A0424
Title: Analysis of multiple long run relations in panel data models with applications to financial ratios
Authors: Alexander Chudik - Federal Reserve Bank of Dallas (United States) [presenting]
Hashem Pesaran - Trinity College, Cambridge (United Kingdom)
Ron Smith - Birkbeck University of London (United Kingdom)
Abstract: A new methodology is provided for the analysis of multiple long-run relations in panel data models where the cross-section dimension, n, is large relative to the time series dimension, T. For panel data models with large n, researchers have focused on panels with a single long-run relationship. The main difficulty has been to eliminate short-run dynamics without generating significant uncertainty for the identification of the long run. This problem is overcome by using non-overlapping sub-sample time averages as deviations from their full-sample counterpart and estimating the number of long-run relations and their coefficients using eigenvalues and eigenvectors of the pooled covariance matrix of these sub-sample deviations. This procedure is referred to as pooled minimum eigenvalue (PME), and it is shown that it applies to unbalanced panels generated from general linear processes with interactive stationary time effects and does not require knowing long-run causal linkages. To best of knowledge, no other estimation procedure exists for this setting. The PME estimator is shown to be 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 estimates of the long-run coefficients show small bias and RMSE and have good size and power properties.
