Testing directional forecast value in the presence of serial correlation

Arbeitspapier

Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations. Yet, in the presence of serial correlation they are markedly oversized as confirmed in a simulation study. We summarize serial correlation robust test procedures and propose a bootstrap approach. By means of a Monte Carlo study we illustrate the relative merits of the latter. Two empirical applications demonstrate the relevance to account for serial correlation in economic time series when testing for the value of directional forecasts.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2008,073

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications ; Money and Interest Rates: Forecasting and Simulation: Models and Applications ; Trade: Forecasting and Simulation ; International Finance Forecasting and Simulation: Models and Applications ; Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation: Models and Applications ; Portfolio Choice; Investment Decisions ; Financial Forecasting and Simulation
• Subject: Directional forecasts ; directional accuracy ; forecast evaluation ; testing independence ; contingency tables ; bootstrap ; Prognoseverfahren ; Zeitreihenanalyse ; Korrelation ; Statistischer Test ; Qualitatives Verfahren ; Bootstrap-Verfahren ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Blaskowitz, Oliver J. ; Herwartz, Helmut
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/25316
• Last update: 10.03.2025, 11:41 AM CET