Assessing the Accuracy of Event Forecasts
AbstractEvent 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. 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 heteroskedasticity- autocorrelation (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 bootstrap.
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Bibliographic InfoArticle provided by College of Business, Feng Chia University, Taiwan in its journal Journal of Economics and Management.
Volume (Year): 5 (2009)
Issue (Month): 2 (July)
naive block bootstrap; HAC robust test; market timing test;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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