Arbeitspapier

Identification of structural VAR models via independent component analysis: A performance evaluation study

Independent Component Analysis (ICA) is a statistical method that transforms a set of random variables in least dependent linear combinations. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR identification, and compare their performance in recovering structural coefficients. Moreover, after suggesting an algorithm that solve the ICA indeterminacy problem, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on distributional scenarios that get gradually close the Gaussian case, which is the case where ICA methods fail to recover the independent components. In terms of statistical properties of the ICA estimators, we find no evidence that a method outperforms all others. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.

Sprache
Englisch

Erschienen in
Series: LEM Working Paper Series ; No. 2020/24

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Fiscal Policy
Thema
Independent Component Analysis
Identification
Structural VAR
Impulse response functions
Non-Gaussianity
Generalized normal distribution

Ereignis
Geistige Schöpfung
(wer)
Moneta, Alessio
Pallante, Gianluca
Ereignis
Veröffentlichung
(wer)
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
(wo)
Pisa
(wann)
2020

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Moneta, Alessio
  • Pallante, Gianluca
  • Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)

Entstanden

  • 2020

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