Estimation and Inference in Time Series with Omitted I(1) Variables

Gerdie Everaert

22 citations0/0 files storedJournal of Time Series Econometrics

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Monetary Policy and Economic Impact Statistical and numerical algorithms Diverse Scientific and Economic Studies

Abstract

Standard cointegration analysis yields spurious results when relevant I(1) variables are omitted from the model. As an alternative, an unobserved components approach is proposed where the error term is modelled as the sum of a transitory and a random walk component. The latter should capture omitted I(1) variables and is allowed to be correlated with the observed I(1) variables. Provided that the model is correctly specified and identified, the long-run relation between the non-cointegrated variables can be estimated consistently with maximum likelihood using the Kalman filter. The robustness of this approach to the integration properties of the error terms is supported for small samples via an extensive Monte Carlo study. The proposed methodology is applied to testing purchasing power parity.

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References

1993A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems 1990Statistical Inference in Instrumental Variables Regression with I(1) Processes 1982Trends and random walks in macroeconmic time series 1996Numerical distribution functions for unit root and cointegration tests 1986DEVELOPMENTS IN THE STUDY OF COINTEGRATED ECONOMIC VARIABLES 1981Some properties of time series data and their use in econometric model specification 1981A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle’1992Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK

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