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Jel Classification:C32 

Working Paper
Measuring the Natural Rate of Interest : International Trends and Determinants

U.S. estimates of the natural rate of interest ? the real short-term interest rate that would prevail absent transitory disturbances ? have declined dramatically since the start of the global financial crisis. For example, estimates using the Laubach-Williams (2003) model indicate the natural rate in the United States fell to close to zero during the crisis and has remained there through the end of 2015. Explanations for this decline include shifts in demographics, a slowdown in trend productivity growth, and global factors affecting real interest rates. This paper applies the ...
Finance and Economics Discussion Series , Paper 2016-073

Working Paper
A narrative approach to a fiscal DSGE model

This version: March 28, 2016 First version: February 2014 {{p}} Structural DSGE models are used both for analyzing policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified VARs based on narrative shocks. This paper asks whether both approaches agree. First, I show that, theoretically, the narrative VAR approach is valid in a class of DSGE models with Taylor-type policy rules. Second, I quantify whether the two approaches also agree empirically, that is, ...
Working Papers , Paper 16-11

Identifying shocks via time-varying volatility

An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances can only be recovered from the data under specific parametric assumptions on the variance process. I propose a new identification argument that identifies the SVAR up to shock orderings using the autocovariance structure of second moments of the residuals, implied by an arbitrary stochastic process for ...
Staff Reports , Paper 871

Working Paper
The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equi-energy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full ...
FRB Atlanta Working Paper , Paper 2014-21

Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade

We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, mobility, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel ...
Working Paper Series , Paper WP-2021-05

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 Papers (Old Series) , Paper 1427

The microstructure of a U.S. Treasury ECN: the BrokerTec platform

We assess the microstructure of the U.S. Treasury securities market following its migration to electronic trading. We model price discovery using a vector autoregression model of price and order flow. We show that both trades and limit orders affect price dynamics, suggesting that traders also choose limit orders to exploit their information. Moreover, while limit orders have smaller price impact, their greater variation contributes more to the variance of price updates. Lastly, we find increased price impact of trades and especially limit orders following major announcements (such as FOMC ...
Staff Reports , Paper 381

Working Paper
DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors

Currently, there is growing interest in dynamic stochastic general equilibrium (DSGE) models that have more parameters, endogenous variables, exogenous shocks, and observables than the Smets and Wouters (2007) model, and substantial additional complexities from non-Gaussian distributions and the incorporation of time-varying volatility. The popular DYNARE software package, which has proved useful for small and medium-scale models is, however, not capable of handling such models, thus inhibiting the formulation and estimation of more re-alistic DSGE models. A primary goal of this paper is to ...
Working Papers , Paper 21-02

Working Paper
Refining the Workhorse Oil Market Model

The Kilian and Murphy (2014) structural vector autoregressive model has become the workhorse model for the analysis of oil markets. I explore various refinements and extensions of this model, including the effects of (1) correcting an error in the measure of global real economic activity, (2) explicitly incorporating narrative sign restrictions into the estimation, (3) relaxing the upper bound on the impact price elasticity of oil supply, (4) evaluating the implied posterior distribution of the structural models, and (5) extending the sample. I demonstrate that the substantive conclusions of ...
Working Papers , Paper 1910

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 ...
International Finance Discussion Papers , Paper 1173


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