scholarly journals Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 151
Author(s):  
Chanamart Intapan ◽  
Chukiat Chaiboonsri ◽  
Pairach Piboonrungroj
Keyword(s):  
Bayesian Inference ◽  
Theoretical Model ◽  
Empirical Studies ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Daily Number ◽  
Real Gdp ◽  
Circular Flows ◽  
Dynamic Stochastic ◽  
To Come

We evaluated the movement in the daily number of COVID-19 cases in response to the real GDP during the COVID-19 pandemic in Thailand from Q1 2020 to Q1 2021. The aim of the study was to find the number of COVID-19 cases that could maintain circulation of the country’s economy. This is the question that most of the world’s economies have been facing and trying to figure out. Our theoretical model introduced dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian inference. From the results of the study, it was found that the most reasonable number of COVID-19 cases that still maintains circulation of the country’s economy is about 3000 per month or about 9000 per quarter. This demonstrates that the daily number of COVID-19 cases significantly affects the growth of Thailand’s real GDP. Economists and policymakers can use the results of empirical studies to come up with guidelines or policies that can be implemented to reduce the number of infections to satisfactory levels in order to avoid Thailand lockdown. Although the COVID-19 outbreak can be suppressed through lockdown, the country cannot be locked down all the time.

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.

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 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.

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 ON THE NUMERICAL ACCURACY OF FIRST-ORDER APPROXIMATE SOLUTIONS TO DSGE MODELS

2016 ◽  
Vol 21 (7) ◽  
pp. 1811-1826
Author(s):  
Christopher Heiberger ◽  
Torben Klarl ◽  
Alfred Maussner
Keyword(s):  
Approximate Solutions ◽  
Dynamic Stochastic General Equilibrium ◽  
System Of Nonlinear Equations ◽  
Dsge Models ◽  
Numerical Accuracy ◽  
Equilibrium Conditions ◽  
First Order ◽  
Dynamic Stochastic ◽  
Simulation Results ◽  
Ill Conditioned Matrices

Many algorithms that provide approximate solutions for dynamic stochastic general equilibrium (DSGE) models employ the QZ factorization because it allows a flexible formulation of the model and exempts the researcher from identifying equations that give raise to infinite eigenvalues. We show, by means of an example, that the policy functions obtained by this approach may differ from both the solution of a properly reduced system and the solution obtained from solving the system of nonlinear equations that arises from applying the implicit function theorem to the model's equilibrium conditions. As a consequence, simulation results may depend on the specific algorithm used and on the numerical values of parameters that are theoretically irrelevant. The sources of this inaccuracy are ill-conditioned matrices as they emerge, e.g., in models with strong habits. Researchers should be aware of those strange effects, and we propose several ways to handle them.

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.

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