Identifying Long-run Behaviour with Non-stationary Data
AbstractResults for the identification of non-linear models are used to support the traditional form of the order condition by sufficient conditions. The sufficient conditions reveal a two step procedure for firstly checking generic identification and then testing identifiability. This approach can be extended to sub-blocks of the system and it generalizes to non-linear restrictions. The procedure is applied to an empirical model of the exchange rate, which is identified by diagonalising the system.
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Bibliographic InfoPaper provided by Economics and Finance Section, School of Social Sciences, Brunel University in its series Economics and Finance Discussion Papers with number 98-01.
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Postal: Brunel University, Uxbridge, Middlesex UB8 3PH, UK
Other versions of this item:
- BAUWENS, Luc & HUNTER, John, 2000. "Identifying long-run behaviour with non-stationary data," CORE Discussion Papers 2000043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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