arXiv 29 Mar 2018 · Econometrics · 9 citations (OpenAlex)
arXiv:1803.10883 · PDF · DOI · OpenAlex · Extracted main text
We develop a novel continuous-time asymptotic framework for inference on whether the predictive ability of a given forecast model remains stable over time. We formally define forecast instability from the economic forecaster's perspective and highlight that the time duration of the instability bears no relationship with stable period. Our approach is applicable in forecasting environment involving low-frequency as well as high-frequency macroeconomic and financial variables. As the sampling interval between observations shrinks to zero the sequence of forecast losses is approximated by a continuous-time stochastic process (i.e., an Ito semimartingale) possessing certain pathwise properties. We build an hypotheses testing problem based on the local properties of the continuous-time limit counterpart of the sequence of losses. The null distribution follows an extreme value distribution. While controlling the statistical size well, our class of test statistics feature uniform power over the location of the forecast failure in the sample. The test statistics are designed to have power against general form of insatiability and are robust to common forms of non-stationarity such as heteroskedasticty and serial correlation. The gains in power are substantial relative to extant methods, especially when the instability is short-lasting and when occurs toward the tail of the sample.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andrews (1993) Tests for Parameter Instability and Structural Change with Unknown Change-Point | 1.000 | 5 | 3 | 100% |
| 2 | Li, Todorov, and Tauchen (2017) Adaptive Estimation of Continuous-Time Regression Models Using High-Frequency Data | 0.928 | 4 | 4 | 100% |
| 3 | Casini and Perron (2017) Continuous Record Asymptotics for Structural Change Models | 0.899 | 11 | 3 | 73% |
| 4 | Barndorff-Nielsen and Shephard (2004) Econometric Analysis of Realised Covariation: High Frequency Based Covariance, Regression and Correlation in Financial Economics | 0.843 | 3 | 3 | 100% |
| 5 | Casini and Perron (2017) Structural Changes in Time Series | 0.843 | 3 | 3 | 100% |
| 6 | Li and Xiu (2016) Generalized Method of Integrated Moments for High-Frequency Data | 0.843 | 3 | 3 | 100% |
| 7 | Perron and Yamamoto (2018) Testing for Changes in Forecast Performance | 0.843 | 3 | 3 | 100% |
| 8 | Bibinger, Jirak, and Vetter (2017) Nonparametric Change-Point Analysis of Volatility | 0.737 | 4 | 3 | 50% |
| 9 | Gilchrist and Zakrajsek (2012) Credit Spreads and Business Cycle Fluctuations | 0.737 | 3 | 2 | 100% |
| 10 | Wu and Zhao (2007) Inference of Trends in Time Series | 0.709 | 14 | 4 | 36% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.