ramTest: Sample Inference for the Random Attention Model

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

Sample Inference for the Random Attention Model

Description

'ramTest' provides tidy candidate-ranking inference for the Random Attention Model of Cattaneo, Ma, Masatlioglu, and Suleymanov (2020). Row-i.i.d. calculations use [revealPref()]. When 'cluster' is supplied, the function retains the same RAM inequalities but estimates their joint covariance from cluster influence vectors and uses multiplier critical values.

Usage

ramTest(
  menu,
  choice,
  pref_list = NULL,
  method = "GMS",
  alpha = 0.05,
  nCritSimu = 2000,
  BARatio2MS = 0.1,
  BARatio2UB = 0.1,
  MNRatioGMS = NULL,
  attBinary = 1,
  limDataCorr = TRUE,
  cluster = NULL
)

Arguments

menu	Numeric matrix of zeros and ones containing observed menus.
choice	Numeric matrix of zeros and ones containing observed choices.
pref_list	Numeric matrix whose rows are candidate strict preference orderings. The default is '1, 2, ...'.
method	Critical-value method: '"GMS"', '"PI"', '"LF"', '"2MS"', '"2UB"', or '"ALL"'.
alpha	One or more nominal test levels chosen from '0.10', '0.05', and '0.01'.
nCritSimu	Number of Gaussian or cluster-multiplier simulations used for critical values.
BARatio2MS	Beta-to-alpha ratio for two-step moment selection.
BARatio2UB	Beta-to-alpha ratio for the two-step upper-bound method.
MNRatioGMS	Generalized moment-selection tuning parameter. 'NULL' uses '1/log(N)', where 'N' is the total sample size under row-i.i.d. sampling and the number of clusters under clustered sampling.
attBinary	Numeric value between one half and one. Values below one impose the attentive-at-binaries restriction used by the legacy API.
limDataCorr	Logical indicating whether to use the limited-menu-domain correction from the legacy RAM implementation.
cluster	Optional vector identifying independent sampling clusters. When supplied, covariance estimation and Gaussian critical values use cluster-level influence vectors and multiplier draws.

Value

An object of class 'ramchoiceRAMTest' with the same tidy components as [aomTest()] and a complete legacy [revealPref()] result.

References

M. D. Cattaneo, X. Ma, Y. Masatlioglu, and E. Suleymanov (2020). A Random Attention Model. Journal of Political Economy 128(7): 2796–2836. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1086/706861")}

ramchoice documentation built on Sept. 4, 2026, 9:07 a.m.