Mark Bognanni


Work in progress

Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility, Mark Bognanni and John Zito, Draft coming soon.

Working papers

A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification, Mark Bognanni, PDF

Abstract. This paper develops a new class of structural vector autoregressions (SVARs) with time-varying parameters, which I call a drifting SVAR (DSVAR). The DSVAR is the first structural time-varying parameter model to allow for internally consistent Bayesian inference under exact--or set--identification, nesting the widely used SVAR framework as a special case. I prove that the DSVAR implies a reduced-form representation, from which structural inference can proceed similarly to the widely used two-step approach for SVARs: first estimating reduced-form parameters and then imposing identifying restrictions to choose among the set of observationally equivalent structural parameters consistent with the reduced-form estimates. In a special case, the reduced form implied by the DSVAR is a tractable known model for which I provide the first algorithm for Bayesian estimation of all free parameters. I demonstrate the framework in the context of Baumeister and Peersman's (2013b) work on time variation in the elasticity of oil demand.

Published papers

A Sequential Monte Carlo Approach to Inference in Multiple-Equation Markov-Switching Models, Mark Bognanni and Edward Herbst, Journal of Applied Econometrics. 2018, 33, 126–140. Published version
Previously: "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach." FEDS 2015-116, FRB Cleveland Working Paper no. 14-27

Abstract. Vector autoregressions with Markov-switching parameters (MS-VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS-VARs has remained challenging, impeding their uptake for empirical applications. We show that Sequential Monte Carlo (SMC) estimators can accurately estimate MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. We use SMC’s flexibility to demonstrate that model selection among MS-VARs can be highly sensitive to the choice of prior.

Resting papers

An Empirical Analysis of Time-Varying Fiscal Multipliers, Mark Bognanni, Mimeo. PDF

Abstract. Debates among policy makers about the appropriate response of fiscal policy to the Great Recession centered on the size of the fiscal multiplier, defined as the number of dollars that output increases in response to a dollar of fiscal stimulus.Mostly using the structure of micro-founded Dynamic Stochastic General Equilibrium models, macroeconomists have argued that fiscal multipliers may vary over time and be particularly large in liquidity traps or during recessions. I extend existing techniques for the Bayesian estimation of vector autoregressions with Markov-switching in selected coefficients to empirically investigate both the extent of time-variation in fiscal multipliers and what factors cause the variation. In contradiction to recent results in the literature, my estimates suggest that the value of the government spending multiplier is likely smaller in recessions than in expansions, while tax cuts have a greater effect in recessions than in expansions. I find little evidence that regime change in monetary policy rules and fiscal policy rules have caused time variation in the value of the fiscal multiplier.

Federal Reserve publications

Economic Commentaries

An Assessment of the ISM Manufacturing Price Index for Inflation Forecasting, Mark Bognanni and Tristan Young, Economic Commentary, 2018-05. HTML PDF
New Normal or Real-Time Noise? Revisiting the Recent Data on Labor Productivity, Mark Bognanni and John Zito, Economic Commentary, 2016-16. HTML PDF

Economic Trends

Does GDI Data Change our Understanding of the Business Cycle? Mark Bognanni and Christian Garciga, Economic Trends, 01.14.16. HTML PDF
US Fiscal Policy: Recent Trends in Historical Context, Mark Bognanni and Sara Millington, Economic Trends, 07.14.15. HTML PDF