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 The time machine: a simulation approach for stochastic trees

2011 ◽  
Vol 467 (2132) ◽  
pp. 2350-2368 ◽  
Author(s):  
Ajay Jasra ◽  
Maria De Iorio ◽  
Marc Chadeau-Hyam
Keyword(s):  
Variance Reduction ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Computing Time ◽  
Point Of View ◽  
Computational Time ◽  
Recent Common Ancestor ◽  
Theoretical Point ◽  
Monte Carlo Techniques ◽  
Most Recent Common Ancestor

In this paper, we consider a simulation technique for stochastic trees. One of the most important areas in computational genetics is the calculation and subsequent maximization of the likelihood function associated with such models. This typically consists of using importance sampling and sequential Monte Carlo techniques. The approach proceeds by simulating the tree, backward in time from observed data, to a most recent common ancestor. However, in many cases, the computational time and variance of estimators are often too high to make standard approaches useful. In this paper, we propose to stop the simulation, subsequently yielding biased estimates of the likelihood surface. The bias is investigated from a theoretical point of view. Results from simulation studies are also given to investigate the balance between loss of accuracy, saving in computing time and variance reduction.

Modelling Monetary Policy

2021 ◽  
pp. 293-316
Author(s):  
Juan Antonio Morales ◽  
Paul Reding
Keyword(s):  
Monetary Policy ◽  
Policy Analysis ◽  
Policy Decision ◽  
Central Banks ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dynamic Stochastic ◽  
Policy Decision Making

This last chapter deals with the toolbox that central banks use to design and implement their monetary policy strategy. Central banks develop various types of model, both for forecasting and for policy analysis. The chapter discusses the main characteristics of the models used, their strengths and limitations. It assesses how dynamic stochastic general equilibrium (DSGE) models are used for monetary policy analysis. Examples are provided on how they contribute to explore fundamental, long-term policy issues specific to LFDCs. The chapter also discusses the contribution of small semi-structural models which, though less strongly theory grounded than DSGE models, can be brought closer to the available data and are therefore possibly better suited to the context of LFDCs. Attention is also drawn to the key role of judgement as the indispensable complement, in monetary policy decision-making, to model-based policy analysis.

scholarly journals Sequential Monte Carlo Methods to Train Neural Network Models

2000 ◽  
Vol 12 (4) ◽  
pp. 955-993 ◽  
Author(s):  
J. F. G. de Freitas ◽  
M. Niranjan ◽  
A. H. Gee ◽  
A. Doucet
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Probability Distributions ◽  
Network Models ◽  
Computational Time ◽  
Optimization Strategy ◽  
Neural Network Models ◽  
Monte Carlo Techniques ◽  
Monte Carlo Algorithms ◽  
Importance Resampling

We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.

scholarly journals On DSGE Models

2018 ◽  
Vol 32 (3) ◽  
pp. 113-140 ◽  
Author(s):  
Lawrence J. Christiano ◽  
Martin S. Eichenbaum ◽  
Mathias Trabandt
Keyword(s):  
Financial Crisis ◽  
General Equilibrium ◽  
Policy Change ◽  
Great Recession ◽  
Relative Strength ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
The Great Recession ◽  
Dynamic Stochastic ◽  
Different Parts

The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.

scholarly journals The Euler equation with habits and measurement errors: Estimates on Russian micro data

2016 ◽  
Vol 63 (4) ◽  
pp. 395-409 ◽  
Author(s):  
Irina Khvostova ◽  
Alexander Larin ◽  
Anna Novak
Keyword(s):  
Euler Equation ◽  
Measurement Errors ◽  
Generalized Method Of Moments ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Elasticity Of Intertemporal Substitution ◽  
Russian Economy ◽  
Lm Test ◽  
Dynamic Stochastic ◽  
Micro Level

This paper presents estimates of the consumption Euler equation for Russia. The estimation is based on micro-level panel data and accounts for the heterogeneity of agents? preferences and measurement errors. The presence of multiplicative habits is checked using the Lagrange multiplier (LM) test in a generalized method of moments (GMM) framework. We obtain estimates of the elasticity of intertemporal substitution and of the subjective discount factor, which are consistent with the theoretical model and can be used for the calibration and the Bayesian estimation of dynamic stochastic general equilibrium (DSGE) models for the Russian economy. We also show that the effects of habit formation are not significant. The hypotheses of multiplicative habits (external, internal, and both external and internal) are not supported by the data.

Credit-Market Imperfection and Monetary Policy Within DSGE Models

2021 ◽  
pp. 16-42
Author(s):  
Salha Ben Salem ◽  
Nadia Mansour ◽  
Moez Labidi
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
Financial Stability ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Taylor Rules ◽  
Credit Market Imperfection ◽  
Credit Cycle ◽  
Dynamic Stochastic ◽  
Financial Market Frictions

This survey presented the various ways that are utilized in the literature to include financial market frictions in dynamic stochastic general equilibrium (DSGE) models. It focuses on the fundamental issue: to what extent the Taylor rules are optimal when the central bank introduces the goal of financial stability. Indeed, the latest financial crisis shows that the vulnerability of the credit cycle is considered the main source for the amplification of a small transitory shock. This conclusion changed the instrument that drives the transmission of monetary policy through the economy and pushed the policymakers to include financial stability as a second objective of the central bank.

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