Karsten Reichold, Carsten Jentsch
arXiv 4 Apr 2022 · Econometrics · 3 citations (OpenAlex)
arXiv:2204.01373 · PDF · DOI · OpenAlex · Extracted main text
Traditional inference in cointegrating regressions requires tuning parameter choices to estimate a long-run variance parameter. Even in case these choices are "optimal", the tests are severely size distorted. We propose a novel self-normalization approach, which leads to a nuisance parameter free limiting distribution without estimating the long-run variance parameter directly. This makes our self-normalized test tuning parameter free and considerably less prone to size distortions at the cost of only small power losses. In combination with an asymptotically justified vector autoregressive sieve bootstrap to construct critical values, the self-normalization approach shows further improvement in small to medium samples when the level of error serial correlation or regressor endogeneity is large. We illustrate the usefulness of the bootstrap-assisted self-normalized test in empirical applications by analyzing the validity of the Fisher effect in Germany and the United States.
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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 | Vogelsang and Wagner (2014) Integrated Modified OLS Estimation and Fixed-$b$ Inference for Cointegrating Regressions | 1.000 | 16 | 5 | 100% |
| 2 | Kiefer et al (2000) Simple Robust Testing of Regression Hypotheses | 1.000 | 7 | 4 | 100% |
| 3 | Palm et al (2010) A Sieve Bootstrap Test for Cointegration in a Conditional Error Correction Model | 1.000 | 7 | 4 | 100% |
| 4 | Park (2002) An Invariance Principle for Sieve Bootstrap in Time Series | 1.000 | 6 | 5 | 100% |
| 5 | Shao (2015) Self-Normalization for Time Series: A Review of Recent Developments | 1.000 | 6 | 4 | 100% |
| 6 | Andrews (1991) Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation | 1.000 | 5 | 4 | 100% |
| 7 | Meyer and Kreiss (2015) On the Vector Autoregressive Sieve Bootstrap | 1.000 | 5 | 3 | 100% |
| 8 | Chang et al (2006) Bootstrapping Cointegrating Regressions | 0.928 | 4 | 3 | 100% |
| 9 | Johansen (1995) Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models | 0.874 | 6 | 2 | 100% |
| 10 | Westerlund (2008) Panel Cointegration Tests of the Fisher Effect | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2205.00577 | 0.405 | 1 | 1 |
| 2 | A Residuals-Based Nonparametric Variance Ratio Test for Cointegration | 0.405 | 1 | 1 |
| 3 | Limit Theory under Network Dependence and Nonstationarity | 0.405 | 1 | 1 |