Misspecified Expectations among Professional Forecasters

Abstract Analyzing professional forecasts, I find that a model of expectation formation in which respondents misperceive the law of motion of the data generating process, which in turn causes them to form an erroneous view of its underlying persistence, outperforms alternative models when fit to forecast errors and revisions. Misspecified expectations, which outperforms the alternatives in and out of sample and for a variety of macro‐economic variables, is successful in part because it allows forecast errors to be longer lived. I conclude that misspecified expectations can serve as a suitable way of modeling expectation formation among professional forecasters.