Topics in Structural VAR Econometrics

by Gianni Amisano, Carlo Giannini

Overview

In recent years a growing interest in the structural V AR approach (SV AR) has followed the path-breaking works by Blanchard and Watson (1986), Bernanke (1986) and Sims (1986), especially in the U.S. applied macroeconometric literature. The approach can be used in two different, partially overlapping, directions: the interpretation of business cycle fluctuations of a small number of significant macroeconomic variables and the identification of the effects of different policies. SV AR literature shows a common feature: the attempt to "organise", in a "structural" theoretical sense, instantaneous correlations among the relevant variables. In non-structural V AR modelling, instead, correlations are normally hidden in the variance­ covariance matrix of the V AR model innovations. of independent V AR analysis tries to isolate ("identify") a set shocks by means of a number of meaningful theoretical restrictions. The shocks can be regarded as the ultimate source of shastic variation of the vector of variables which can all be seen as potentially endogenous. Looking at the development of SV AR literature we felt that it still lacked a formal general framework which could embrace the several types of models so far proposed for identification and estimation. This is the second edition of the book, which originally appeared as number 381 of the Springer series "Lecture notes in Economics of the first edition was Carlo and Mathematical Systems". The author Giannini.

Product Details

ISBN-13:	9783642644818
Publisher:	Springer Berlin Heidelberg
Publication date:	09/18/2011
Edition description:	2nd ed. 1997. Softcover reprint of the original 2nd ed. 1997
Pages:	181
Product dimensions:	5.90(w) x 9.00(h) x 0.50(d)

Table of Contents

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.- 1.5. 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.- 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.- 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.- 9.2. The transmission mechanism among Italian interest rates.- 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.