NONSENSE REGRESSIONS BETWEEN INTEGRATED PROCESSES OF DIFFERENT ORDERS

Francesc Mármol

37 citations0/0 files storedOxford Bulletin of Economics and Statistics

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Monetary Policy and Economic Impact Advanced Statistical Methods and Models Diverse Scientific and Economic Studies

Abstract

Abstract Herein we develop an analytical study of the asymptotic distributions obtained when we run linear regressions in the levels of stochastically independent integrated time series when the orders of integration of the dependent and independent variables are different. These theoretical findings largely explain the Monte Carlo results recently reported in Banerjee et al. (1993).

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1982Trends and random walks in macroeconmic time series 1986Understanding spurious regressions in econometrics 1993Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data 1995Do purchasing power parity and uncovered interest rate parity hold in the long run? An example of likelihood inference in a multivariate time-series model 1986Do we reject too often?1985Trends, random walks, and tests of the permanent income hypothesis 1958Studies in the Quantity Theory of Money 2001Spurious regressions with stationary series

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1996Efficient Estimation of Cointegrating Relationships Among Higher Order and Fractionally Integrated Processes 2010A note on spurious significance in regressions involving I(0) and I(1) variables

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