Integrated modified OLS estimation and fixed-b inference for cointegrating multivariate polynomial regressions - Timothy J. Vogelsang; Martin Wagner (Institut für Höhere Studien - Institute for Advanced Studies (IHS), 2024)
This paper shows that the integrated modified OLS (IM-OLS) estimator developed for cointegrating linear regressions in Vogelsang and Wagner (2014a) can be straightforwardly extended to cointegrating multivariate polynomial regressions. These are regression models that include as explanatory variables deterministic variables, integrated processes and products of (non-negative) integer powers of these variables as regressors. The stationary errors are allowed to be serially correlated and the regressors are allowed to be endogenous. The IM-OLS estimator is tuningparameter free and does not require the estimation of any long-run variances. A scalar long-run variance, however, has to be estimated and scaled out when using IM-OLS for inference. In this respect, we consider both standard asymptotic inference as well as fixed-b inference. Fixed-b inference requires that the regression model is of full design. The results may be particularly interesting for specification testing of cointegrating relationships, with RESET-type specification tests following immediately. The simulation section also zooms in on RESET specification testing and illustrates that the performance of IM-OLS is qualitatively comparable to its performance in cointegrating linear regressions.
Keywords: C12; C13; C32; 330; Cointegration; fixed-b asymptotics; IM-OLS; multivariate polynomials; nonlinearity; RESET