A1167
Title: FM-OLS estimation and inference for SUCPRs with common integrated regressors
Authors: Martin Wagner - University of Klagenfurt, Bank of Slovenia and Institute for Advanced Studies, Vienna (Austria) [presenting]
Fabian Knorre - TU Dortmund University and Statkraft (Germany)
Abstract: Two fully modified OLS (FM-OLS) type estimators are developed for systems of seemingly unrelated cointegrating polynomial regressions with common regressors, i.e., systems of regressions that include deterministic variables, integrated processes, integer powers of integrated processes as well as common - across (potentially subsets of) equations - integrated processes and integer powers of common integrated processes as explanatory variables. The stationary errors are allowed to be serially correlated, and the regressors to be endogenous. Furthermore, the errors and regressors are allowed to be dynamically cross-sectionally correlated. The developed estimators have zero mean Gaussian mixture limiting distributions that allow for asymptotic normal or chi-squared inference. The Wald-type hypothesis tests are used as a basis to formulate tests for general forms of group-wise poolability. In case group-wise poolability is not rejected, the corresponding group-wise pooled variants of the developed FM-OLS-type estimators are provided. The simulations indicate that appropriate pooling leads, as expected, to improved performance of both the estimators and hypothesis tests based upon them. The developed methodology is applied to analyzing the environmental and material Kuznets curve hypotheses for multi-country data for CO2 and SO2 emissions and aluminum, lead and zinc usage.
