Panel Cointegration with Global Stochastic Trends

TL;DR

This paper develops new estimators for panel cointegration models affected by unobserved global stochastic trends, ensuring consistent and reliable inference in the presence of cross-sectional dependence.

Contribution

It introduces two novel iterative estimators, CupBC and CupFM, that jointly estimate slope parameters and stochastic trends, overcoming inconsistency issues of standard methods.

Findings

Estimators are consistent and asymptotically normal.

Methods are valid with mixed stationary and non-stationary factors.

Standard inference procedures can be applied using the proposed estimators.

Abstract

This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservable I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously-updated and bias-corrected) and the CupFM (continuously-updated and fully-modified) estimators. We establish their consistency and derive their limiting distributions. Both are asymptotically unbiased and asymptotically mixed normal and permit inference to be conducted using standard test statistics. The estimators are also valid when there are mixed stationary and non-stationary factors, as well as when the factors are all stationary.