scholarly journals Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model

2015 ◽  
Vol 19 (2) ◽  
Author(s):  
Stelios Bekiros ◽  
Alessia Paccagnini
Keyword(s):  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dsge Model ◽  
Time Data ◽  
Vector Autoregressive ◽  
Us Economy ◽  
The Us ◽  
Dynamic Stochastic ◽  
Statistical Identification ◽  
Density Forecasts

AbstractAlthough policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.

The new macroeconometrics: A Bayesian approach

2018 ◽  
Author(s):  
Peter Challenor ◽  
Doug McNeall ◽  
James Gattiker
Keyword(s):  
Central Banks ◽  
Dynamic Stochastic General Equilibrium ◽  
Public Institutions ◽  
New Keynesian ◽  
Dsge Models ◽  
Us Economy ◽  
Private Organizations ◽  
Final Good ◽  
The Us ◽  
Dynamic Stochastic

This article examines the dynamics of the US economy over the last five decades using Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models. It highlights an example application in what is commonly referred to as the new macroeconometrics, which combines macroeconomics with econometrics. The article describes a benchmark New Keynesian DSGE model that incorporates four types of agents: households that consume, save, and supply labour to a labour ‘packer’; a labour ‘packer’ that puts together the labour supplied by different households into an homogeneous labour unit; intermediate good producers, who produce goods using capital and aggregated labour; and a final good producer that mixes all the intermediate goods. It also considers the application of the model in policy analysis for public institutions such as central banks, along with private organizations and businesses. Finally, it discusses three avenues for further research in the estimation of DSGE models.

scholarly journals Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach

2007 ◽  
Vol 97 (3) ◽  
pp. 586-606 ◽  
Author(s):  
Frank Smets ◽  
Rafael Wouters
Keyword(s):  
Business Cycle ◽  
Dynamic Stochastic General Equilibrium ◽  
Structural Shocks ◽  
Us Economy ◽  
The Us ◽  
Out Of Sample ◽  
Key Issues ◽  
Macroeconomic Time Series ◽  
Dynamic Stochastic ◽  
Out Of Sample Prediction

Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macroeconomic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model, we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the “Great Moderation”? (JEL D58, E23, E31, E32)

scholarly journals A NOTE ON AN EXTENSION OF A CLASS OF SOLUTIONS TO DYNAMIC PROGRAMMING PROBLEMS ARISING IN ECONOMIC GROWTH

2011 ◽  
Vol 16 (3) ◽  
pp. 472-476 ◽  
Author(s):  
Jürgen Antony ◽  
Alfred Maußner
Keyword(s):  
Economic Growth ◽  
Dynamic Programming ◽  
General Equilibrium ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dsge Model ◽  
Savings Rate ◽  
Dynamics And Control ◽  
Dynamic Stochastic ◽  
And Control

This note extends the findings of Benhabib and Rusticchini [Journal of Economic Dynamics and Control 18, 807–813 (1994)], who provide a class of dynamic stochastic general equilibrium (DSGE) models whose solution is characterized by a constant savings rate. We show that this class of models may be interpreted as a standard–representative agent DSGE model with costly adjustment of capital.

scholarly journals Monetary Policy Analysis with Potentially Misspecified Models

2009 ◽  
Vol 99 (4) ◽  
pp. 1415-1450 ◽  
Author(s):  
Marco Del Negro ◽  
Frank Schorfheide
Keyword(s):  
Policy Analysis ◽  
Recent Literature ◽  
Model Misspecification ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Estimation Of Parameters ◽  
Dsge Model ◽  
Misspecified Models ◽  
Trade Offs ◽  
Dynamic Stochastic

Policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models faces two challenges: estimation of parameters that are relevant for policy trade-offs, and treatment of the deviations from the cross-equation restrictions. Using post-1982 US data, we study the robustness of the policy prescriptions from a state-of-the-art DSGE model with respect to two approaches to model misspecification pursued in the recent literature: (i) adding shocks to the DSGE model and/or generalizing the processes followed by these shocks; and (ii) explicit modeling of deviations from cross-equation restrictions (DSGE-VAR). (JEL C51, E13, E43, E52, E58)

scholarly journals Balanced Growth Approach to Tracking Recessions

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 14
Author(s):  
Marta Boczoń ◽  
Jean-François Richard
Keyword(s):  
Autoregressive Process ◽  
Vector Autoregressive ◽  
Balanced Growth ◽  
Economic Agents ◽  
Us Economy ◽  
Forecasting Performance ◽  
The Us ◽  
Vector Autoregressive Process ◽  
Dynamic Stochastic ◽  
Business Cycle Model

In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely upon an Error Correction Mechanism to adjust to changes in target ratios driven by an underlying state Vector AutoRegressive process. Our proposal is illustrated by an application to a pilot Real Business Cycle model for the US economy from 1948 to 2019. An extensive recursive validation exercise over the last 35 years, covering 3 recessions, is used to highlight its parameters invariance, tracking and 1- to 3-step ahead forecasting performance, outperforming those of an unconstrained benchmark Vector AutoRegressive model.

scholarly journals Online estimation of DSGE models

2020 ◽  
Author(s):  
Michael Cai ◽  
Marco Del Negro ◽  
Edward Herbst ◽  
Ethan Matlin ◽  
Reca Sarfati ◽  
...  
Keyword(s):  
Sequential Monte Carlo ◽  
Forecast Accuracy ◽  
Predictive Performance ◽  
Dsge Models ◽  
Dsge Model ◽  
Online Estimation ◽  
Prior Variance ◽  
Out Of Sample ◽  
Dynamic Stochastic ◽  
Density Forecasts

Summary This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared with the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.

scholarly journals Constructing a Small-Region DSGE Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Kenichi Tamegawa
Keyword(s):  
General Equilibrium ◽  
Interest Rates ◽  
Numerical Simulations ◽  
National Economy ◽  
Regional Economy ◽  
Small Region ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dsge Model ◽  
Dynamic Stochastic

This paper constructs a tractable dynamic stochastic general equilibrium (DSGE) model of a regional economy that is considered small because it does not affect its national economy. To examine properties of our small-region DSGE model, we conduct several numerical simulations. Notably, fiscal expansion in our model is larger than that in standard DSGE models. This is because the increase in regional output does not raise interest rates, and this leads to the crowding-in effects of investment.

Bayesian Macroeconometrics

2011 ◽  
pp. 292-389 ◽  
Author(s):  
Marco del Negro
Keyword(s):  
Bayesian Methods ◽  
Dynamic Stochastic General Equilibrium ◽  
Reduced Form ◽  
Dsge Models ◽  
Var Models ◽  
Economic Implications ◽  
Vector Autoregressive ◽  
Macroeconomic Time Series ◽  
Structural Vars ◽  
Dynamic Stochastic

This article presents the challenges that arise since macroeconomists often work in data-rich environments. It emphasizes multivariate models that can capture the co-movements of macroeconomic time series analysis. It discusses vector autoregressive (VAR) models distinguishing between reduced-form and structural VARs. Reduced-form VARs summarize the autocovariance properties of the data and provide a useful forecasting tool. The article shows how Bayesian methods have been empirically successful in responding to these challenges. It also encounters dynamic stochastic general equilibrium (DSGE) models that potentially differ in their economic implications. With posterior model probabilities, inference and decisions can be based on model averages. This article discusses inference with linearized as well as nonlinear DSGE models and reviews various approaches for evaluating the empirical fit of DSGE models. It concludes with a discussion of model uncertainty and decision-making with multiple models.

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

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. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

scholarly journals Decision between Public Investment and Public Consumption: A Policy Analysis

2020 ◽  
Vol V (I) ◽  
pp. 131-152
Author(s):  
Muhammad Raashid ◽  
Abdul Saboor ◽  
Aneela Afzal
Keyword(s):  
Public Investment ◽  
Policy Decision ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Model ◽  
Structural Unemployment ◽  
Government Investment ◽  
Long Run ◽  
Public Consumption ◽  
Dynamic Stochastic ◽  
Best Fit

This study aims to draw a policy decision between public investment and public consumption by designing a Dynamic Stochastic General Equilibrium (DSGE) model for the economy of Pakistan which is experiencing persistent shocks that have stressed the growth pattern. The DSGE model has a microeconomic foundation and justifies locus critics by envisioning an artificial economy. The model is evaluated and set to best fit for data through an exercise of moment matching. Government consumption shocks and Government Investment shocks are used to trace out the behaviour of the economy. The analysis confirms that Pakistan economy could go for capital formation through public investment but it results in compromised public consumption and structural unemployment. It is further concluded that the export base and long-run public investment programs are needed to achieve sustainable development in the economy.

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