Forecast Comparison in L2
AbstractThis paper provides a comprehensive framework for comparing predictors of univariate time series in the mean square norm. Initially, the forecast errors are assumed to be unbiased, independent, and normally distributed. Each of these is progressively relaxed. A new heteroscedasticity and autocorrelation consistent statistic for forecast comparison is derived. Finite sample distributions are tabulated in a sequence of Monte Carlo exercises. Power is examined by comparing forecast errors from a moving average model with misspecified autoregressive alternatives.
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Bibliographic InfoPaper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 199524.
Date of creation: 01 Aug 1996
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Mean squared prediction error; robust forecast comparison;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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