arXiv 10 Sep 2023 · Econometrics · publishedEconometric Reviews (2025)
arXiv:2309.04926 · PDF · DOI · OpenAlex · Extracted main text
This study considers tests for coefficient randomness in predictive regressions. Our focus is on how tests for coefficient randomness are influenced by the persistence of random coefficient. We show that when the random coefficient is stationary, or I(0), Nyblom's (1989) LM test loses its optimality (in terms of power), which is established against the alternative of integrated, or I(1), random coefficient. We demonstrate this by constructing a test that is more powerful than the LM test when the random coefficient is stationary, although the test is dominated in terms of power by the LM test when the random coefficient is integrated. The power comparison is made under the sequence of local alternatives that approaches the null hypothesis at different rates depending on the persistence of the random coefficient and which test is considered. We revisit an earlier empirical research and apply the tests considered in this study to the U.S. stock returns data. The result mostly reverses the earlier finding.
appendix boundary found by appendix_titled_section at “Appendix to ``Testing for Stationary or Persistent Coefficient Randomness in Predictive Regressions" by M.Nishi” · 66% of the source is main text. Read the extracted text to check this.
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 | Nyblom (1989) Testing for the Constancy of Parameters Over Time | 1.000 | 11 | 4 | 100% |
| 2 | Georgiev, Harvey, Leybourne and Taylor (2018) Testing for Parameter Instability in Predictive Regression Models | 0.969 | 11 | 5 | 91% |
| 3 | Nishi (2023) Testing for Coefficient Randomness in Local-to-Unity Autoregressions self | 0.928 | 5 | 3 | 80% |
| 4 | Lin and Teräsvirta (1999) Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters | 0.894 | 7 | 3 | 71% |
| 5 | Devpura, Narayan and Sharma (2018) Is Stock Return Predictability Time-Varying? | 0.874 | 10 | 2 | 100% |
| 6 | Andrews and Guggenberger (2009) Hybrid and Size-Corrected Subsampling Methods | 0.874 | 6 | 2 | 100% |
| 7 | Hansen (1992) Testing for Parameter Instability in Linear Models | 0.843 | 3 | 3 | 100% |
| 8 | Fu, Hong, Su and Wang (2023) Specification Tests for Time-Varying Coefficient Models | 0.644 | 2 | 2 | 100% |
| 9 | Liang, Phillips, Wang and Wang (2016) Weak Convergence to Stochastic Integrals for Econometric Applications | 0.511 | 5 | 2 | 20% |
| 10 | Chen and Hong (2012) Testing for Smooth Structural Changes in Time Series Models via Nonparametric Regression | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 30 scored citations.