  volume =        {10},
  year =          {2020},
}

@techreport{shiller2020popular,
  author =        {Shiller, Robert J},
  institution =   {National Bureau of Economic Research},
  title =         {Popular economic narratives advancing the longest US
                   economic expansion 2009-2019},
  year =          {2020},
}

@article{hanchen_inflation_2017,
  author =        {Hachem, Kinda and Wu, Jing Cynthia},
  journal =       {Journal of Money, Credit and Banking},
  number =        {8},
  pages =         {1673--1713},
  publisher =     {Wiley Online Library},
  title =         {Inflation announcements and social dynamics},
  volume =        {49},
  year =          {2017},
}

@article{meeuwis2018belief,
  author =        {Meeuwis, Maarten and Parker, Jonathan A and
                   Schoar, Antoinette and Simester, Duncan I},
  journal =       {The Journal of Finance},
  title =         {Belief Disagreement and Portfolio Choice},
  volume =        {forthcoming},
  year =          {2021},
}

@article{miller2012note,
  author =        {Miller, Joel C},
  journal =       {Bulletin of mathematical biology},
  number =        {9},
  pages =         {2125--2141},
  publisher =     {Springer},
  title =         {A note on the derivation of epidemic final sizes},
  volume =        {74},
  year =          {2012},
}

@article{harko2014exact,
  author =        {Harko, Tiberiu and Lobo, Francisco SN and Mak, MK},
  journal =       {Applied Mathematics and Computation},
  pages =         {184--194},
  publisher =     {Elsevier},
  title =         {Exact analytical solutions of the
                   Susceptible-Infected-Recovered (SIR) epidemic model
                   and of the SIR model with equal death and birth
                   rates},
  volume =        {236},
  year =          {2014},
}

@book{bailey1975mathematical,
  author =        {Bailey, Norman TJ and others},
  publisher =     {Charles Griffin \& Company Ltd, 5a Crendon Street,
                   High Wycombe, Bucks HP13 6LE.},
  title =         {The mathematical theory of infectious diseases and
                   its applications},
  year =          {1975},
}

@book{anderson_infectious_1992,
  author =        {Anderson, Roy M. and Anderson, B. and May, Robert M.},
  month =         aug,
  publisher =     {OUP Oxford},
  title =         {Infectious {Diseases} of {Humans}: {Dynamics} and
                   {Control}},
  year =          {1992},
  abstract =      {This book deals with infectious diseases -- viral,
                   bacterial, protozoan and helminth -- in terms of the
                   dynamics of their interaction with host populations.
                   The book combines mathematical models with extensive
                   use of epidemiological and other data. This analytic
                   framework is highly useful for the evaluation of
                   public health strategies aimed at controlling or
                   eradicating particular infections. Such a framework
                   is increasingly important in light of the widespread
                   concern for primary health care programs aimed at
                   such diseases as measles, malaria, river blindness,
                   sleeping sickness, and schistosomiasis, and the
                   advent of AIDS/HIV and other emerging viruses.
                   Throughout the book, the mathematics is used as a
                   tool for thinking clearly about fundamental and
                   applied problems having to do with infectious
                   diseases. The book is divided into two parts, one
                   dealing with microparasites (viruses, bacteria and
                   protozoans) and the other with macroparasites
                   (helminths and parasitic arthropods). Each part
                   begins with simple models, developed in a
                   biologically intuitive way, and then goes on to
                   develop more complicated and realistic models as
                   tools for public health planning. The book
                   synthesizes previous work in this rapidly growing
                   field (much of which is scattered between the
                   ecological and the medical literature) with a good
                   deal of new material.},
  isbn =          {978-0-19-854040-3},
  language =      {en},
}

@article{hethcote_mathematics_2000,
  author =        {Hethcote, Herbert W.},
  journal =       {SIAM Review},
  month =         jan,
  number =        {4},
  pages =         {599--653},
  title =         {The {Mathematics} of {Infectious} {Diseases}},
  volume =        {42},
  year =          {2000},
  abstract =      {Many models for the spread of infectious diseases in
                   populations have been analyzed mathematically and
                   applied to specific diseases. Threshold theorems
                   involving the basic reproduction number R0, the
                   contact number σ, and the replacement number R are
                   reviewed for the classic SIR epidemic and endemic
                   models. Similar results with new expressions for R0
                   are obtained for MSEIR and SEIR endemic models with
                   either continuous age or age groups. Values of R0 and
                   σ are estimated for various diseases including
                   measles in Niger and pertussis in the United States.
                   Previous models with age structure, heterogeneity,
                   and spatial structure are surveyed.},
  doi =           {10.1137/S0036144500371907},
  issn =          {0036-1445, 1095-7200},
  language =      {en},
}

@article{brauer2017mathematical,
  author =        {Brauer, Fred},
  journal =       {Infectious Disease Modelling},
  number =        {2},
  pages =         {113--127},
  publisher =     {Elsevier},
  title =         {Mathematical epidemiology: Past, present, and future},
  volume =        {2},
  year =          {2017},
}

@article{granovetter1978threshold,
  author =        {Granovetter, Mark},
  journal =       {American journal of sociology},
  number =        {6},
  pages =         {1420--1443},
  publisher =     {University of Chicago Press},
  title =         {Threshold models of collective behavior},
  volume =        {83},
  year =          {1978},
}

@article{jackson2007diffusion,
  author =        {Jackson, Matthew O and Yariv, Leeat},
  journal =       {American Economic Review},
  number =        {2},
  pages =         {92--98},
  title =         {Diffusion of behavior and equilibrium properties in
                   network games},
  volume =        {97},
  year =          {2007},
}

@article{glasserman2016contagion,
  author =        {Glasserman, Paul and Young, H Peyton},
  journal =       {Journal of Economic Literature},
  number =        {3},
  pages =         {779--831},
  title =         {Contagion in financial networks},
  volume =        {54},
  year =          {2016},
}

@article{shiller1989survey,
  author =        {Shiller, Robert J and Pound, John},
  journal =       {Journal of Economic Behavior \& Organization},
  number =        {1},
  pages =         {47--66},
  publisher =     {Elsevier},
  title =         {Survey evidence on diffusion of interest and
                   information among investors},
  volume =        {12},
  year =          {1989},
}

@article{kroger2020analytical,
  author =        {Kr{\"o}ger, Martin and Schlickeiser, Reinhard},
  journal =       {Journal of Physics A: Mathematical and Theoretical},
  number =        {50},
  pages =         {505601},
  publisher =     {IOP Publishing},
  title =         {Analytical solution of the SIR-model for the temporal
                   evolution of epidemics. Part A: time-independent
                   reproduction factor},
  volume =        {53},
  year =          {2020},
}

@article{okabe2021microscopic,
  author =        {Okabe, Yutaka and Shudo, Akira},
  journal =       {Mathematics},
  number =        {9},
  pages =         {932},
  publisher =     {Multidisciplinary Digital Publishing Institute},
  title =         {Microscopic Numerical Simulations of Epidemic Models
                   on Networks},
  volume =        {9},
  year =          {2021},
}

@article{banerjee2013diffusion,
  author =        {Banerjee, Abhijit and Chandrasekhar, Arun G and
                   Duflo, Esther and Jackson, Matthew O},
  journal =       {Science},
  number =        {6144},
  publisher =     {American Association for the Advancement of Science},
  title =         {The diffusion of microfinance},
  volume =        {341},
  year =          {2013},
}

@article{rogers1962diffusion,
  author =        {Rogers, Everett M and others},
  journal =       {Diffusion of innovations.},
  publisher =     {New York: Free Pr. of Glencoe.},
  title =         {Diffusion of innovations.},
  year =          {1962},
}

@article{young2009innovation,
  author =        {Young, H Peyton},
  journal =       {American economic review},
  number =        {5},
  pages =         {1899--1924},
  title =         {Innovation diffusion in heterogeneous populations:
                   Contagion, social influence, and social learning},
  volume =        {99},
  year =          {2009},
}

@article{munshi2004social,
  author =        {Munshi, Kaivan},
  journal =       {Journal of development Economics},
  number =        {1},
  pages =         {185--213},
  publisher =     {Elsevier},
  title =         {Social learning in a heterogeneous population:
                   technology diffusion in the Indian Green Revolution},
  volume =        {73},
  year =          {2004},
}

@article{comin2010exploration,
  author =        {Comin, Diego and Hobijn, Bart},
  journal =       {American Economic Review},
  number =        {5},
  pages =         {2031--59},
  title =         {An exploration of technology diffusion},
  volume =        {100},
  year =          {2010},
}

@article{lucas2014knowledge,
  author =        {Lucas, Robert E and Moll, Benjamin},
  journal =       {Journal of Political Economy},
  number =        {1},
  pages =         {1--51},
  publisher =     {University of Chicago Press Chicago, IL},
  title =         {Knowledge growth and the allocation of time},
  volume =        {122},
  year =          {2014},
}

@article{fogli2021germs,
  author =        {Fogli, Alessandra and Veldkamp, Laura},
  journal =       {The Review of Economic Studies},
  number =        {3},
  pages =         {1074--1100},
  publisher =     {Oxford University Press},
  title =         {Germs, Social Networks, and Growth},
  volume =        {88},
  year =          {2021},
}

@book{vegaConfusion,
  author =        {De La Vega, Joseph},
  publisher =     {Amsterdam},
  title =         {Confusion de Confusiones},
  year =          {1688},
}

@book{mackay_memoirs_1852,
  author =        {Mackay, Charles},
  note =          {Google-Books-ID: JhU3AQAAMAAJ},
  publisher =     {Office of National illustrated library},
  title =         {Memoirs of {Extraordinary} {Popular} {Delusions} and
                   the {Madness} of {Crowds}},
  year =          {1852},
  language =      {en},
}

@book{lewis2011big,
  author =        {Lewis, Michael},
  publisher =     {Penguin UK},
  title =         {The big short: Inside the doomsday machine},
  year =          {2011},
}

@article{hirshleifer2020presidential,
  author =        {Hirshleifer, David},
  journal =       {The Journal of Finance},
  number =        {4},
  pages =         {1779--1831},
  publisher =     {Wiley Online Library},
  title =         {Presidential address: Social transmission bias in
                   economics and finance},
  volume =        {75},
  year =          {2020},
}

@article{akccay2021social,
  author =        {Ak{\c{c}}ay, Erol and Hirshleifer, David},
  journal =       {Proceedings of the National Academy of Sciences},
  number =        {26},
  publisher =     {National Acad Sciences},
  title =         {Social finance as cultural evolution, transmission
                   bias, and market dynamics},
  volume =        {118},
  year =          {2021},
  doi =           {10.1073/pnas.2015568118},
  issn =          {0027-8424},
}

@article{hirshleifer2015behavioral,
  author =        {Hirshleifer, David},
  journal =       {Annual Review of Financial Economics},
  pages =         {133--159},
  publisher =     {Annual Reviews},
  title =         {Behavioral finance},
  volume =        {7},
  year =          {2015},
}

@article{kuchler2021social,
  author =        {Kuchler, Theresa and Stroebel, Johannes},
  journal =       {Annual Review of Financial Economics},
  publisher =     {Annual Reviews},
  title =         {Social finance},
  volume =        {13},
  year =          {2021},
}

@article{shiller1984stock,
  author =        {Shiller, Robert J},
  journal =       {Brookings papers on economic activity},
  number =        {2},
  pages =         {457--510},
  publisher =     {JSTOR},
  title =         {Stock prices and social dynamics},
  volume =        {1984},
  year =          {1984},
}

@article{shive2010epidemic,
  author =        {Shive, Sophie},
  journal =       {Journal of Financial and Quantitative Analysis},
  pages =         {169--198},
  publisher =     {JSTOR},
  title =         {An epidemic model of investor behavior},
  year =          {2010},
}

@article{huang2021rate,
  author =        {Huang, Shiyang and Hwang, Byoung-Hyoun and Lou, Dong},
  journal =       {Journal of Financial Economics},
  publisher =     {Elsevier},
  title =         {The rate of communication},
  year =          {2021},
}

@article{han2022social,
  author =        {Han, Bing and Hirshleifer, David and Walden, Johan},
  journal =       {Journal of Financial and Quantitative Analysis},
  number =        {1},
  pages =         {390--412},
  publisher =     {Cambridge University Press},
  title =         {Social transmission bias and investor behavior},
  volume =        {57},
  year =          {2022},
}

@article{burnside_understanding_2016,
  author =        {Burnside, Craig and Eichenbaum, Martin and
                   Rebelo, Sergio},
  journal =       {Journal of Political Economy},
  month =         aug,
  number =        {4},
  pages =         {1088--1147},
  title =         {Understanding {Booms} and {Busts} in {Housing}
                   {Markets}},
  volume =        {124},
  year =          {2016},
  abstract =      {Some booms in housing prices are followed by busts.
                   Others are not. It is generally difficult to find
                   observable fundamentals that are useful for
                   predicting whether a boom will turn into a bust or
                   not. We develop a model consistent with these
                   observations. Agents have heterogeneous expectations
                   about long-run fundamentals but change their views
                   because of “social dynamics.” Agents with tighter
                   priors are more likely to convert others to their
                   beliefs. Boom-bust episodes typically occur when
                   skeptical agents happen to be correct. The booms that
                   are not followed by busts typically occur when
                   optimistic agents happen to be correct.},
  doi =           {10.1086/686732},
  issn =          {0022-3808},
}

@article{lo2004adaptive,
  author =        {Lo, Andrew W},
  journal =       {The Journal of Portfolio Management},
  number =        {5},
  pages =         {15--29},
  publisher =     {Institutional Investor Journals Umbrella},
  title =         {The adaptive markets hypothesis},
  volume =        {30},
  year =          {2004},
}

@article{brennan2011origin,
  author =        {Brennan, Thomas J and Lo, Andrew W},
  journal =       {The Quarterly Journal of Finance},
  number =        {01},
  pages =         {55--108},
  publisher =     {World Scientific},
  title =         {The origin of behavior},
  volume =        {1},
  year =          {2011},
}

@article{lebaronAgentCompFinance,
  author =        {LeBaron, Blake},
  journal =       {Handbook of computational economics},
  pages =         {1187--1233},
  publisher =     {Elsevier},
  title =         {Agent-based computational finance},
  volume =        {2},
  year =          {2006},
}

@article{hirshleifer2021social,
  author =        {Hirshleifer, David A and Lo, Andrew W and
                   Zhang, Ruixun},
  journal =       {Available at SSRN},
  title =         {Social Contagion and the Survival of Diverse
                   Investment Styles},
  year =          {2021},
}

@inproceedings{dmw2022inflationexpectation,
  author =        {D'Acunto, Francesco and Malmendier, Ulrike and
                   Weber, Michael},
  booktitle =     {Handbook of Economic Expectations},
  title =         {What do the data tell us about inflation
                   expectations?},
  year =          {2022},
}

@inproceedings{bv2022bayesianlearning,
  author =        {Baley, Isaac and Veldkamp, Laura},
  booktitle =     {Handbook of Economic Expectations},
  title =         {Bayesian learning},
  year =          {2022},
}

@article{carroll2003macroeconomic,
  author =        {Carroll, Christopher D},
  journal =       {the Quarterly Journal of economics},
  number =        {1},
  pages =         {269--298},
  publisher =     {MIT Press},
  title =         {Macroeconomic expectations of households and
                   professional forecasters},
  volume =        {118},
  year =          {2003},
}

@article{easaw2015households,
  author =        {Easaw, Joshy and Mossay, Pascal},
  journal =       {The BE Journal of Macroeconomics},
  number =        {1},
  pages =         {339--363},
  publisher =     {De Gruyter},
  title =         {Households forming macroeconomic expectations:
                   inattentive behavior with social learning},
  volume =        {15},
  year =          {2015},
}

@article{lamla2014role,
  author =        {Lamla, Michael J and Lein, Sarah M},
  journal =       {Journal of Economic Behavior \& Organization},
  pages =         {62--77},
  publisher =     {Elsevier},
  title =         {The role of media for consumers' inflation
                   expectation formation},
  volume =        {106},
  year =          {2014},
}

@article{larsen2021news,
  author =        {Larsen, Vegard H and Thorsrud, Leif Anders and
                   Zhulanova, Julia},
  journal =       {Journal of Monetary Economics},
  pages =         {507--520},
  publisher =     {Elsevier},
  title =         {News-driven inflation expectations and information
                   rigidities},
  volume =        {117},
  year =          {2021},
}

@article{mr2002Sticky,
  author =        {Mankiw, N Gregory and Reis, Ricardo},
  journal =       {The Quarterly Journal of Economics},
  number =        {4},
  pages =         {1295--1328},
  publisher =     {MIT Press},
  title =         {Sticky information versus sticky prices: a proposal
                   to replace the New Keynesian Phillips curve},
  volume =        {117},
  year =          {2002},
}

@article{mankiw2007sticky,
  author =        {Mankiw, N Gregory and Reis, Ricardo},
  journal =       {Journal of the European Economic Association},
  number =        {2-3},
  pages =         {603--613},
  publisher =     {Oxford University Press},
  title =         {Sticky information in general equilibrium},
  volume =        {5},
  year =          {2007},
}

@article{branchHeteroExp,
  author =        {Branch, William A.},
  journal =       {The Economic Journal},
  month =         {06},
  number =        {497},
  pages =         {592-621},
  title =         {{The Theory of Rationally Heterogeneous Expectations:
                   Evidence from Survey Data on Inflation Expectations}},
  volume =        {114},
  year =          {2004},
  abstract =      {{Previous work with survey data on inflationary
                   expectations casts doubt on the Rational Expectations
                   Hypothesis. In this paper, we develop a model of
                   expectation formation where agents form their
                   forecasts of inflation by selecting a predictor
                   function from a set of costly alternatives whereby
                   they may rationally choose a method other than the
                   most accurate. We use this model to test whether
                   survey data exhibit rationally heterogeneous
                   expectations. Maximum likelihood is applied to a new
                   discrete choice setting where the observed variable
                   is continuous and the latent variable is discrete.
                   The results show there is dynamic switching that
                   depends on the relative mean squared errors of the
                   predictors.}},
  doi =           {10.1111/j.1468-0297.2004.00233.x},
  issn =          {0013-0133},
}

@article{shibata2019current,
  author =        {Shibata, Akihisa and Shintani, Mototsugu and
                   Tsuruga, Takayuki},
  journal =       {Journal of International Money and Finance},
  pages =         {153--176},
  publisher =     {Elsevier},
  title =         {Current account dynamics under information rigidity
                   and imperfect capital mobility},
  volume =        {92},
  year =          {2019},
}

@article{carroll2020sticky,
  author =        {Carroll, Christopher D and Crawley, Edmund and
                   Slacalek, Jiri and Tokuoka, Kiichi and
                   White, Matthew N},
  journal =       {American economic journal: macroeconomics},
  number =        {3},
  pages =         {40--76},
  title =         {Sticky expectations and consumption dynamics},
  volume =        {12},
  year =          {2020},
}

@techreport{auclert2020micro,
  author =        {Auclert, Adrien and Rognlie, Matthew and
                   Straub, Ludwig},
  institution =   {National Bureau of Economic Research},
  title =         {Micro jumps, macro humps: Monetary policy and
                   business cycles in an estimated HANK model},
  year =          {2020},
}

@article{angeletos2010noisy,
  author =        {Angeletos, George-Marios and La'o, Jennifer},
  journal =       {NBER Macroeconomics Annual},
  number =        {1},
  pages =         {319--378},
  publisher =     {The University of Chicago Press},
  title =         {Noisy Business Cycles},
  volume =        {24},
  year =          {2010},
}

@article{benhabib2015sentiments,
  author =        {Benhabib, Jess and Wang, Pengfei and Wen, Yi},
  journal =       {Econometrica},
  number =        {2},
  pages =         {549--585},
  publisher =     {Wiley Online Library},
  title =         {Sentiments and aggregate demand fluctuations},
  volume =        {83},
  year =          {2015},
}

@article{angeletos2018quantifying,
  author =        {Angeletos, George-Marios and Collard, Fabrice and
                   Dellas, Harris},
  journal =       {Econometrica},
  number =        {5},
  pages =         {1689--1726},
  publisher =     {Wiley Online Library},
  title =         {Quantifying confidence},
  volume =        {86},
  year =          {2018},
}

@article{angeletos2013sentiments,
  author =        {Angeletos, George-Marios and La'O, Jennifer},
  journal =       {Econometrica},
  number =        {2},
  pages =         {739--779},
  publisher =     {Wiley Online Library},
  title =         {Sentiments},
  volume =        {81},
  year =          {2013},
}

@techreport{han2019visibility,
  author =        {Han, Bing and Hirshleifer, David and Walden, Johan},
  institution =   {National Bureau of Economic Research},
  title =         {Visibility bias in the transmission of consumption
                   beliefs and undersaving},
  year =          {2019},
}

@article{brock1997rational,
  author =        {Brock, William A and Hommes, Cars H},
  journal =       {Econometrica: Journal of the Econometric Society},
  pages =         {1059--1095},
  publisher =     {JSTOR},
  title =         {A rational route to randomness},
  year =          {1997},
}

@article{arifovic2018learning,
  author =        {Arifovic, Jasmina and Schmitt-Groh{\'e}, Stephanie and
                   Uribe, Martin},
  journal =       {Journal of Economic Dynamics and Control},
  pages =         {120--136},
  publisher =     {Elsevier},
  title =         {Learning to live in a liquidity trap},
  volume =        {89},
  year =          {2018},
}

@article{tesfatsion2006agent,
  author =        {Tesfatsion, Leigh},
  journal =       {Handbook of computational economics},
  pages =         {831--880},
  publisher =     {Elsevier},
  title =         {Agent-based computational economics: A constructive
                   approach to economic theory},
  volume =        {2},
  year =          {2006},
}

@article{haldane_drawing_2019,
  author =        {Haldane, Andrew G. and Turrell, Arthur E.},
  journal =       {Journal of Evolutionary Economics},
  month =         mar,
  note =          {ZSCC: 0000061},
  number =        {1},
  pages =         {39--66},
  title =         {Drawing on different disciplines: macroeconomic
                   agent-based models},
  volume =        {29},
  year =          {2019},
  abstract =      {Macroeconomic modelling has been under intense
                   scrutiny since the Great Financial Crisis, when
                   serious shortcomings were exposed in the methodology
                   used to understand the economy as a whole. Criticism
                   has been levelled at the assumptions employed in the
                   dominant models, particularly that economic agents
                   are homogenous and optimising and that the economy is
                   equilibrating. In a related paper (Haldane and
                   Turrell Oxford Rev Econ Polic 34(1–2):219–251
                   2018), we argue that an interdisciplinary approach to
                   modelling in macroeconomics is beneficial. Here we
                   focus on what one such approach - agent-based
                   modelling, which has been extensively used across a
                   wide range of disciplines - could do for
                   macroeconomics. Agent-based models are complementary
                   to existing approaches to macroeconomics and are
                   particularly well-suited to answering questions where
                   complexity, heterogeneity, networks, and heuristics
                   play an important role.},
  doi =           {10.1007/s00191-018-0557-5},
  issn =          {1432-1386},
  language =      {en},
  url =           {https://doi.org/10.1007/s00191-018-0557-5},
}

@article{hommes2006heterogeneous,
  author =        {Hommes, Cars H},
  journal =       {Handbook of computational economics},
  pages =         {1109--1186},
  publisher =     {Elsevier},
  title =         {Heterogeneous agent models in economics and finance},
  volume =        {2},
  year =          {2006},
}

@article{ddAgentBasedMacro,
  author =        {Dawid, Herbert and Gatti, Domenico Delli},
  journal =       {Handbook of computational economics},
  pages =         {63--156},
  publisher =     {Elsevier},
  title =         {Agent-based macroeconomics},
  volume =        {4},
  year =          {2018},
}

@article{manski1993identification,
  author =        {Manski, Charles F},
  journal =       {The review of economic studies},
  number =        {3},
  pages =         {531--542},
  publisher =     {Wiley-Blackwell},
  title =         {Identification of endogenous social effects: The
                   reflection problem},
  volume =        {60},
  year =          {1993},
}

@techreport{allen2018social,
  author =        {Allen, Linda and Peng, Lin and Shan, Yu},
  institution =   {Working Paper. Zicklin School of Business, Baruch
                   College, New York},
  title =         {Social interactions and peer-to-peer lending
                   decisions},
  year =          {2018},
  doi =           {10.2139/ssrn.3537714},
  issn =          {1556-5068},
}

@article{cookson_why_2020,
  author =        {Cookson, J. Anthony and Niessner, Marina},
  journal =       {The Journal of Finance},
  number =        {1},
  pages =         {173--228},
  title =         {Why {Don}'t {We} {Agree}? {Evidence} from a {Social}
                   {Network} of {Investors}},
  volume =        {75},
  year =          {2020},
  abstract =      {We study sources of investor disagreement using
                   sentiment of investors from a social media investing
                   platform, combined with information on the users'
                   investment approaches (e.g., technical, fundamental).
                   We examine how much of overall disagreement is driven
                   by different information sets versus differential
                   interpretation of information by studying
                   disagreement within and across investment approaches.
                   Overall disagreement is evenly split between both
                   sources of disagreement, but within-group
                   disagreement is more tightly related to trading
                   volume than cross-group disagreement. Although both
                   sources of disagreement are important, our findings
                   suggest that information differences are more
                   important for trading than differences across market
                   approaches.},
  doi =           {10.1111/jofi.12852},
  issn =          {1540-6261},
  language =      {en},
}

@article{cookson2022echo,
  author =        {Cookson, J Anthony and Engelberg, Joseph and
                   Mullins, William},
  journal =       {Available at SSRN 3603107},
  title =         {Echo chambers},
  year =          {2022},
}

@article{bailey2018economic,
  author =        {Bailey, Michael and Cao, Ruiqing and Kuchler, Theresa and
                   Stroebel, Johannes},
  journal =       {Journal of Political Economy},
  number =        {6},
  pages =         {2224--2276},
  publisher =     {University of Chicago Press Chicago, IL},
  title =         {The economic effects of social networks: Evidence
                   from the housing market},
  volume =        {126},
  year =          {2018},
}

@article{bailey2019house,
  author =        {Bailey, Michael and D{\'a}vila, Eduardo and
                   Kuchler, Theresa and Stroebel, Johannes},
  journal =       {The Review of Economic Studies},
  number =        {6},
  pages =         {2403--2452},
  publisher =     {Oxford University Press},
  title =         {House price beliefs and mortgage leverage choice},
  volume =        {86},
  year =          {2019},
}

@inproceedings{kps2022housingexpectation,
  author =        {Kuchler, Theresa and Piazzesi, Monika and
                   Stroebel, Johannes},
  booktitle =     {Handbook of Economic Expectations},
  title =         {Housing market expectations},
  year =          {2022},
}

@article{bailey_social_2018,
  author =        {Bailey, Michael and Cao, Rachel and Kuchler, Theresa and
                   Stroebel, Johannes and Wong, Arlene},
  journal =       {Journal of Economic Perspectives},
  month =         aug,
  number =        {3},
  pages =         {259--280},
  title =         {Social {Connectedness}: {Measurement},
                   {Determinants}, and {Effects}},
  volume =        {32},
  year =          {2018},
  abstract =      {Social networks can shape many aspects of social and
                   economic activity: migration and trade, job-seeking,
                   innovation, consumer preferences and sentiment,
                   public health, social mobility, and more. In turn,
                   social networks themselves are associated with
                   geographic proximity, historical ties, political
                   boundaries, and other factors. Traditionally, the
                   unavailability of large-scale and representative data
                   on social connectedness between individuals or
                   geographic regions has posed a challenge for
                   empirical research on social networks. More recently,
                   a body of such research has begun to emerge using
                   data on social connectedness from online social
                   networking services such as Facebook, LinkedIn, and
                   Twitter. To date, most of these research projects
                   have been built on anonymized administrative
                   microdata from Facebook, typically by working with
                   coauthor teams that include Facebook employees.
                   However, there is an inherent limit to the number of
                   researchers that will be able to work with social
                   network data through such collaborations. In this
                   paper, we therefore introduce a new measure of social
                   connectedness at the US county level. Our Social
                   Connectedness Index is based on friendship links on
                   Facebook, the global online social networking
                   service. Specifically, the Social Connectedness Index
                   corresponds to the relative frequency of Facebook
                   friendship links between every county-pair in the
                   United States, and between every US county and every
                   foreign country. Given Facebook's scale as well as
                   the relative representativeness of Facebook's user
                   body, these data provide the first comprehensive
                   measure of friendship networks at a national level.},
  doi =           {10.1257/jep.32.3.259},
  issn =          {0895-3309},
  language =      {en},
  url =           {https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259},
}

@article{makridis2019effect,
  author =        {Makridis, Christos},
  journal =       {Available at SSRN 3092489},
  title =         {The effect of economic sentiment on consumption:
                   Evidence from social networks},
  year =          {2019},
}

@article{makridis2020learning,
  author =        {Makridis, Christos and Wang, Tao},
  journal =       {Available at SSRN 3601500},
  title =         {Learning from friends in a pandemic: Social networks
                   and the macroeconomic response of consumption},
  year =          {2020},
}

@article{ratnadiwakara2021flooded,
  author =        {Ratnadiwakara, Dimuthu},
  journal =       {Quarterly Journal of Finance (QJF)},
  number =        {04},
  pages =         {1--15},
  publisher =     {World Scientific Publishing Co. Pte. Ltd.},
  title =         {Flooded Social Connections},
  volume =        {11},
  year =          {2021},
}

@article{hong2005thy,
  author =        {Hong, Harrison and Kubik, Jeffrey D and
                   Stein, Jeremy C},
  journal =       {The Journal of Finance},
  number =        {6},
  pages =         {2801--2824},
  publisher =     {Wiley Online Library},
  title =         {Thy neighbor's portfolio: Word-of-mouth effects in
                   the holdings and trades of money managers},
  volume =        {60},
  year =          {2005},
}

@article{hvide2015social,
  author =        {Hvide, Hans K and {\"O}stberg, Per},
  journal =       {Journal of Financial Economics},
  number =        {3},
  pages =         {628--652},
  publisher =     {Elsevier},
  title =         {Social interaction at work},
  volume =        {117},
  year =          {2015},
}

@article{cohen2008small,
  author =        {Cohen, Lauren and Frazzini, Andrea and
                   Malloy, Christopher},
  journal =       {Journal of Political Economy},
  number =        {5},
  pages =         {951--979},
  publisher =     {The University of Chicago Press},
  title =         {The small world of investing: Board connections and
                   mutual fund returns},
  volume =        {116},
  year =          {2008},
}

@article{hong2004social,
  author =        {Hong, Harrison and Kubik, Jeffrey D and
                   Stein, Jeremy C},
  journal =       {The journal of finance},
  number =        {1},
  pages =         {137--163},
  publisher =     {Wiley Online Library},
  title =         {Social interaction and stock-market participation},
  volume =        {59},
  year =          {2004},
  abstract =      {We propose that stock-market participation is
                   influenced by social interaction. In our model, any
                   given “social” investor finds the market more
                   attractive when more of his peers participate. We
                   test this theory using data from the Health and
                   Retirement Study, and find that social
                   households—those who interact with their neighbors,
                   or attend church—are substantially more likely to
                   invest in the market than non-social households,
                   controlling for wealth, race, education, and risk
                   tolerance. Moreover, con- sistent with a peer-effects
                   story, the impact of sociability is stronger in
                   states where stock-market participation rates are
                   higher.},
}

@article{brown2008neighbors,
  author =        {Brown, Jeffrey R and Ivkovi{\'c}, Zoran and
                   Smith, Paul A and Weisbenner, Scott},
  journal =       {The Journal of Finance},
  number =        {3},
  pages =         {1509--1531},
  publisher =     {Wiley Online Library},
  title =         {Neighbors matter: Causal community effects and stock
                   market participation},
  volume =        {63},
  year =          {2008},
}

@article{ivkovic2007information,
  author =        {Ivkovi{\'c}, Zoran and Weisbenner, Scott},
  journal =       {The Review of Financial Studies},
  number =        {4},
  pages =         {1327--1357},
  publisher =     {Oxford University Press},
  title =         {Information diffusion effects in individual
                   investors' common stock purchases: Covet thy
                   neighbors' investment choices},
  volume =        {20},
  year =          {2007},
}

@article{bayer2021speculative,
  author =        {Bayer, Patrick and Mangum, Kyle and Roberts, James W},
  journal =       {American Economic Review},
  number =        {2},
  pages =         {609--51},
  title =         {Speculative fever: Investor contagion in the housing
                   bubble},
  volume =        {111},
  year =          {2021},
}

@article{kaustia2012peer,
  author =        {Kaustia, Markku and Kn{\"u}pfer, Samuli},
  journal =       {Journal of Financial Economics},
  number =        {2},
  pages =         {321--338},
  publisher =     {Elsevier},
  title =         {Peer performance and stock market entry},
  volume =        {104},
  year =          {2012},
}

@incollection{TETLOCK2015701,
  author =        {Paul C. Tetlock},
  booktitle =     {Handbook of Media Economics},
  editor =        {Simon P. Anderson and Joel Waldfogel and
                   David Stromberg},
  pages =         {701-721},
  publisher =     {North-Holland},
  series =        {Handbook of Media Economics},
  title =         {Chapter 18 - The Role of Media in Finance},
  volume =        {1},
  year =          {2015},
  abstract =      {This chapter reviews and synthesizes a rapidly
                   growing subfield that analyzes the relation between
                   media and financial markets. Research in this area
                   identifies novel data sources, such as newspaper
                   articles, Internet search queries, and posts on
                   social networks, and employs inventive empirical
                   methods, notably textual analysis, to quantify the
                   rich information environment in modern financial
                   markets. Such data and methods enable powerful tests
                   of theories and have the potential to address
                   longstanding puzzles in finance, such as why trading
                   volume and stock price volatility are so high.},
  doi =           {10.1016/B978-0-444-63685-0.00018-8},
  issn =          {2213-6630},
  url =           {https://www.sciencedirect.com/science/article/pii/
                  B9780444636850000188},
}

@article{dougal2012journalists,
  author =        {Dougal, Casey and Engelberg, Joseph and Garcia, Diego and
                   Parsons, Christopher A},
  journal =       {The Review of Financial Studies},
  number =        {3},
  pages =         {639--679},
  publisher =     {Oxford University Press},
  title =         {Journalists and the stock market},
  volume =        {25},
  year =          {2012},
}

@techreport{soo_quantifying_2015,
  address =       {Rochester, NY},
  author =        {Soo, Cindy},
  institution =   {Social Science Research Network},
  month =         oct,
  number =        {ID 2330392},
  type =          {{SSRN} {Scholarly} {Paper}},
  title =         {Quantifying {Animal} {Spirits}: {News} {Media} and
                   {Sentiment} in the {Housing} {Market}},
  year =          {2015},
  abstract =      {This paper develops first measures of housing
                   sentiment for 34 cities across the U.S. by
                   quantifying the qualitative tone of local housing
                   news. I find that housing media sentiment has
                   significant predictive power for future house prices,
                   above and beyond his- torically predictive factors
                   and past returns. Sentiment leads price movements by
                   more than two years, and is highly correlated with
                   available survey expectations measures. The structure
                   of the media sentiment index itself reflects a
                   backward-looking nature consistent with extrapolative
                   expectations. Consistent with theories of sentiment,
                   the media sentiment index has a greater effect in
                   markets with more minority homebuyers, more
                   speculative investors, and across lower-priced homes.
                   Including additional controls for subprime lending
                   and easy credit has no impact on the magnitude of the
                   results, but the predictive effect of sentiment is
                   amplified in markets where more subprime loans were
                   issued. Directly investigating the content across
                   news articles finds that results are not driven by
                   news stories of unobserved fundamentals.},
  language =      {en},
  url =           {https://papers.ssrn.com/abstract=2330392},
}

@unpublished{choi2022popular,
  author =        {Choi, James J},
  note =          {Unpublished manuscript},
  title =         {Popular Personal Financial Advice vs. the Professors},
  year =          {2022},
}

@article{soroka2015s,
  author =        {Soroka, Stuart N and Stecula, Dominik A and
                   Wlezien, Christopher},
  journal =       {American Journal of Political Science},
  number =        {2},
  pages =         {457--474},
  publisher =     {Wiley Online Library},
  title =         {It's (change in) the (future) economy, stupid:
                   economic indicators, the media, and public opinion},
  volume =        {59},
  year =          {2015},
}

@article{damstra2021economy,
  author =        {Damstra, Alyt and Boukes, Mark},
  journal =       {Communication Research},
  number =        {1},
  pages =         {26--50},
  publisher =     {SAGE Publications Sage CA: Los Angeles, CA},
  title =         {The economy, the news, and the public: A longitudinal
                   study of the impact of economic news on economic
                   evaluations and expectations},
  volume =        {48},
  year =          {2021},
}

@techreport{bybee2020structure,
  author =        {Bybee, Leland and Kelly, Bryan T and Manela, Asaf and
                   Xiu, Dacheng},
  institution =   {National Bureau of Economic Research},
  title =         {The structure of economic news},
  year =          {2020},
}

@article{nimark2014man,
  author =        {Nimark, Kristoffer P},
  journal =       {American Economic Review},
  number =        {8},
  pages =         {2320--67},
  title =         {Man-bites-dog business cycles},
  volume =        {104},
  year =          {2014},
}

@article{chahrour2021sectoral,
  author =        {Chahrour, Ryan and Nimark, Kristoffer and
                   Pitschner, Stefan},
  journal =       {American Economic Review},
  number =        {12},
  pages =         {3872--3922},
  title =         {Sectoral media focus and aggregate fluctuations},
  volume =        {111},
  year =          {2021},
}

@article{ludvigson2004consumer,
  author =        {Ludvigson, Sydney C},
  journal =       {Journal of Economic perspectives},
  number =        {2},
  pages =         {29--50},
  title =         {Consumer confidence and consumer spending},
  volume =        {18},
  year =          {2004},
}

@article{cfwSentiment,
  author =        {Carroll, Christopher D. and Fuhrer, Jeffrey C. and
                   Wilcox, David W.},
  journal =       {American Economic Review},
  note =
  {\url{https://www.econ2.jhu.edu/people/ccarroll/SentAERCarrollFuhrerWilcox.pdf}},
  number =        {5},
  pages =         {1397-1408},
  publisher =     {JSTOR},
  title =         {Does Consumer Sentiment Forecast Household Spending?
                   {I}f So, Why?},
  volume =        {84},
  year =          {1994},
  url =           {https://www.econ2.jhu.edu/people/ccarroll/
                  SentAERCarrollFuhrerWilcox.pdf},
}

@techreport{doms2004consumer,
  author =        {Doms, Mark E and Morin, Norman J},
  institution =   {Federal Reserve Bank of San Francisco},
  number =        {2004-09},
  publisher =     {FRB of San Francisco Working Paper},
  title =         {Consumer sentiment, the economy, and the news media},
  year =          {2004},
}

@article{lamla2012role,
  author =        {Lamla, Michael J and Maag, Thomas},
  journal =       {Journal of Money, Credit and Banking},
  number =        {7},
  pages =         {1325--1350},
  publisher =     {Wiley Online Library},
  title =         {The role of media for inflation forecast disagreement
                   of households and professional forecasters},
  volume =        {44},
  year =          {2012},
}

@article{pfajfar2013news,
  author =        {Pfajfar, Damjan and Santoro, Emiliano},
  journal =       {Journal of Money, Credit and Banking},
  number =        {6},
  pages =         {1045--1067},
  publisher =     {Wiley Online Library},
  title =         {News on inflation and the epidemiology of inflation
                   expectations},
  volume =        {45},
  year =          {2013},
}

@article{choi2012predicting,
  author =        {Choi, Hyunyoung and Varian, Hal},
  journal =       {Economic record},
  pages =         {2--9},
  publisher =     {Wiley Online Library},
  title =         {Predicting the present with Google Trends},
  volume =        {88},
  year =          {2012},
}

@article{baker2016measuring,
  author =        {Baker, Scott R and Bloom, Nicholas and
                   Davis, Steven J},
  journal =       {The quarterly journal of economics},
  number =        {4},
  pages =         {1593--1636},
  publisher =     {Oxford University Press},
  title =         {Measuring economic policy uncertainty},
  volume =        {131},
  year =          {2016},
}

@article{allcott2017social,
  author =        {Allcott, Hunt and Gentzkow, Matthew},
  journal =       {Journal of economic perspectives},
  number =        {2},
  pages =         {211--36},
  title =         {Social media and fake news in the 2016 election},
  volume =        {31},
  year =          {2017},
  doi =           {10.3386/w23089},
}

@article{eliaz2020model,
  author =        {Eliaz, Kfir and Spiegler, Ran},
  journal =       {American Economic Review},
  number =        {12},
  pages =         {3786--3816},
  title =         {A model of competing narratives},
  volume =        {110},
  year =          {2020},
}

@article{shiller1995conversation,
  author =        {Shiller, Robert J},
  journal =       {The American economic review},
  number =        {2},
  pages =         {181--185},
  publisher =     {JSTOR},
  title =         {Conversation, information, and herd behavior},
  volume =        {85},
  year =          {1995},
}

@book{akerlof2010animal,
  author =        {Akerlof, George A and Shiller, Robert J},
  pages =         {1-10},
  publisher =     {Princeton university press},
  title =         {Animal spirits: How human psychology drives the
                   economy, and why it matters for global capitalism},
  volume =        {9},
  year =          {2010},
  doi =           {10.4337/roke.2021.01.01},
  issn =          {2049-5323},
}

@book{shiller_narrative_2019,
  author =        {Shiller, Robert J.},
  month =         oct,
  note =          {Google-Books-ID: HciXDwAAQBAJ},
  publisher =     {Princeton University Press},
  title =         {Narrative {Economics}: {How} {Stories} {Go} {Viral}
                   and {Drive} {Major} {Economic} {Events}},
  year =          {2019},
  abstract =      {From Nobel Prize–winning economist and New York
                   Times bestselling author Robert Shiller, a new way to
                   think about how popular stories help drive economic
                   eventsIn a world in which internet troll farms
                   attempt to influence foreign elections, can we afford
                   to ignore the power of viral stories to affect
                   economies? In this groundbreaking book, Nobel
                   Prize–winning economist and New York Times
                   bestselling author Robert Shiller offers a new way to
                   think about the economy and economic change. Using a
                   rich array of historical examples and data, Shiller
                   argues that studying popular stories that affect
                   individual and collective economic behavior—what he
                   calls "narrative economics"—has the potential to
                   vastly improve our ability to predict, prepare for,
                   and lessen the damage of financial crises,
                   recessions, depressions, and other major economic
                   events.Spread through the public in the form of
                   popular stories, ideas can go viral and move
                   markets—whether it's the belief that tech stocks
                   can only go up, that housing prices never fall, or
                   that some firms are too big to fail. Whether true or
                   false, stories like these—transmitted by word of
                   mouth, by the news media, and increasingly by social
                   media—drive the economy by driving our decisions
                   about how and where to invest, how much to spend and
                   save, and more. But despite the obvious importance of
                   such stories, most economists have paid little
                   attention to them. Narrative Economics sets out to
                   change that by laying the foundation for a way of
                   understanding how stories help propel economic events
                   that have had led to war, mass unemployment, and
                   increased inequality.The stories people tell—about
                   economic confidence or panic, housing booms, the
                   American dream, or Bitcoin—affect economic
                   outcomes. Narrative Economics explains how we can
                   begin to take these stories seriously. It may be
                   Robert Shiller's most important book to date.},
  isbn =          {978-0-691-18229-2},
  language =      {en},
}

@article{ash2021text,
  author =        {Ash, Elliott and Gauthier, Germain and
                   Widmer, Philine},
  journal =       {arXiv preprint arXiv:2108.01720},
  title =         {Text Semantics Capture Political and Economic
                   Narratives},
  year =          {2021},
  url =           {https://doi.org/10.48550/arXiv.2108.01720},
}

@article{andre2022narratives,
  author =        {Andre, Peter and Haaland, Ingar and Roth, Christopher and
                   Wohlfart, Johannes},
  journal =       {Working Paper},
  title =         {{Narratives about the Macroeconomy}},
  year =          {2022},
  doi =           {10.1093/restud/rdac008},
  issn =          {0034-6527},
}

@techreport{larsen2019business,
  author =        {Larsen, Vegard H and Thorsrud, Leif Anders},
  institution =   {CESifo Working Paper},
  title =         {Business Cycle Narratives},
  year =          {2019},
}

@article{whiten2021burgeoning,
  author =        {Whiten, Andrew},
  journal =       {Science},
  number =        {6537},
  pages =         {eabe6514},
  publisher =     {American Association for the Advancement of Science},
  title =         {The burgeoning reach of animal culture},
  volume =        {372},
  year =          {2021},
}

@article{whiten2016cultural,
  author =        {Whiten, Andrew and Caldwell, Christine A and
                   Mesoudi, Alex},
  journal =       {Current Opinion in Psychology},
  month =         apr,
  note =          {Culture},
  pages =         {15--21},
  publisher =     {Elsevier},
  series =        {Culture},
  title =         {Cultural diffusion in humans and other animals},
  volume =        {8},
  year =          {2016},
  abstract =      {Recent years have seen an enormous expansion and
                   progress in studies of the cultural diffusion
                   processes through which behaviour patterns, ideas and
                   artifacts are transmitted within and between
                   generations of humans and other animals. The first of
                   two main approaches focuses on identifying, tracing
                   and understanding cultural diffusion as it naturally
                   occurs, an essential foundation to any science of
                   culture. This endeavor has been enriched in recent
                   years by sophisticated statistical methods and
                   surprising new discoveries particularly in humans,
                   other primates and cetaceans. This work has been
                   complemented by a growing corpus of powerful,
                   purpose-designed cultural diffusion experiments with
                   captive and natural populations that have facilitated
                   the rigorous identification and analysis of cultural
                   diffusion in species from insects to humans.},
  doi =           {10.1016/j.copsyc.2015.09.002},
  issn =          {2352-250X},
  language =      {en},
  url =           {https://www.sciencedirect.com/science/article/pii/
                  S2352250X15002213},
}

@article{kendal2015chimpanzees,
  author =        {Kendal, Rachel and Hopper, Lydia M and Whiten, Andrew and
                   Brosnan, Sarah F and Lambeth, Susan P and
                   Schapiro, Steven J and Hoppitt, Will},
  journal =       {Evolution and Human Behavior},
  number =        {1},
  pages =         {65--72},
  publisher =     {Elsevier},
  title =         {Chimpanzees copy dominant and knowledgeable
                   individuals: implications for cultural diversity},
  volume =        {36},
  year =          {2015},
}

@article{kendal2018social,
  author =        {Kendal, Rachel L and Boogert, Neeltje J and
                   Rendell, Luke and Laland, Kevin N and Webster, Mike and
                   Jones, Patricia L},
  journal =       {Trends in cognitive sciences},
  number =        {7},
  pages =         {651--665},
  publisher =     {Elsevier},
  title =         {Social learning strategies: Bridge-building between
                   fields},
  volume =        {22},
  year =          {2018},
}

@article{carcea2019biological,
  author =        {Carcea, Ioana and Froemke, Robert C},
  journal =       {Current opinion in neurobiology},
  pages =         {178--185},
  publisher =     {Elsevier},
  title =         {Biological mechanisms for observational learning},
  volume =        {54},
  year =          {2019},
}

@article{diamond_bank_1983,
  author =        {Diamond, Douglas W. and Dybvig, Philip H.},
  journal =       {Journal of Political Economy},
  note =          {Publisher: University of Chicago Press},
  number =        {3},
  pages =         {401--419},
  title =         {Bank {Runs}, {Deposit} {Insurance}, and {Liquidity}},
  volume =        {91},
  year =          {1983},
  abstract =      {This paper shows that bank deposit contracts can
                   provide allocations superior to those of exchange
                   markets, offering an explanation of how banks subject
                   to runs can attract deposits. Investors face
                   privately observed risks which lead to a demand for
                   liquidity. Traditional demand deposit contracts which
                   provide liquidity have multiple equilibria, one of
                   which is a bank run. Bank runs in the model cause
                   real economic damage, rather than simply reflecting
                   other problems. Contracts which can prevent runs are
                   studied, and the analysis shows that there are
                   circumstances when government provision of deposit
                   insurance can produce superior contracts.},
  issn =          {0022-3808},
  url =           {https://www.jstor.org/stable/1837095},
}

@article{iyer2012understanding,
  author =        {Iyer, Rajkamal and Puri, Manju},
  journal =       {American Economic Review},
  number =        {4},
  pages =         {1414--45},
  title =         {Understanding bank runs: The importance of
                   depositor-bank relationships and networks},
  volume =        {102},
  year =          {2012},
}

@incollection{cabrales2015financial,
  author =        {Cabrales, Antonio and Gale, Douglas and
                   Gottardi, Piero},
  booktitle =     {The Oxford Handbook of the Economics of Networks},
  publisher =     {Oxford University Press},
  title =         {Financial contagion in networks},
  year =          {2015},
}

@article{daley1964epidemics,
  author =        {Daley, Daryl J and Kendall, David G},
  journal =       {Nature},
  number =        {4963},
  pages =         {1118--1118},
  publisher =     {Nature Publishing Group},
  title =         {Epidemics and rumours},
  volume =        {204},
  year =          {1964},
  doi =           {10.1038/2041118A0},
}

@inproceedings{jin2013epidemiological,
  author =        {Jin, Fang and Dougherty, Edward and Saraf, Parang and
                   Cao, Yang and Ramakrishnan, Naren},
  booktitle =     {Proceedings of the 7th workshop on social network
                   mining and analysis},
  pages =         {1--9},
  title =         {Epidemiological modeling of news and rumors on
                   twitter},
  year =          {2013},
}

@article{vosoughi2018spread,
  author =        {Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
  journal =       {Science},
  number =        {6380},
  pages =         {1146--1151},
  publisher =     {American Association for the Advancement of Science},
  title =         {The spread of true and false news online},
  volume =        {359},
  year =          {2018},
}

@article{berger2012makes,
  author =        {Berger, Jonah and Milkman, Katherine L},
  journal =       {Journal of marketing research},
  number =        {2},
  pages =         {192--205},
  publisher =     {SAGE Publications Sage CA: Los Angeles, CA},
  title =         {What makes online content viral?},
  volume =        {49},
  year =          {2012},
  doi =           {10.1509/JMR.10.0353},
}

@inproceedings{zannettou2018origins,
  author =        {Zannettou, Savvas and Caulfield, Tristan and
                   Blackburn, Jeremy and De Cristofaro, Emiliano and
                   Sirivianos, Michael and Stringhini, Gianluca and
                   Suarez-Tangil, Guillermo},
  booktitle =     {Proceedings of the Internet Measurement Conference
                   2018},
  pages =         {188--202},
  title =         {On the origins of memes by means of fringe web
                   communities},
  year =          {2018},
}

@article{kohlhasasymmetric,
  author =        {Kohlhas, Alexandre N and Walther, Ansgar},
  journal =       {American Economic Review},
  month =         {September},
  number =        {9},
  pages =         {2879--2925},
  title =         {Asymmetric Attention},
  volume =        {111},
  year =          {2021},
}

@article{acemoglu2010spread,
  author =        {Acemo{\u{g}}lu, Daron and Ozdaglar, Asuman and
                   ParandehGheibi, Ali},
  journal =       {Games and Economic Behavior},
  number =        {2},
  pages =         {194--227},
  publisher =     {Elsevier},
  title =         {Spread of (mis) information in social networks},
  volume =        {70},
  year =          {2010},
  doi =           {10.1016/j.geb.2010.01.005},
  issn =          {0899-8256},
}

@article{bettencourt2006power,
  author =        {Bettencourt, Lu{\'\i}s MA and
                   Cintr{\'o}n-Arias, Ariel and Kaiser, David I and
                   Castillo-Ch{\'a}vez, Carlos},
  journal =       {Physica A: Statistical Mechanics and its
                   Applications},
  pages =         {513--536},
  publisher =     {Elsevier},
  title =         {The power of a good idea: Quantitative modeling of
                   the spread of ideas from epidemiological models},
  volume =        {364},
  year =          {2006},
}

@incollection{dawkins1978selfish,
  author =        {Dawkins, Richard},
  booktitle =     {The selfish gene},
  pages =         {224--p},
  publisher =     {Oxford University Press},
  title =         {The selfish gene},
  year =          {1978},
}

@article{wang2011epidemiological,
  author =        {Wang, Lin and Wood, Brendan C},
  journal =       {Applied Mathematical Modelling},
  number =        {11},
  pages =         {5442--5447},
  publisher =     {Elsevier},
  title =         {An epidemiological approach to model the viral
                   propagation of memes},
  volume =        {35},
  year =          {2011},
}

@techreport{carroll2001epidemiology,
  author =        {Carroll, Christopher D},
  institution =   {National Bureau of Economic Research Cambridge,
                   Mass., USA},
  title =         {The epidemiology of macroeconomic expectations},
  year =          {2001},
}

@article{coibion2012can,
  author =        {Coibion, Olivier and Gorodnichenko, Yuriy},
  journal =       {Journal of Political Economy},
  number =        {1},
  pages =         {116--159},
  publisher =     {University of Chicago Press Chicago, IL},
  title =         {What can survey forecasts tell us about information
                   rigidities?},
  volume =        {120},
  year =          {2012},
}

@article{arrondel2020informative,
  author =        {Arrondel, Luc and Calvo Pardo, Hector F and
                   Giannitsarou, Chryssi and Haliassos, Michael},
  journal =       {Available at SSRN 3171564},
  title =         {Informative social interactions},
  year =          {2020},
}

@article{gentzkow2019text,
  author =        {Gentzkow, Matthew and Kelly, Bryan and Taddy, Matt},
  journal =       {Journal of Economic Literature},
  number =        {3},
  pages =         {535--74},
  title =         {Text as data},
  volume =        {57},
  year =          {2019},
}