Tests of Equal Forecast Accuracy and Encompassing for Nested Models

By Todd E. Clark, Michael W. McCracken. 2001

1,138 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3e899e2bcd51fc085c80d6b034034b02510df2dc

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In Economics and Econometrics

Citations in the corpus

Predictive density and conditional confidence interval accuracy tests (2006, background): “West (1996), West and McCracken (1998), Clark and McCracken (2001 and 2003)). Notable exceptions are Giacomini and White (2003), who propose the use of a rolling scheme with a fixed window, not increasing with the sample size, so that estimated parameters are treated as mixing variables, and Pesaran”
Risk-Adjusted Forecasts of Oil Prices (2009, background): “Using specication (7), the residuals are serially uncorrelated under the null hypothesis of no predictability of excess returns, because all variables in equation (7) are in nancial marketsinformation set by the beginning of the next period.”
Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, background): “In contrast, Clark & McCracken (2001) present forecast accuracy and forecast encompassing tests for nested models.”
Short Interest and Aggregate Stock Returns (2016, methodology): “20The popular Diebold and Mariano (1995) and West (1996) statistic for comparing predictive accuracy has a nonstandard distribution when comparing forecasts from nested models (Clark and McCracken, 2001; McCracken, 2007).”
Forecasting the Price of Oil (2010, methodology): “Related results can be found in Calhoun (2010) who shows that standard tests of equal predictive accuracy for nested models such as Clark and McCracken (2001) or Clark and West (2007) will choose the larger model too often when the smaller model is more accurate in out-of-sample forecasts and also…”
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes (2007, background): “This evidence is in loose agreement with the results reported by Clark and McCracken (2001), where it was found that in-sample evidence of Granger causality was much stronger than out-of-sample evidence.”
The Continuing Puzzle of Short Horizon Exchange Rate Forecasting (2008, methodology): “On the basis of both theoretical and simulation evidence, West (1996), McCracken (1999), Clark and McCracken (2001, 2005) and Clark and West (2006) show that, when comparing nested models, the asymptotic DMW test statistic is undersized, which means that it may not detect statistical signi cance…”
Asymptotics for out of sample tests of Granger causality (2007, background): “Clark and McCracken (2001) establish results for tests of forecast encompassing when comparing nested models.”
Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns (2006, neutral): “Clark and McCracken (2001) and McCracken (1999).”
Tail Risk and Asset Prices (2014, neutral): “We conduct tests of outof-sample predictive power based on Clark and McCracken (2001), which is the benchmark out-of-sample predictive test in the forecasting literature.”