revealAtte: Revealed Attention Analysis in Random Limited Attention...

In ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models

Revealed Attention Analysis in Random Limited Attention Models

Description

Given a random sample of choice problems and choices, revealAtte returns the upper and lower bounds on the attention frequency following the construction of Cattaneo, Cheung, Ma, and Masatlioglu (2022).

sumData is a low-level function that generates summary statistics. For revealed preference analysis, see revealPref.

Usage

revealAtte(
  menu,
  choice,
  alternative = NULL,
  S = NULL,
  lower = TRUE,
  upper = TRUE,
  pref = NULL,
  nCritSimu = 2000,
  level = 0.95
)

Arguments

menu	Numeric matrix of 0s and 1s, the collection of choice problems.
choice	Numeric matrix of 0s and 1s, the collection of choices.
alternative	Numeric vector, the alternatives for which to compute bounds on the attention frequency. For example, c(1, 2, 4) means the first, second, and fourth alternatives.
S	Numeric matrix of 0s and 1s, the collection of choice problems to compute bounds on the attention frequency.
lower	Boolean, whether lower bounds should be computed (default is TRUE).
upper	Boolean, whether upper bounds should be computed (default is TRUE).
pref	Numeric vector, corresponding to the 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. This option only applies to the upper bounds (i.e., when upper is set to TRUE).
nCritSimu	Integer, number of simulations used to construct the critical value. Default is 2000.
level	Numeric, the significance level (default is 0.95).

Value

sumStats	Summary statistics, generated by sumData.
lowerBound	Matrix containing the lower bounds.
upperBound	Matrix containing the upper bounds.
critVal	The simulated critical value.
opt	Options used in the function call.

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.

Examples

# Load data
data(ramdata)

# Set seed, to replicate simulated critical values
set.seed(42)

# preference
pref <- matrix(c(1, 2, 3, 4, 5), ncol=5, byrow=TRUE)
# list of choice problems
S <- matrix(c(1, 1, 0, 0, 0,
              1, 1, 1, 0, 0,
              1, 1, 1, 0, 1,
              1, 1, 1, 1, 1), ncol=5, byrow=TRUE)
result <- revealAtte(menu = ramdata$menu, choice = ramdata$choice,
  alternative = c(1,2), S = S,
  lower = TRUE, upper = TRUE,
  pref = pref)
summary(result)

ramchoice documentation built on May 24, 2022, 1:06 a.m.