Bounded Rationality and Limited Datasets: Testable Implications, Identifiability, and Out-of-Sample Prediction∗

Theories of bounded rationality are typically characterized over an exhaustive data set. This paper aims to operationalize some leading theories when the available data is limited, as is the case in most practical settings. How does one tell if observed choices are consistent with a theory of bounded rationality if the data is incomplete? What information can be identified about preferences? How can out-of-sample predictions be made? Our approach is contrasted with earlier attempts to examine bounded rationality theories on limited data, showing their notion of consistency is inappropriate for identifiability and out-of-sample prediction. ∗First version: January 2012. We thank Kfir Eliaz, Andrew Postlewaite, Ariel Rubinstein, Larry Samuelson, and the seminar audience at Brown University for helpful suggestions. †Department of Economics. Email address: [email protected] ‡Department of Economics and the Cowles Foundation for Research in Economics. Email address: [email protected]. Rozen thanks the NSF for financial support under grant SES-0919955.