Arbeitspapier

Identifying shocks via time-varying volatility

An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances is available only under specific parametric assumptions on the variance process. I propose a new identification argument that identifies the SVAR up to shock orderings using the autocovariance structure of second moments of the residuals implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions like those required by existing approaches. I offer intuitive criteria to select among shock orderings; this selection does not impact inference asymptotically. The identification scheme performs well in simulations. I apply it to the debate on fiscal multipliers. I obtain estimates that are lower than those of Blanchard and Perotti (2002) and Mertens and Ravn (2014), but in line with those of more recent studies.

Sprache
Englisch

Erschienen in
Series: Staff Report ; No. 871

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Financial Econometrics
Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy: General (includes Measurement and Data)
Fiscal Policy
Fiscal Policies and Behavior of Economic Agents: General
Thema
identification
impulse response function
structural shocks
SVAR
fiscal multiplier
time-varying volatility
heteroskedasticity

Ereignis
Geistige Schöpfung
(wer)
Lewis, Daniel J.
Ereignis
Veröffentlichung
(wer)
Federal Reserve Bank of New York
(wo)
New York, NY
(wann)
2018

Handle
Letzte Aktualisierung
10.03.2025, 11:44 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

  • Lewis, Daniel J.
  • Federal Reserve Bank of New York

Entstanden

  • 2018

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