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Author: Francis X. Diebold
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Languages : en
Pages : 0
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Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.
Author: Francis X. Diebold
Publisher:
ISBN:
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Languages : en
Pages : 0
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Book Description
Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.
Author: Francis X. Diebold
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ISBN:
Category :
Languages : en
Pages : 50
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Author: Sergey Ivashchenko
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Category :
Languages : en
Pages :
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Author: Edward P. Herbst
Publisher: Princeton University Press
ISBN: 0691161089
Category : Business & Economics
Languages : en
Pages : 295
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Book Description
Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Author: Andrea Carriero
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 58
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Book Description
This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate the model, in order to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of real-time GDP growth in the U.S. from 1985 through 2011. In terms of point forecasts, our proposal is comparable to alternative econometric methods and survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful, while parameter time-variation does not seem to matter.
Author: Katerina Petrova
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Languages : en
Pages : 0
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We propose a novel quasi-Bayesian Metropolis-within-Gibbs algorithm that can be used to estimate drifts in the shock volatilities of a linearized dynamic stochastic general equilibrium (DSGE) model. The resulting volatility estimates differ from the existing approaches in two ways. First, the time variation enters non-parametrically, so that our approach ensures consistent estimation in a wide class of processes, thereby eliminating the need to specify the volatility law of motion and alleviating the risk of invalid inference due to mis-specification. Second, the conditional quasi-posterior of the drifting volatilities is available in closed form, which makes inference straightforward and simplifies existing algorithms. We apply our estimation procedure to a standard DSGE model and find that the estimated volatility paths are smoother compared to alternative stochastic volatility estimates. Moreover, we demonstrate that our procedure can deliver statistically significant improvements to the density forecasts of the DSGE model compared to alternative methods.
Author: Todd E. Clark
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Category :
Languages : en
Pages :
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Author: Jian Wang
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Category :
Languages : en
Pages :
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Author: Edward Herbst
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Category : Bayesian statistical decision theory
Languages : en
Pages : 42
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This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.
Author: Denis Tkachenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
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Abstract: In this dissertation, we use frequency domain methods to address issues related to identification and estimation in linearized dynamic stochastic general equilibrium (DSGE) and stochastic volatility models.The first chapter provides a necessary and sufficient condition for the local identification of the structural parameters based on the (first and) second order properties of the linearized DSGE model. The condition is flexible and simple to verify. It is extended to study identification through a subset of frequencies, partial identification, conditional identification, and constrained identification. When lack of identification is detected, the method can be used to trace out nonidentification curves. For estimation in nonsingular systems, we consider a frequency domain quasi-maximum likelihood (FDQML) estimator and present its asymptotic properties, which can be different from existing results due to the structure of the DSGE model. Finally, we discuss a quasi-Bayesian procedure for estimation and inference that can incorporate relevant prior distributions and is computationally attractive.The second chapter analyzes a popular medium scale DSGE model of Smets and Wouters (2007) using the framework developed in the previous chapter. For identification, in addition to checking parameter identifiability, we derive the corresponding nonidentification curve. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the non-parametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture.The final chapter proposes an FDQML estimator of the integrated volatility of financial assets in the noisy high frequency data setting. The approach allows for the microstructure noise to be a stationary linear process, and is analytically tractable. In practice, we approximate the noise process by a finite order autoregression, where the order is chosen using the Akaike information criterion (AIC). The simulation study shows that the finite sample performance of the estimator is very similar to its time domain analogue in the case of i.i.d. noise, and is substantially better when more sophisticated noise specifications are considered.
