Macroeconomic Foresight: Dynamic Stochastic General Equilibrium (DSGE) Models Training Course

Introduction

In the intricate world of modern macroeconomics, understanding the complex interplay of economic agents and their responses to various shocks is paramount for robust forecasting and effective policy design. Dynamic Stochastic General Equilibrium (DSGE) models stand as the cutting edge in this domain. Built upon microeconomic foundations, these sophisticated models derive aggregate behavior from the optimizing decisions of households, firms, and governments, providing a coherent and internally consistent framework to analyze business cycles, monetary policy, fiscal interventions, and structural changes within an economy.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of how to construct, solve, estimate, and apply DSGE models. From the foundational principles of dynamic optimization and rational expectations to mastering the computational tools for model simulation, calibration, and Bayesian estimation, you will gain the expertise to analyze macroeconomic phenomena with unparalleled rigor. This empowers you to engage in advanced macroeconomic research, contribute to evidence-based policymaking in central banks and government institutions, and deepen your understanding of the forces shaping global economies.

Target Audience

  • Macroeconomists and econometricians in central banks, government ministries, and international organizations.
  • Researchers and academics in economics and finance.
  • Graduate students (Master's and PhD) specializing in macroeconomics or econometrics.
  • Quantitative analysts and forecasters in financial institutions.
  • Policy advisors involved in macroeconomic strategy and evaluation.
  • Anyone seeking a deep, micro-founded understanding of aggregate economic dynamics.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the core theoretical foundations of DSGE models, including rational expectations and intertemporal optimization.
  • Grasp the construction principles of representative agent DSGE models, starting from the Real Business Cycle (RBC) framework.
  • Analyze the structure and key equations of New Keynesian DSGE models, incorporating nominal rigidities and monetary policy rules.
  • Comprehend the numerical methods for solving DSGE models, including linearization and perturbation techniques.
  • Evaluate methods for calibrating and estimating DSGE model parameters, with a focus on Bayesian estimation.
  • Develop practical skills in using specialized software (e.g., Dynare, Matlab/Julia/Python) to build, simulate, and estimate DSGE models.
  • Navigate the application of DSGE models for macroeconomic forecasting, policy analysis, and counterfactual simulations.
  • Formulate a strategic approach to extending and critically evaluating DSGE models for diverse economic questions.

Course Content

  1. Introduction to DSGE Models and Macroeconomic Foundations
  • The evolution of macroeconomic modeling: from large-scale to micro-founded models
  • What are DSGE models? Dynamic, Stochastic, General Equilibrium
  • Microfoundations: Households (utility maximization), Firms (profit maximization), Government
  • Rational expectations hypothesis and its implications
  • DSGE models vs. VARs and other empirical models: strengths and weaknesses
  1. The Real Business Cycle (RBC) Model: A Baseline
  • Building blocks of the RBC model: preferences, technology, constraints
  • Steady state analysis: finding the long-run equilibrium
  • Social planner's problem vs. decentralized equilibrium
  • Solving the RBC model: analytical solutions (if simple) and numerical methods (linearization around steady state)
  • Impulse Response Functions (IRFs) to technology shocks
  • Calibration vs. estimation: an initial discussion
  1. New Keynesian DSGE Models I: Basic Closed Economy
  • Introducing nominal rigidities: sticky prices (Calvo pricing) and sticky wages
  • The New Keynesian Phillips Curve: inflation dynamics
  • The New Keynesian IS curve: aggregate demand dynamics
  • Monetary policy: simple Taylor rules
  • Solving the basic New Keynesian model: linearization and solution algorithms
  1. New Keynesian DSGE Models II: Extensions and Frictions
  • Habit formation in consumption
  • Investment adjustment costs and variable capital utilization
  • Labor market frictions and search unemployment
  • Fiscal policy extensions: government spending, taxation, debt dynamics
  • Incorporating financial frictions and the financial accelerator mechanism
  1. Solving DSGE Models Numerically
  • State-space representation of linearized DSGE models
  • Perturbation methods for solving non-linear models (1st order, 2nd order)
  • Understanding the Blanchard-Kahn conditions for unique stable solutions
  • Software tools for solving DSGE models: Dynare syntax and commands
  • Simulating the model: generating IRFs and historical decompositions
  1. Calibration and Estimation of DSGE Models
  • Calibration: setting parameters based on steady-state relationships and external evidence
  • Maximum Likelihood Estimation (MLE) for DSGE models
  • Introduction to Bayesian Econometrics for DSGE: priors, likelihood, posteriors
  • Markov Chain Monte Carlo (MCMC) methods: Metropolis-Hastings algorithm
  • Identifying parameters and assessing model fit
  • Dealing with identification issues in DSGE models
  1. Model Evaluation and Diagnostic Tools
  • Comparing model-generated moments with empirical moments
  • Prior and posterior predictive checks
  • Filtering data: Kalman filter for state-space models
  • Model comparison criteria: Bayes factors, Deviance Information Criterion (DIC)
  • Variance decomposition and historical shock attribution
  • Robustness checks and sensitivity analysis of parameters
  1. Policy Analysis and Counterfactual Simulations
  • Using DSGE models to evaluate alternative monetary policy rules (e.g., different Taylor rules)
  • Analyzing the impact of fiscal policy changes (e.g., tax cuts, government spending shocks)
  • Simulating the effects of exogenous shocks (e.g., productivity, oil price, global demand)
  • Counterfactual experiments: "what if" scenarios for policy interventions
  • Communicating policy implications from DSGE models
  1. Open Economy DSGE Models
  • Integrating trade and international financial linkages
  • Exchange rate dynamics and uncovered interest parity (UIP)
  • Terms of Trade and global spillovers
  • Small open economy models vs. two-country models
  • Analyzing the impact of external shocks on a small open economy
  1. Advanced Topics and Future Directions
  • Heterogeneous Agent DSGE (HANK/TANK) models: going beyond the representative agent
  • Estimation with alternative filters (e.g., particle filter)
  • DSGE-VAR models and Bayesian VARs with DSGE priors
  • Incorporating climate change, pandemics, or financial crises into DSGE frameworks
  • Challenges and limitations of DSGE models: "Puzzles" and areas for ongoing research
  • The role of DSGE models in central banking and public policy today.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Macroeconomic Foresight: Dynamic Stochastic General Equilibrium (dsge) Models Training Course in Kenya
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