Topics in Structural VAR Econometrics

1. Front Matter

   Pages i-xiii

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2. From VAR models to Structural VAR models

   • Gianni Amisano, Carlo Giannini

   Pages 1-28

3. Identification analysis and F.I.M.L estimation for the K-Model

   • Gianni Amisano, Carlo Giannini

   Pages 29-39

4. Identification analysis and F.I.M.L estimation for the C-Model

   • Gianni Amisano, Carlo Giannini

   Pages 40-47

5. Identification analysis and F.I.M.L estimation for the AB-Model

   • Gianni Amisano, Carlo Giannini

   Pages 48-59

6. Impulse response analysis and forecast error variance decomposition in SVAR modelling

   • Gianni Amisano, Carlo Giannini

   Pages 60-77

7. Long run a prior information. Deterministic components. Cointegration

   • Gianni Amisano, Carlo Giannini

   Pages 78-106

8. Model selection in Structural VAR analysis

   • Gianni Amisano, Carlo Giannini

   Pages 107-113

9. The problem of non-fundamental representations

   • Gianni Amisano, Carlo Giannini

   Pages 114-130

10. Two applications of Structural VAR analysis

    • Gianni Amisano, Carlo Giannini

    Pages 131-150

11. Back Matter

    Pages 151-181

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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 stochastic 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.

• Kointegration
• Regressionsanalyse
• Simulation
• cointegration
• econometrics
• integration
• modeling
• regression
• regression analysis
• simulations
• time series
• value-at-risk
• Ökonom
• Ökonometrie

• Dipartimento di Scienze Economiche, Università di Brescia, Brescia, Italy

  Gianni Amisano

• Dipartimento di Economia Politica e Metodi Quantitativi, Università di Pavia, Pavia, Italy

  Carlo Giannini

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