ramchoice (version 2.2)

revealPrefModel: Model Falsification with Random Limited Attention

Description

Given a collection of choice problems and corresponding choice probabilities, revealPrefModel determines if they are compatible with the Random Attention Model (RAM) of Cattaneo, Ma, Masatlioglu, and Suleymanov (2020) and/or the Attention Overload Model (AOM) of Cattaneo, Cheung, Ma, and Masatlioglu (2024).

See revealPref for revealed preference analysis with empirical choice data.

Usage

revealPrefModel(
  menu,
  prob,
  pref_list = NULL,
  RAM = TRUE,
  AOM = TRUE,
  limDataCorr = TRUE,
  attBinary = 1
)

Value

constraints

Matrices of constraints, generated by genMat. R: a matrix containing all constraints. ConstN: number of constraints for each preference.

inequalities

The moment inequalities. Positive numbers indicate that the RAM/AOM restrictions are rejected by the given choice probabilities. R: a vector containing all moment inequalities. ConstN: number of constraints for each preference.

Arguments

menu

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

prob

Numeric matrix, the collection of choice probabilities

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.

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. (2024) 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).

Author

Matias D. Cattaneo, Princeton University. .

Paul Cheung, University of Maryland.

Xinwei Ma (maintainer), University of California San Diego.

Yusufcan Masatlioglu, University of Maryland.

Elchin Suleymanov, Purdue University.

References

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

M. D. Cattaneo, P. Cheung, X. Ma, and Y. Masatlioglu (2024). Attention Overload. Working paper.

Examples

# Logit attention with parameter 2
# True preference: 1 2 3 4 5 6
menu <- prob <- matrix(c(1, 1, 1, 1, 1, 1,
                         0, 1, 1, 1, 1, 1,
                         1, 0, 1, 1, 1, 1,
                         1, 1, 0, 1, 1, 1,
                         1, 1, 1, 0, 1, 1,
                         1, 1, 1, 1, 0, 1,
                         1, 1, 1, 1, 1, 0), ncol=6, byrow=TRUE)
for (i in 1:nrow(prob)) prob[i, menu[i, ]==1] <- logitAtte(sum(menu[i, ]), 2)$choiceProb

# List of preferences to be tested
pref_list <- matrix(c(1, 2, 3, 4, 5, 6,
                      2, 3, 4, 5, 6, 1), ncol=6, byrow=TRUE)
# RAM only
result1 <- revealPrefModel(menu = menu, prob = prob, pref_list = pref_list, RAM = TRUE, AOM = FALSE)
summary(result1)

# AOM only
result2 <- revealPrefModel(menu = menu, prob = prob, pref_list = pref_list, RAM = FALSE, AOM = TRUE)
summary(result2)

# Both RAM and AOM
result3 <- revealPrefModel(menu = menu, prob = prob, pref_list = pref_list, RAM = TRUE, AOM = TRUE)
summary(result3)