Merge pull request #39 from danielwilhelm/dev

Added references to arxiv preprint of rank rank paper

DESCRIPTION

@@ -10,7 +10,8 @@ Description: Account for uncertainty when working with ranks.
     Estimate standard errors consistently in linear regression with ranked variables.
     Construct confidence sets of various kinds for positions of populations in a ranking 
     based on values of a certain feature and their estimation errors. 
-    Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006>.
+    Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and
+    Chetverikov and Wilhelm (2023) <arXiv:2310.15512>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

R/lmranks.R

@@ -109,7 +109,8 @@
 #' a \code{rank_terms_indices} - an integer vector with indices of entries of \code{terms.labels} attribute
 #' of \code{terms(formula)}, which correspond to ranked regressors.
 #' 
-#' @references Chetverikov and Wilhelm (2023), "Inference for Rank-Rank Regressions", Working Paper
+#' @references Chetverikov and Wilhelm (2023), "Inference for Rank-Rank Regressions". 
+#' \href{https://arxiv.org/pdf/2310.15512.pdf}{arXiv preprint arXiv:2310.15512}
 #' 
 #' @seealso 
 #' \code{\link{lm}} for details about other arguments; \code{\link{frank}}.

README.Rmd

@@ -26,7 +26,7 @@ popular in applied work in economics.
 The functions are based on recent work developing these procedures and their theoretical properties. The confidence sets for
 ranks are based on [Mogstad, Romano, Shaikh, and Wilhelm (2023)](https://doi.org/10.1093/restud/rdad006) and 
 [Bazylik, Mogstad, Romano, Shaikh, and Wilhelm (2022)](https://dwilhelm.userweb.mwn.de/papers/cwp4021.pdf). The inference methods
-for regressions involving ranks are developed in Chetverikov and Wilhelm (2023).
+for regressions involving ranks are developed in [Chetverikov and Wilhelm (2023)](https://arxiv.org/pdf/2310.15512).
 
 
 ## Installation

README.md

@@ -21,7 +21,7 @@ their theoretical properties. The confidence sets for ranks are based on
 Romano, Shaikh, and Wilhelm
 (2022)](https://dwilhelm.userweb.mwn.de/papers/cwp4021.pdf). The
 inference methods for regressions involving ranks are developed in
-Chetverikov and Wilhelm (2023).
+[Chetverikov and Wilhelm (2023)](https://arxiv.org/pdf/2310.15512).
 
 ## Installation
 

inst/CITATION

@@ -19,4 +19,13 @@ bibentry(
   issn = "0034-6527",
   doi = "10.1093/restud/rdad006",
   url = "http://dwilhelm.userweb.mwn.de/papers/cwp0323.pdf"
+)
+
+bibentry(
+  bibtype = "article",
+  key = "chetverikov2023inference",
+  title = "Inference for Rank-Rank Regressions",
+  author = c("Denis Chetverikov", "Daniel Wilhelm"),
+  journal = "arXiv preprint arXiv:2310.15512",
+  year = 2023
 )

vignettes/Rank-Rank-Reg.rmd

@@ -172,6 +172,6 @@ The coefficient calculated for the whole sample has a narrow confidence interval
 expected. In this example, there are some differences in the correct (`csranks`) confidence intervals and the incorrect (`naive`) confidence intervals, but they are rather small. The paper by Chetverikov and Wilhelm (2023), however, provides empirical examples in which the differences can be quite large.
 
 ## Reference & further reading
-Chetverikov and Wilhelm (2023), "Inference for Rank-Rank Regressions"
+[Chetverikov and Wilhelm (2023), "Inference for Rank-Rank Regressions". arXiv preprint arXiv:2310.15512](https://arxiv.org/pdf/2310.15512.pdf)
 
 Check out the documentation of individual functions at the package's [website](https://danielwilhelm.github.io/R-CS-ranks/) and further examples in the package's [Github repository](https://github.com/danielwilhelm/R-CS-ranks/tree/master/examples).