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Online Estimation of DSGE Models

Author

Listed:
  • Michael Cai

    (Northwestern University)

  • Marco Del Negro

    (FRB New York)

  • Edward Herbst

    (Federal Reserve Board)

  • Ethan Matlin

    (FRB New York)

  • Reca Sarfati

    (FRB New York)

  • Frank Schorfheide

    (Department of Economics, University of Pennsylvania)

Abstract

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for \online" estimation, 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 of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts of macroeconomic variables and interest rate expectations. We also study whether the predictive ability of DSGE models changes when we use priors that are substantially looser than those that are commonly adopted in the literature.

Suggested Citation

  • Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," PIER Working Paper Archive 19-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:19-014
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    References listed on IDEAS

    as
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    5. Fasolo, Angelo M. & Araujo, Eurilton & Jorge, Marcos Valli & Kornelius, Alexandre & Marinho, Leonardo Sousa Gomes, 2024. "Brazilian macroeconomic dynamics redux: Shocks, frictions, and unemployment in SAMBA model," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(2).
    6. Jabeen, Fauzia & Kaur, Puneet & Talwar, Shalini & Malodia, Suresh & Dhir, Amandeep, 2022. "I love you, but you let me down! How hate and retaliation damage customer-brand relationship," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
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    More about this item

    Keywords

    Adaptive algorithms; Bayesian inference; density forecasts; online estimation; sequential Monte Carlo methods;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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