Testing exact rational expectations in cointegrated vector autoregressive models

DOI10.1016/S0304-4076(99)00004-4zbMath0951.62094OpenAlexW1964244431MaRDI QIDQ1808556

Søren Glud Johansen, Anders Rygh Swensen

Publication date: 10 May 2000

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4076(99)00004-4

zbMATH Keywords

cointegrationreduced rank regressionVAR modelpresent value modelrational expectations hypothesislikelihood-ratio tests

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Economic time series analysis (91B84)

Related Items (9)

PRESENT VALUE RELATIONS, GRANGER NONCAUSALITY, AND VAR STABILITY ⋮ Adjustment coefficients and exact rational expectations in cointegrated vector autoregressive models ⋮ Exact rational expectations, cointegration, and reduced rank regression ⋮ Some exact and inexact linear rational expectation models in vector autoregressive models ⋮ Multi-equational linear quadratic adjustment cost models with rational expectations and cointe\-gration ⋮ The New Keynesian Phillips curve revisited ⋮ A new approach for estimating and testing the linear quadratic adjustment cost model under rational expectations and I(1) variables ⋮ SEPARATION, WEAK EXOGENEITY, AND P-T DECOMPOSITION IN COINTEGRATED VAR SYSTEMS WITH COMMON FEATURES ⋮ Dynamic adjustment cost models with forward‐looking behaviour

Cites Work

• Cointegration in partial systems and the efficiency of single-equation analysis
• Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK
• Optimal Properties of Exponentially Weighted Forecasts
• Estimation for Partially Nonstationary Multivariate Autoregressive Models
• Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
• Co-Integration and Error Correction: Representation, Estimation, and Testing
• Econometric tests of rationality and market efficiency
• Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models

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