rAtte {ramchoice}

Revealed Preference Analysis in Random Limited Attention Models

Description

This has been replaced by revealPref.

Usage

rAtte(
  menu,
  choice,
  pref_list = NULL,
  method = "GMS",
  nCritSimu = 2000,
  BARatio2MS = 0.1,
  BARatio2UB = 0.1,
  MNRatioGMS = NULL,
  RAM = TRUE,
  AOM = TRUE,
  limDataCorr = TRUE,
  attBinary = 1
)

Arguments

menu

Numeric matrix of 0s and 1s, the collection of choice problems.

choice

Numeric matrix of 0s and 1s, the collection of choices.

pref_list

Numeric matrix, each row corresponds to one preference. For example, c(2, 3, 1) means 2 is preferred to 3 and to 1. When set to NULL, the default, c(1, 2, 3, ...), will be used.

method

String, the method for constructing critical values. Default is GMS (generalized moment selection). Other available options are LF (least favorable model), PI (plug-in method), 2MS (two-step moment selection), 2UB (two-step moment upper bound), or ALL (report all critical values).

nCritSimu

Integer, number of simulations used to construct the critical value. Default is 2000.

BARatio2MS

Numeric, beta-to-alpha ratio for two-step moment selection method. Default is 0.1.

BARatio2UB

Numeric, beta-to-alpha ratio for two-step moment upper bound method. Default is 0.1.

MNRatioGMS

Numeric, shrinkage parameter. Default is sqrt(1/log(N)), where N is the sample size.

RAM

Boolean, whether the restrictions implied by the RAM of Cattaneo et al. (2020) should be incorporated, that is, their monotonic attention assumption (default is TRUE).

AOM

Boolean, whether the restrictions implied by the AOM of Cattaneo et al. (2022) should be incorporated, that is, their attention overload assumption (default is TRUE).

limDataCorr

Boolean, whether assuming limited data (default is TRUE). When set to FALSE, will assume all choice problems are observed. This option only applies when RAM is set to TRUE.

attBinary

Numeric, between 1/2 and 1 (default is 1), whether additional restrictions (on the attention rule) should be imposed for binary choice problems (i.e., attentive at binaries).

Value

sumStats

Summary statistics, generated by sumData.

constraints

Matrices of constraints, generated by genMat.

Tstat

Test statistic.

critVal

Critical values.

pVal

P-values (only available for GMS, LF and PI).

method

Method for constructing critical value.

Author(s)

Matias D. Cattaneo, Princeton University. [e-mail].

Paul Cheung, University of Maryland. [e-mail]

Xinwei Ma (maintainer), University of California San Diego. [e-mail]

Yusufcan Masatlioglu, University of Maryland. [e-mail]

Elchin Suleymanov, Purdue University. [e-mail]

References

M. D. Cattaneo, X. Ma, Y. Masatlioglu, and E. Suleymanov (2020). A Random Attention Model. Journal of Political Economy 128(7): 2796-2836. doi:10.1086/706861

M. D. Cattaneo, P. Cheung, X. Ma, and Y. Masatlioglu (2022). Attention Overload. Working paper.