DOC: fix Nevo (2000) cites

docs/notation.rst

@@ -1,7 +1,7 @@
 Notation
 ========
 
-The notation in PyBLP is a customized amalgamation of the notation employed by :ref:`references:Berry, Levinsohn, and Pakes (1995)`, :ref:`references:Nevo (2000)`, :ref:`references:Morrow and Skerlos (2011)`, :ref:`references:Grigolon and Verboven (2014)`, and others.
+The notation in PyBLP is a customized amalgamation of the notation employed by :ref:`references:Berry, Levinsohn, and Pakes (1995)`, :ref:`references:Nevo (2000a)`, :ref:`references:Morrow and Skerlos (2011)`, :ref:`references:Grigolon and Verboven (2014)`, and others.
 
 
 Indices

docs/notebooks/api/build_matrix.ipynb

@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this example, we'll load the fake cereal data from :ref:`references:Nevo (2000)` and create a simple matrix involving a constant, prices, and shares."
+    "In this example, we'll load the fake cereal data from :ref:`references:Nevo (2000a)` and create a simple matrix involving a constant, prices, and shares."
    ]
   },
   {

docs/notebooks/api/data_to_dict.ipynb

@@ -40,7 +40,7 @@
    "source": [
     "In this example, we'll convert a dataset constructed by PyBLP into a dictionary that can more easily ingested by other Python packages. Note that you can also [pickle](https://docs.python.org/3/library/pickle.html#module-pickle) most PyBLP objects, which may be more convenient.\n",
     "\n",
-    "First we'll initialize a :class:`Problem` with the fake cereal data from :ref:`references:Nevo (2000)`."
+    "First we'll initialize a :class:`Problem` with the fake cereal data from :ref:`references:Nevo (2000a)`."
    ]
   },
   {

docs/notebooks/api/parallel.ipynb

@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this example, we'll use parallel processing to compute elasticities market-by-market for a simple Logit problem configured with some of the fake cereal data from :ref:`references:Nevo (2000)`."
+    "In this example, we'll use parallel processing to compute elasticities market-by-market for a simple Logit problem configured with some of the fake cereal data from :ref:`references:Nevo (2000a)`."
    ]
   },
   {

docs/notebooks/tutorial/logit_nested.ipynb

@@ -37,9 +37,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we'll use data from :ref:`references:Nevo (2000)` to solve the paper's fake cereal problem. Locations of CSV files that contain the data are in the :mod:`data` module.\n",
+    "In this tutorial, we'll use data from :ref:`references:Nevo (2000a)` to solve the paper's fake cereal problem. Locations of CSV files that contain the data are in the :mod:`data` module.\n",
     "\n",
-    "We will compare two simple models, the plain (IIA) logit model and the nested logit (GEV) model using the fake cereal dataset of :ref:`references:Nevo (2000)`.\n",
+    "We will compare two simple models, the plain (IIA) logit model and the nested logit (GEV) model using the fake cereal dataset of :ref:`references:Nevo (2000a)`.\n",
     "\n",
     "## Theory of Plain Logit\n",
     "\n",
@@ -74,7 +74,7 @@
     "\n",
     "A Logit :class:`Problem` can be created by simply excluding the formulation for the nonlinear parameters, $X_2$, along with any agent information. In other words, it requires only specifying the _linear component_ of demand.\n",
     "\n",
-    "We'll set up and solve a simple version of the fake data cereal problem from :ref:`references:Nevo (2000)`. Since we won't include any demand-side nonlinear characteristics or parameters, we don't have to worry about configuring an optimization routine.\n",
+    "We'll set up and solve a simple version of the fake data cereal problem from :ref:`references:Nevo (2000a)`. Since we won't include any demand-side nonlinear characteristics or parameters, we don't have to worry about configuring an optimization routine.\n",
     "\n",
     "There are some important reserved variable names:\n",
     "\n",

docs/notebooks/tutorial/nevo.ipynb

@@ -37,7 +37,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we'll use data from :ref:`references:Nevo (2000)` to solve the paper's fake cereal problem. Locations of CSV files that contain the data are in the :mod:`data` module.\n",
+    "In this tutorial, we'll use data from :ref:`references:Nevo (2000a)` to solve the paper's fake cereal problem. Locations of CSV files that contain the data are in the :mod:`data` module.\n",
     "\n",
     "## Theory of Random Coefficients Logit\n",
     "\n",
@@ -1005,7 +1005,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The initialized problem can be solved with :meth:`Problem.solve`. We'll use the same starting values as :ref:`references:Nevo (2000)`. By passing a diagonal matrix as starting values for $\\Sigma$, we're choosing to ignore covariance terms. Similarly, zeros in the starting values for $\\Pi$ mean that those parameters will be fixed at zero.\n",
+    "The initialized problem can be solved with :meth:`Problem.solve`. We'll use the same starting values as :ref:`references:Nevo (2000a)`. By passing a diagonal matrix as starting values for $\\Sigma$, we're choosing to ignore covariance terms. Similarly, zeros in the starting values for $\\Pi$ mean that those parameters will be fixed at zero.\n",
     "\n",
     "To replicate common estimates, we'll use the non-default BFGS optimization routine (with a slightly tighter tolerance to avoid getting stuck at a spurious local minimum), and we'll configure :meth:`Problem.solve` to use 1-step GMM instead of 2-step GMM."
    ]

docs/notebooks/tutorial/post_estimation.ipynb

@@ -51,7 +51,7 @@
     "\n",
     "## Problem Results\n",
     "\n",
-    "As in the [fake cereal tutorial](nevo.ipynb), we'll first solve the fake cereal problem from :ref:`references:Nevo (2000)`. We load the fake data and estimate the model as in the previous tutorial. We output the setup of the model to confirm we have correctly configured the :class:`Problem`"
+    "As in the [fake cereal tutorial](nevo.ipynb), we'll first solve the fake cereal problem from :ref:`references:Nevo (2000a)`. We load the fake data and estimate the model as in the previous tutorial. We output the setup of the model to confirm we have correctly configured the :class:`Problem`"
    ]
   },
   {
@@ -435,7 +435,7 @@
    "source": [
     "We can use :meth:`ProblemResults.compute_approximate_prices` or :meth:`ProblemResults.compute_prices` to estimate post-merger prices. The first method, which is in the spirit of early approaches to merger evaluation such as :ref:`references:Hausman, Leonard, and Zona (1994)` and :ref:`references:Werden (1997)`, is only a partial merger simulation in that it assumes shares and their price derivatives are unaffected by the merger.\n",
     "\n",
-    "The second method, which is used by :ref:`references:Nevo (2000)`, is a full merger simulation in that it does not make these assumptions, and is the preferred approach to merger simulation. By default, we iterate over the $\\zeta$-markup equation from :ref:`references:Morrow and Skerlos (2011)` to solve the full system of $J_t$ equations and $J_t$ unknowns in each market $t$. We'll use the latter, since it is fast enough for this example problem."
+    "The second method, which is used by :ref:`references:Nevo (2000b)`, is a full merger simulation in that it does not make these assumptions, and is the preferred approach to merger simulation. By default, we iterate over the $\\zeta$-markup equation from :ref:`references:Morrow and Skerlos (2011)` to solve the full system of $J_t$ equations and $J_t$ unknowns in each market $t$. We'll use the latter, since it is fast enough for this example problem."
    ]
   },
   {

docs/references.rst

@@ -163,16 +163,16 @@ Morrow and Skerlos (2011)
 Morrow, W. Ross, and Steven J. Skerlos (2011). `Fixed-point approaches to computing Bertrand-Nash equilibrium prices under mixed-logit demand <https://ideas.repec.org/a/inm/oropre/v59y2011i2p328-345.html>`_. *Operations Research, 59* (2), 328-345.
 
 
-Nevo (1997)
-~~~~~~~~~~~
+Nevo (2000a)
+~~~~~~~~~~~~
 
-Nevo, Aviv (1997). `Mergers with differentiated products: The case of the ready-to-eat cereal industry <https://ideas.repec.org/p/cdl/compol/qt1d53t6ts.html>`_. Competition Policy Center, Working Paper Series qt1d53t6ts, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
+Nevo, Aviv (2000). `A practitioner's guide to estimation of random‐coefficients logit models of demand <https://ideas.repec.org/a/bla/jemstr/v9y2000i4p513-548.html>`_. *Journal of Economics & Management Strategy, 9* (4), 513-548.
 
 
-Nevo (2000)
-~~~~~~~~~~~
+Nevo (2000b)
+~~~~~~~~~~~~
 
-Nevo, Aviv (2000). `A practitioner's guide to estimation of random‐coefficients logit models of demand <https://ideas.repec.org/a/bla/jemstr/v9y2000i4p513-548.html>`_. *Journal of Economics & Management Strategy, 9* (4), 513-548.
+Nevo, Aviv (2000). `Mergers with differentiated products: The case of the ready-to-eat cereal industry <https://ideas.repec.org/a/rje/randje/v31y2000iautumnp395-421.html>`_. *RAND Journal of Economics, 31* (3), 395-421.
 
 
 Newey and West (1987)

pyblp/data/__init__.py

@@ -3,8 +3,8 @@
 Attributes
 ----------
 NEVO_PRODUCTS_LOCATION : `str`
-    Location of a CSV file containing the fake cereal product data from :ref:`references:Nevo (2000)`. The file includes
-    the same pre-computed excluded instruments used in the original paper.
+    Location of a CSV file containing the fake cereal product data from :ref:`references:Nevo (2000a)`. The file
+    includes the same pre-computed excluded instruments used in the original paper.
 NEVO_AGENTS_LOCATION : `str`
     Location of a CSV file containing the fake cereal agent data. Included in the file are Monte Carlo weights and
     draws along with demographics from the original paper.