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Author:Bognanni, Mark 

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, and unconstrained by reliance on convenient relationships between prior and likelihood. For our second contribution, we exploit SMC?s flexibility to demonstrate that the use of priors with superior data fit alters inference about the presence of time variation in macroeconomic dynamics. Using the same data as Sims, Waggoner, and Zha (2008, we provide evidence of recurrent episodes characterized by a flat Phillips Curve.
AUTHORS: Herbst, Edward; Bognanni, Mark
DATE: 2014-11-12

Working Paper
A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification
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 probabilistic 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: beginning with estimation of a reduced form and then choosing among observationally equivalent candidate structural parameters via the imposition of identifying restrictions. In a special case, the implied reduced form 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.
AUTHORS: Bognanni, Mark
DATE: 2018-09-11

Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility
We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for the model as an effective mutation kernel for fighting particle degeneracy. We show that, relative to using MCMC alone, our algorithm increases the precision of inference while reducing computing time by an order of magnitude when estimating a medium-scale VAR-SV model.
AUTHORS: Bognanni, Mark; Zito, John
DATE: 2019-12-16

Journal Article
New Normal or Real-Time Noise? Revisiting the Recent Data on Labor Productivity
Some economic observers have argued that the weakness of recent productivity data indicates we have entered a new era of low economic growth. To investigate that claim, we study labor productivity between 1968 and 2016 and compare recent productivity growth to its past behavior. We find that though recent productivity data are unambiguously weak, they are not greatly out of line with the variation of productivity over the historical record. We find that when labor productivity has been weak in the past, it did not persist at those levels. In addition, we find a systematic tendency to understate growth in real time, suggesting that the average rate of the past six years will likely be revised up in future.
AUTHORS: Zito, John; Bognanni, Mark
DATE: 2016-12

Journal Article
An Assessment of the ISM Manufacturing Price Index for Inflation Forecasting
The Institute for Supply Management produces a measure of pricing trends, the manufacturing price index or ISMPI, that is constructed from its periodic surveys of purchasing and supply executives. We investigate this measure?s predictive content for producer and consumer price inflation by assessing its ability to improve inflation forecasts for three broad monthly inflation measures. We find that the ISMPI has some predictive content for producer prices but not for consumer prices.
AUTHORS: Young, Tristan; Bognanni, Mark
DATE: 2018-05

Journal Article
Has the Real-Time Reliability of Monthly Indicators Changed over Time?
Economic data are routinely revised after they are initially released. I examine the extent to which the real-time reliability of six monthly macroeconomic indicators important to policymakers has remained stable over time by studying the time-series properties of their short-term and long-term revisions. I show that the revisions to many monthly economic indicators display systematic behaviors that policymakers could build into their real-time assessments. I also find that some indicators? revision series have varied substantially over time, suggesting that these indicators may now be less useful in real time than they once were. Lastly, I find that substantial revisions tend to occur indefinitely after the initial data release, a result which suggests a certain degree of caution is in order when using even thrice-revised monthly data in policymaking.
AUTHORS: Bognanni, Mark
DATE: 2019-10

Journal Article
A Forecasting Assessment of Market-Based PCE Inflation
This article explores the potential for market-based inflation measures to improve inflation forecasting. To do so, I compare the pseudo-real time forecasting performance of a suite of models for forecasting total or “headline” PCE inflation over the short and medium run. In the forecasting exercise, a simple model using only market-based core PCE inflation showed the best forecasting performance at all horizons.
AUTHORS: Bognanni, Mark
DATE: 2020-01

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian 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. For our second contribution, we use SMC's flexibility to demonstrate that the choice of prior drives the key empirical finding of Sims, Waggoner, and Zha (2008) as much as does the data.
AUTHORS: Herbst, Edward; Bognanni, Mark
DATE: 2015-12-18

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