Panel cointegrating polynomial regression analysis and the environmental Kuznets curve
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Date
2018
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Abstract
This paper develops a modified and a fully modified OLS estimator for a panel of cointegrating
polynomial regressions, i.e. regressions that include an integrated process and its powers
as explanatory variables. The stationary errors are allowed to be serially correlated and the
regressors are allowed to be endogenous and we allow for individual and time fixed effects. Inspired
by Phillips and Moon (1999) we consider a cross-sectional i.i.d. random linear process
framework. The modified OLS estimator utilizes the large cross-sectional dimension that allows
to consistently estimate and subtract an additive bias term without the need to also transform
the dependent variable as required in fully modified OLS estimation. Both developed estimators
have zero mean Gaussian limiting distributions and thus allow for standard asymptotic inference.
Our illustrative application indicates that the developed methods are a potentially useful
addition to not least the environmental Kuznets curve literature's toolkit.
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Keywords
cointegration, unit roots, polynomial transformation, panel data, environmental Kuznets curve,