Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/322489 
Year of Publication: 
2025
Series/Report no.: 
CESifo Working Paper No. 11927
Publisher: 
CESifo GmbH, Munich
Abstract: 
This paper provides a new methodology 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 focussed on panels with a single long run relationship. The main difficulty has been to eliminate short run dynamics without generating signifcant uncertainty for identifcation of the long run. We overcome this problem 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 subsample deviations. We refer to this procedure as pooled minimum eigenvalue (PME) and show 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 our knowledge, no other estimation procedure exists for this setting. We show the PME estimator is consistent and asymptotically normal as n and T → ∞ jointly, such that T ≈ nd , 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. The utility of our approach is illustrated with an application to key financial variables using an unbalanced panel of US firms from merged CRSP-Compustat data set covering 2,000 plus firms over the period 1950 - 2021.
Subjects: 
multiple long run relations
pooled Minimum Eigenvalue (PME) estimator
eigenvalue thresholding
panel data
I(1) regressors
interactive time effects
financial ratios
JEL: 
C13
C23
C33
G30
Document Type: 
Working Paper
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.