Search Results
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 ...
Working Paper
Refining Set-Identification in VARs through Independence
Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help with identification; it is robust to weak identification. In simulations we show that it controls coverage well, in ...
Working Paper
National and Regional Housing Vacancy: Insights Using Markov-switching Models
We examine homeowner vacancy rates over time and space using Markov-switching models. Our theoretical analysis extends the Wheaton (1990) search and matching model for housing by incorporating regime-switching behavior and interregional spillovers. Our approach is strongly supported by our empirical results. Estimations, using constant-only as well as Vector Autoregressions, allow us to examine differences in vacancy rates as well as explore the possibility of asymmetries within and across housing markets, depending on the state/regime (e.g., low or high vacancy) of a given housing market. ...
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, ...
Working Paper
Monetary Policy, Real Activity, and Credit Spreads : Evidence from Bayesian Proxy SVARs
This paper studies the interaction between monetary policy, financial markets, and the real economy. We develop a Bayesian framework to estimate proxy structural vector autoregressions (SVARs) in which monetary policy shocks are identified by exploiting the information contained in high frequency data. For the Great Moderation period, we find that monetary policy shocks are key drivers of fluctuations in industrial output and corporate credit spreads, explaining about 20 percent of the volatility of these variables. Central to this result is a systematic component of monetary policy ...
Briefing
Monetary Policy across Space and Time
Many major macroeconomic events have occurred across multiple countries. This Economic Brief looks at similarities and differences among the euro area, the United Kingdom, and the United States and finds that macroeconomic variables tend to become more interconnected during periods of financial distress. Movements in monetary policy are highly correlated across all three regions. In addition, inflation and unemployment become less responsive to monetary policy shocks over time.
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
Oil Price Elasticities and Oil Price Fluctuations
We study the identification of oil shocks in a structural vector autoregressive (SVAR) model of the oil market. First, we show that the cross-equation restrictions of a SVAR impose a nonlinear relation between the short-run price elasticities of oil supply and oil demand. This relation implies that seemingly plausible restrictions on oil supply elasticity may map into implausible values of the oil demand elasticity, and vice versa. Second, we propose an identification scheme that restricts these elasticities by minimizing the distance between the elasticities allowed by the SVAR and target ...
Working Paper
The Response of Stock Market Volatility to Futures-Based Measures of Monetary Policy Shocks
In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a vector autoregressive model, our findings reveal a significant and asymmetric response of stock returns and volatility to monetary policy shocks. Although the increase in the volatility risk premium, futures-trading volume, and leverage appear to contribute to a short-term increase in volatility, the longer-term dynamics of volatility are dominated by monetary policy's effect on fundamentals. The estimation results from a bivariate VAR-GARCH model suggest that the Fed does not ...
Working Paper
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common ...