Search Results
Working Paper
Dynamic Identification Using System Projections and Instrumental Variables
We propose System Projections on Instrumental Variables (SP-IV) to estimate dynamic structural relationships using impulse responses obtained from local projections or vector autoregressions. SP-IV replaces lag sequences of instruments in traditional IV with lead sequences of endogenous variables. By allowing the inclusion of lagged variables as controls, SP-IV weakens exogeneity requirements and can improve efficiency and effective instrument strength relative to 2SLS. We provide inference procedures under strong and weak identification, and show that SP-IV outperforms conventional IV ...
Working Paper
Uniform Priors for Impulse Responses
There has been a call for caution when using the conventional method for Bayesian inference in set-identified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for key objects of interest. This paper challenges this call. Although the prior distributions of individual impulse responses induced by the conventional method may be nonuniform, they typically do not drive the posteriors if one does not condition on the reduced-form parameters. Importantly, when the focus is on joint inference, the uniform prior over the ...
Working Paper
Dynamic Identification Using System Projections on Instrumental Variables
We propose System Projections on Instrumental Variables (SP-IV) to estimate structural relationships using regressions of structural impulse responses obtained from local projections or vector autoregressions. Relative to IV with distributed lags of shocks as instruments, SP-IV imposes weaker exogeneity requirements and can improve efficiency and increase effective instrument strength relative to the typical 2SLS estimator. We describe inference under strong and weak identification. The SP-IV estimator outperforms other estimators of Phillips Curve parameters in simulations. We estimate the ...
Working Paper
What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?
We use economic theory to rank the impact of structural shocks across sectors. This ranking helps us to identify the origins of U.S. business cycles. To do this, we introduce a Hierarchical Vector Auto-Regressive model, encompassing aggregate and sectoral variables. We find that shocks whose impact originate in the "demand" side (monetary, household, and government consumption) account for 43 percent more of the variance of U.S. GDP growth at business cycle frequencies than identified shocks originating in the "supply" side (technology and energy). Furthermore, corporate financial shocks, ...
Working Paper
Measurement Errors and Monetary Policy: Then and Now
Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinctionbetween real-time data and final data matters for the impact of monetary policy shocks: The impact on final data is substantially and systematically different (in particular, larger in magnitude for different measures of real activity) from theimpact on real-time data. These differences have persisted over the last 40 years and should be taken into account when ...
Working Paper
Constrained Discretion and Central Bank Transparency
We develop and estimate a general equilibrium model in which monetary policy can deviate from active inflation stabilization and agents face uncertainty about the nature of these deviations. When observing a deviation, agents conduct Bayesian learning to infer its likely duration. Under constrained discretion, only short deviations occur: Agents are confident about a prompt return to the active regime, macroeconomic uncertainty is low, welfare is high. However, if a deviation persists, agents? beliefs start drifting, uncertainty accelerates, and welfare declines. If the duration of the ...
Working Paper
Dynamic Identification Using System Projections and Instrumental Variables
We propose System Projections with Instrumental Variables (SP-IV) to estimate dynamic structural relationships. SP-IV replaces lag sequences of instruments in traditional IV with lead sequences of endogenous variables. SP-IV allows the inclusion of controls to weaken exogeneity requirements, can be more efficient than IV with lags, and allows identification over many time horizons without creating many-weak-instruments problems. SP-IV also enables the estimation of structural relationships across impulse responses obtained from local projections or vector autoregressions. We provide a ...
Working Paper
Comment on Giacomini, Kitagawa and Read's 'Narrative Restrictions and Proxies'
In a series of recent studies, Raffaella Giacomini and Toru Kitagawa have developed an innovative new methodological approach to estimating sign-identified structural VAR models that seeks to build a bridge between Bayesian and frequentist approaches in the literature. Their latest paper with Matthew Read contains thought-provoking new insights about modeling narrative restrictions in sign-identified structural VAR models. My discussion puts their contribution into the context of Giacomini and Kitagawa’s broader research agenda and relates it to the larger literature on estimating ...
Working Paper
Weak Instrument Bias in Impulse Response Estimators
We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers prioritizing robustness against weak instrument bias should favor vector autoregressions (VARs) over local projections (LPs). Existing testing procedures are ill-suited for assessing weak instrument bias in IRF estimates, and we propose a novel simple test based on the usual first-stage F-statistic. We ...
Working Paper
The Role of the Prior in Estimating VAR Models with Sign Restrictions
Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have been used in the literature to illustrate this potential problem are invalid. Specifically, we show that it does not make sense from a Bayesian point of view to characterize the impulse response prior based on the distribution of the impulse responses conditional on the maximum likelihood estimator of ...