Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/273806 
Year of Publication: 
2022
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2022-093/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We study a set of fully modified (FM) estimators in multivariate cointegrating polynomial regressions. Such regressions allow for deterministic trends, stochastic trends, and integer powers of stochastic trends to enter the cointegrating relations. A new feasible generalized least squares estimator is proposed. Our estimator incorporates: (1) the inverse autocovariance matrix of multidimensional errors and (2) second-order bias corrections. The resulting estimator has the intuitive interpretation of applying a weighted least squares objective function to filtered data series. Moreover, the required second-order bias corrections are convenient byproducts of our approach and lead to a conventional asymptotic inference. Based on different FM estimators, multiple multivariate KPSS-type of tests for the null of cointegration are constructed. We then undertake a comprehensive Monte Carlo study to compare the performance of the FM estimators and the related tests. We find good performance of the proposed estimator and the implied test statistics for linear hypotheses and cointegration.
Subjects: 
Cointegrating Polynomial Regression
Cointegration Testing
Fully Modified Estimation
Generalized Least Squares
JEL: 
C12
C13
C32
Document Type: 
Working Paper

Files in This Item:
File
Size
1.36 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.