Alexander Chudik, M. Hashem Pesaran, Ron P. Smith
arXiv 2 Jun 2025 · Econometrics
arXiv:2506.02135 · PDF · DOI · OpenAlex · Extracted main text
The literature on panel cointegration is extensive but does not cover data sets where the cross section dimension, $n$, is larger than the time series dimension $T$. This paper proposes a novel methodology that filters out the short run dynamics using sub-sample time averages as deviations from their full-sample counterpart, and estimates the number of long-run relations and their coefficients using eigenvalues and eigenvectors of the pooled covariance matrix of these sub-sample deviations. We refer to this procedure as pooled minimum eigenvalue (PME). We show that PME estimator is consistent and asymptotically normal as $n$ and $T \rightarrow \infty$ jointly, such that $T\approx n^{d}$, with $d>0$ for consistency and $d>1/2$ for asymptotic normality. Extensive Monte Carlo studies show that the number of long-run relations can be estimated with high precision, and the PME estimators have good size and power properties. The utility of our approach is illustrated by micro and macro applications using Compustat and Penn World Tables.
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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 | Pedroni, P (1996) https://web.williams.edu/Economics/pedroni/WP-96-20.pdfFully Modified OLS for Heterogeneous Cointegrated Panels and the Case of… | 1.000 | 8 | 3 | 100% |
| 2 | Chudik, A., M. H. Pesaran, and R. P. Smith (2023) https://doi.org/10.1016/j.ecosta.2023.11.001Pooled Bewley estimator of long-run relationships in dynamic heterogenous panels self | 0.928 | 10 | 3 | 80% |
| 3 | Breitung, J (2005) https://doi.org/10.1081/etc-200067895A parametric approach to the estimation of cointegration vectors in panel data | 0.928 | 4 | 3 | 100% |
| 4 | Chudik, A., M. H. Pesaran, and R. P. Smith (2023) https://doi.org/10.24149/gwp415Revisiting the Great Ratios Hypothesis self | 0.928 | 4 | 3 | 100% |
| 5 | Mark, N. C. and D. Sul (2003) https://doi.org/10.1111/j.1468-0084.2003.00066.xCointegration vector estimation by panel DOLS and long-run money demand | 0.928 | 4 | 3 | 100% |
| 6 | Pedroni, P (2001) https://doi.org/10.1016/S0731-9053(00)15004-2Fully modified OLS for heterogeneous cointegrated panels | 0.928 | 4 | 3 | 100% |
| 7 | Pedroni, P (2001) https://doi.org/10.1162/003465301753237803Purchasing Power Parity Tests in Cointegrated Panels | 0.928 | 4 | 3 | 100% |
| 8 | Pesaran, M. H., Y. Shin, and R. P. Smith (1999) https://doi.org/10.1080/01621459.1999.10474156Pooled mean group estimation of dynamic heterogeneous panels self | 0.928 | 4 | 3 | 100% |
| 9 | Geelen, T., J. Hajda, E. Morellec, and A. Winegar (2024) https://doi.org/10.1016/j.jfineco.2024.103796Asset life, leverage, and debt maturity matching | 0.737 | 4 | 2 | 75% |
| 10 | Im, K. S., M. Pesaran, and Y. Shin (2003) https://doi.org/10.1016/S0304-4076(03)00092-7Testing for unit roots in heterogeneous panels | 0.511 | 2 | 2 | 50% |
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