Out-of-Sample Forecast Tests Robust to the Choice of Window Size
By Barbara Rossi, A. Inoue. 2011
380 citations recorded by the source corpus.
This article proposes new methodologies for evaluating economic models’ out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. The study shows that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models’ forecasting ability.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/5f7f1556fbc8879063679fc03e798c9b46b29d84
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Citations in the corpus
- Manager sentiment and stock returns (2019, methodology): “Following Kandel and Stambaugh (1996) and Campbell and Thompson (2008), among others, using the out-of-sample predictive regression forecasts, we compute the certainty”
- Forecasting the realized range-based volatility using dynamic model averaging approach (2017, background): “3 More details and discussions about the optimization of estimation window can be seen in Rossi and Inoue (2012).”
- Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries (2013, neutral): “We do not attempt to optimize the performance of any one model by modifying these settings, in recognition of the concern over data mining discussed in Rossi and Inoue (2012), although we will discuss the robustness of our conclusions in Section 6.”
- Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics ∗ (2012, methodology): “1 Introduction Out-of-sample tests of predictive accuracy are used extensively throughout economics and finance and are regarded by many researchers as the “ultimate test of a forecasting model” (Stock and Watson (2007, p. 571)). Such tests are frequently undertaken using the approach of West (1996)”
- Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests (2015, neutral): “…Comparing Predictive Accuracy, Twenty Years Later 7 methods actually expand the scope for data mining in finite samples, as emphasized by Rossi and Inoue (2012) and Hansen and Timmermann (201 1), because one can then also mine over ť. Achieving robustness to data mining in finite samples…”