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Tests of Joint Hypotheses for Time Series Regression with a Unit Root

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  • Perron, P.

Abstract

This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression "F-Test" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

Suggested Citation

  • Perron, P., 1986. "Tests of Joint Hypotheses for Time Series Regression with a Unit Root," Cahiers de recherche 8632, Universite de Montreal, Departement de sciences economiques.
    • Handle: RePEc:mtl:montde:8632
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    File URL: http://hdl.handle.net/1866/412
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    Cited by:

    1. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    2. Mehmet Balcilar & Godwin Oluseye Olasehinde-Williams & Muhammad Shahbaz, 2019. "Asymmetric dynamics of insurance premium: the impact of monetary policy uncertainty on insurance premiums in Japan," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 12(3), pages 233-247.

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