Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy

Abstract

A growing line of research makes use of structural changes and different volatility regimes found in the data in a constructive manner to improve the identification of structural parameters in structural vector autoregressions (SVARs). A standard assumption made in the literature is that the reduced form unconditional error covariance matrix varies while the structural parameters remain constant. Under this hypothesis, it is possible to identify the SVAR without needing to resort to additional restrictions. With macroeconomic data, the assumption that the transmission mechanism of the shocks does not vary across volatility regimes is debatable. We derive novel necessary and sufficient rank conditions for local identification of SVARs, where both the error covariance matrix and the structural parameters are allowed to change across volatility regimes. Our approach generalizes the existing literature on 'identification through changes in volatility' to a broader framework and opens up interesting possibilities for practitioners. An empirical illustration focuses on a small monetary policy SVAR of the US economy and suggests that monetary policy has become more effective at stabilizing the economy since the 1980s.

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

“…For example, Rigobon (2003), Rigobon and Sack (2003) Lanne and Lütkepohl (2008), and Bacchiocchi and Fanelli (2012) use simply a deterministic shift in the variances while Normandin and Phaneuf (2004) and Bouakez and Normandin (2010) model the changes in volatility by a vector GARCH process and propose a Markov switching (MS) mechanism for changes in volatility. The GARCH and MS approaches have the disadvantage that estimation of the models is very involved and so far reliable estimation methods are available only for small models with three or four variables and a moderate number of lags and volatility states at best.…”

Gimme a Break! Identification and Estimation of the Macroeconomic Effects of Monetary Policy Shocks in the U.S.

“…However, our methodology can in principle deal with a number of break dates larger than one (for a discussion, see Bacchiocchi and Fanelli (2012)). …”

“…4 In a companion paper, Bacchiocchi and Fanelli (2012) apply the methodology proposed in this paper to a small-scale VAR which focuses on the interaction between nominal interest rate and money. Our larger-scale VAR involves macroeconomic indicators such as consumption and investment, which carry relevant information on agentsí expectations.…”

Ambiguity and Time-Varying Risk Aversion in Sovereign Debt Markets

“…The assumption on the constancy of B may be challenged and newer literature is adopting more flexible models with state dependent impact matrices (Bacchiocchi and Fanelli, 2015;Podstawski and Velinov, 2016). However, the feature of interest in the current setup is the ability to make use of the statistical properties of the data in order to identify a set of structural shocks, rather than the analysis of a potential state dependency of the shock transmission that could be introduced into the model via a regime switching structural impact matrix B.…”