Expectations: Point-Estimates, Probability Distributions, Confidence, and Forecasts

Abstract

Abstract. In this article I test a new graphical, interactive interface that captures both "best estimate" point-estimates and probability distributions from nonexperts. As in the previous literature, respondents are overconfident. My innovation is to show that in contrast to the standard method of directly asking respondents to state their confidence, using my method, which induces the respondents to reveal confidence, there is a sizable and statically significant positive relationship between the respondents' confidence and the accuracy of their individual-level expectations. This positive correlation between confidence and accuracy can be utilized to create confidence-weighted aggregated forecasts that are more accurate than the standard "consensus forecasts". The payment of financial incentives does not affect these findings.

Overconfidence in Political Behavior

“…Finally, as in the Proof of Proposition 7, the first part follows from standard properties of Bayesian Updating. et al, 1999;Rothschild, 2011).…”

The Standard Portfolio Choice Problem in Germany

“…with a shifter of -10 should invest very little whereas those with -5 should invest intermediate amounts. 6 On the fourth screen we elicit respondents' beliefs about the return of the fund, using the histogram elicitation method pioneered by Delavande and Rohwedder (2008) and refined by Delavande et al (2011) andRothschild (2012). 7 A screenshot of the interface can be found in Appendix A. Respondents have to place 20 "bricks", each representing a probability mass of 5%, into seven bins of possible percentage-point returns.…”

The Standard Portfolio Choice Problem in Germany

“…Under the assumptions of rational expectations, EU-CRRA and usual degrees of risk aversion, one can generate the approximate prediction that in treatments with non-negative shifters, most respondents should invest their entire endowment (all respondents with degree of relative risk aversion below 3); those with a shifter of -10 should invest very little whereas those with -5 should invest intermediate amounts. 6 On the fourth screen we elicit respondents' beliefs about the return of the fund, using the histogram elicitation method pioneered by Delavande and Rohwedder (2008) and refined by Delavande et al 2011and Rothschild (2012). 7 A screenshot of the interface can be found in Appendix A. Respondents have to place 20 "bricks", each representing a probability mass of 5%, into seven bins of possible percentage-point returns.…”