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" Topics in structural var econometrics. "
Gianni Amisano
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BL
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732152
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b551939
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Gianni Amisano
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Topics in structural var econometrics.\ Gianni Amisano
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| Publication Statement
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[Place of publication not identified]: Springer-Verlag Berlin An, 2012
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| ISBN
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3642644813
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: 9783642644818
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| Contents
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l: From VAR models to Structural VAR models.- 1.1. Origins of VAR modelling.- 1.2. Basic concepts of VAR analysis.- 1.3. Efficient estimation: the BVAR approach.- 1.4. Uses of VAR models.- 1.4.1. Dynamic simulation.- 1.4.2. Unconditional and conditional forecasting.- 1.4.3. Granger causality.- 1.5. Different classes of Structural VAR models.- 1.6. The likelihood function for SVAR models.- 1.7. Structural VAR models vs. dynamic simultaneous equations models.- 1.8. Some examples of Structural VARs in the applied literature.- 1.8.1. Triangular representation deriving from the Choleski decomposition of ?.- 1.8.2. Blanchard and Quah (1989) long run constraints.- 1.8.3. A traditional interpretation of macroeconomic fluctuations: Blanchard (1989).- 2: Identification analysis and F.I.M.L. estimation for the K-Model.- 2.1. Identification analysis.- 2.2. F.I.M.L. estimation.- 3: Identification analysis and F.I.M.L. estimation for the C-Model.- 3.1. Identification analysis.- 3.2. F.I.M.L. estimation.- 4: Identification analysis and F.I.M.L. estimation for the AB-Model.- 4.1. Identification analysis.- 4.2. F.I.M.L. estimation.- 5: Impulse response analysis and forecast error variance decomposition in SVAR modeling.- 5.1. Impulse response analysis.- 5.2. Variance decomposition (by Antonio Lanzarotti).- 5.3. Finite sample and asymptotic distributions for dynamic simulations.- 6: Long run a priori information. Deterministic components. Cointegration.- 6.1. Long run a priori information.- 6.2. Deterministic components.- 6.3. Cointegration.- 6.3.1. Representation and identification issues.- 6.3.2. Estimation issues.- 6.3.3. Interpretation of the cointegrating coefficients.- 6.3.4. Asymptotic distributions of the parameter estimates: Structural VAR analysis with cointegrated series.- 6.3.5. Finite sample properties.- 7: Model selection in Structural VAR analysis.- 7.1. General aspects of the model selection problem.- 7.2. The dominance ordering criterion.- 7.3. The likelihood dominance criterion (LDC).- 8: The problem of non fundamental representations.- 8.1. Non fundamental representations in time series models.- 8.2. Economic significance of non fundamental representations and examples.- 8.3. Non fundamental representations and applied SVAR analysis.- 8.4. An example.- 9: Two applications of Structural VAR analysis.- 9.1. A traditional interpretation of Italian macroeconomic fluctuations.- 9.1.1 The reduced form VAR model.- 9.2.2 Cointegration properties.- 9.3.3 Structural identification of instantaneous relationships.- 9.4.4 Dynamic simulation.- 9.2. The transmission mechanism among Italian interest rates.- 9.2.1 The choice of the variables.- 9.2.2 The reduced form VAR model.- 9.2.3 Cointegration properties.- 9.2.4 Structural identification of instantaneous relationships.- 9.2.5 Dynamic simulation.- 9.2.6 The Lippi-Reichlin criticism.- Annex 1: The notions of reduced form and structure in Structural VAR modelling.- Annex 2: Some considerations on the semantics, choice and management of the K, C, and AB-models.- Appendix A.- Appendix B.- Appendix C (by Antonio Lanzarotti and Mario Seghelini).- References.
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Gianni Amisano
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