Assessing the Accuracy of Event Forecasts

Abstract

Event forecasts, often generated from estimated econometric models, comprise a binary time series. In empirical finance, the market timing test proposed by Henricksson and Merton(1981) is probably the most popular method to assess the accuracy of these forecasts._x000D_ Unfortunately, event forecasts and/or realizations are serially correlated, violating the independent identical distributed (IID) assumption. Consequently, the market timing test has an inflated size that can lead to doubtful empirical results. We find that the heteroskedasticityautocorrelation(HAC) robust t-test with fixed-b asymptotics in Kiefer and Vogelsang (2005) and with the empirical distribution obtained using the naive block bootstrap can overcome this problem. As compared to several extant testing methods, simulation results reveal that the empirical size of these two testing procedures is quite close to the nominal size in finite samples. An empirical study is performed to demonstrate the usefulness of the naive block_x000D_ bootstrap.