Panel cointegrating polynomial regressions: Group-mean fully modified OLS estimation and inference
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Date
2018
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Abstract
This paper considers group-mean fully modified OLS estimation 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, the regressor
to be endogenous and { as usual in the nonstationary panel literature { we include individual
specific fixed effects. We consider a fixed cross-section dimension, asymptotics in the time
dimension only and show that the estimator allows for standard asymptotic inference in this
setting. In both the simulations as well as an illustrative application estimating environmental
Kuznets curves for carbon dioxide emissions we compare our group-mean estimator with the
pooled fully modified OLS estimator of de Jong and Wagner (2018).
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Keywords
cointegration, polynomial transformation, panel data, group-mean estimation, fully modified OLS