Cointegrating polynomial regressions: Fully modified OLS estimation and inference

Arbeitspapier

This paper develops a fully modified OLS estimator for cointegrating polynomial regressions, i.e. for regressions including deterministic variables, integrated processes and powers of integrated processes as explanatory variables and stationary errors. The errors are allowed to be serially correlated and the regressors are allowed to be endogenous. The paper thus extends the fully modified approach developed in Phillips and Hansen (1990). The FM-OLS estimator has a zero mean Gaussian mixture limiting distribution, which is the basis for standard asymptotic inference. In addition Wald and LM tests for specification as well as a KPSS-type test for cointegration are derived. The theoretical analysis is complemented by a simulation study which shows that the developed FM-OLS estimator and tests based upon it perform well in the sense that the performance advantages over OLS are by and large similar to the performance advantages of FM-OLS over OLS in cointegrating regressions.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 264

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: cointegrating polynomial regression ; fully modified OLS estimation ; integrated process , testing

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hong, Seung Hyun ; Wagner, Martin
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/68510
• Last update: 10.03.2025, 11:43 AM CET