Oliver Wichert, I. Gaia Becheri, Feike C. Drost, Ramon van den Akker
arXiv 27 May 2019 · Econometrics
arXiv:1905.11184 · PDF · DOI · OpenAlex · Extracted main text
This paper considers unit-root tests in large n and large T heterogeneous panels with cross-sectional dependence generated by unobserved factors. We reconsider the two prevalent approaches in the literature, that of Moon and Perron (2004) and the PANIC setup proposed in Bai and Ng (2004). While these have been considered as completely different setups, we show that, in case of Gaussian innovations, the frameworks are asymptotically equivalent in the sense that both experiments are locally asymptotically normal (LAN) with the same central sequence. Using Le Cam's theory of statistical experiments we determine the local asymptotic power envelope and derive an optimal test jointly in both setups. We show that the popular Moon and Perron (2004) and Bai and Ng (2010) tests only attain the power envelope in case there is no heterogeneity in the long-run variance of the idiosyncratic components. The new test is asymptotically uniformly most powerful irrespective of possible heterogeneity. Moreover, it turns out that for any test, satisfying a mild regularity condition, the size and local asymptotic power are the same under both data generating processes. Thus, applied researchers do not need to decide on one of the two frameworks to conduct unit root tests. Monte-Carlo simulations corroborate our asymptotic results and document significant gains in finite-sample power if the variances of the idiosyncratic shocks differ substantially among the cross sectional units.
appendix boundary found by appendix_command · 34% 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 | Bai, J. and S. Ng (2004) A PANIC Attack on Unit Roots and Cointegration | 1.000 | 9 | 3 | 100% |
| 2 | Westerlund, J (2015) The Power of PANIC | 1.000 | 5 | 4 | 100% |
| 3 | Becheri, I. G., F. C. Drost, and R. Van den Akker (2014) Asymptotically UMP Panel Unit Root Teststhe Effect of Heterogeneity in the Alternatives self | 1.000 | 5 | 3 | 100% |
| 4 | Bai, J. and S. Ng (2010) Panel Unit Root Tests with Cross-Section Dependence: A Further Investigation | 0.981 | 18 | 6 | 94% |
| 5 | Moon, H. R. and B. Perron (2004) Testing for a Unit Root in Panels with Dynamic Factors | 0.933 | 32 | 8 | 81% |
| 6 | Moon, H. R., B. Perron, and P. C. B. Phillips (2007) Incidental trends and the power of panel unit root tests | 0.928 | 4 | 3 | 100% |
| 7 | Van der Vaart, A. W (2000) Asymptotic Statistics | 0.928 | 4 | 3 | 100% |
| 8 | Moon, H. R., B. Perron, and P. C. B. Phillips (2014) Point-Optimal Panel Unit Root Tests with Serially Correlated Errors | 0.843 | 3 | 3 | 100% |
| 9 | Newey, W. K. and K. D. West (1994) Automatic Lag Selection in Covariance Matrix Estimation | 0.763 | 9 | 2 | 67% |
| 10 | Juodis, A. and J. Westerlund (2018) Optimal panel unit root testing with covariates | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 36 scored citations.