Identifying shocks via time-varying volatility

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Staff Report ; No. 871

Classification and Topics

• Classification: 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
• Subject: identification ; impulse response function ; structural shocks ; SVAR ; fiscal multiplier ; time-varying volatility ; heteroskedasticity

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lewis, Daniel J.
• Event: Veröffentlichung
• (who): Federal Reserve Bank of New York
• (where): New York, NY
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/210723
• Last update: 10.03.2025, 11:44 AM CET