Forecast Evaluation of Small Nested Model Sets

Konferenzbeitrag

We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do not require reestimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum tstatistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White's (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate our procedures by comparing forecasts of different models for U.S. inflation.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Macroeconomic Modeling and Forecasting Performance ; No. D11-V3

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Model Evaluation, Validation, and Selection ; Price Level; Inflation; Deflation
• Subject: Out-of-sample ; prediction ; testing ; multiple model comparisons ; inflation forecasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hubrich, Kirstin ; West, Kenneth
• Event: Veröffentlichung
• (who): Verein für Socialpolitik
• (where): Frankfurt a. M.
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/37160
• Last update: 10.03.2025, 11:42 AM CET