Integrated modified OLS estimation for cointegrating polynomial regressions - with an application to the environmental Kuznets curve for CO2 emissions
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Date
2016
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Abstract
This paper considers the integrated modified OLS (IM-OLS) estimator for cointegrating
polynomial regressions recently developed in Vogelsang and Wagner (2014a; 2014b).
Cointegrating polynomial regressions include deterministic variables, integrated processes
and integer powers of integrated processes as explanatory variables. The stochastic
regressors are allowed to be endogenous and the stationary errors are allowed to
be serially correlated. The IM-OLS estimator allows for asymptotically standard inference
in this framework when using consistent estimators of the long run variance.
Additionally, we also provide fixed-b asymptotic theory for the case of full design to
capture the impact of kernel and bandwidth choice on the sampling distributions of
estimators and test statistics. We investigate the properties of the IM-OLS estimator
and hypothesis tests based upon it by means of a simulation study to compare its
performance with fully modified OLS (FM-OLS) and dynamic OLS (D-OLS). Finally,
we apply the method to estimate the environmental Kuznets curve for CO2 emissions
over the period 1870-2009.
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Keywords
bandwidth, kernel, integrated modified OLS estimation, fixed-b asymptotics, environmental Kuznets curve, cointegrating polynomial regression