Premium
A Simple Explanation of the Forecast Combination Puzzle *
Author(s) -
Smith Jeremy,
Wallis Kenneth F.
Publication year - 2009
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2008.00541.x
Subject(s) - forecast error , monte carlo method , econometrics , simple (philosophy) , variance (accounting) , watson , sample (material) , stock (firearms) , mathematics , statistics , economics , computer science , artificial intelligence , accounting , chromatography , engineering , philosophy , epistemology , mechanical engineering , chemistry
This article presents a formal explanation of the forecast combination puzzle, that simple combinations of point forecasts are repeatedly found to outperform sophisticated weighted combinations in empirical applications. The explanation lies in the effect of finite‐sample error in estimating the combining weights. A small Monte Carlo study and a reappraisal of an empirical study by Stock and Watson [ Federal Reserve Bank of Richmond Economic Quarterly (2003) Vol. 89/3, pp. 71–90] support this explanation. The Monte Carlo evidence, together with a large‐sample approximation to the variance of the combining weight, also supports the popular recommendation to ignore forecast error covariances in estimating the weight.