Estimation and forecasting with time-varying parameters models and sequential method

Sun, Zhendong

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https://hdl.handle.net/2142/124632 Copy

Description

Title

Estimation and forecasting with time-varying parameters models and sequential method

Author(s)

Sun, Zhendong

Issue Date

2024-03-29

Director of Research (if dissertation) or Advisor (if thesis)

Amir-Ahmadi, Pooyan

Doctoral Committee Chair(s)

Amir-Ahmadi, Pooyan

Committee Member(s)

• Bernhardt, Dan
• Xie, Shihan
• Chen, Yuguo

Department of Study

Economics

Discipline

Economics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Economic Forecasting
• Time-varying Parameters Model
• Sequential Monte Carlo

Language

eng

Abstract

In this research, we examine the use of time-varying parameters (TVP) models for out-of-sample forecasting within the realms of macroeconomics and finance. From a methodological perspective, the efficacy of the Sequential Monte Carlo (SMC) method in estimating TVP models is emphasized. Notably, SMC provides a distinct computational edge, requiring substantially less processing time relative to the traditional Markov Chain Monte Carlo (MCMC) method, all the while preserving predictive accuracy. Furthermore, we augment a generic SMC approach by incorporating the variational Bayes method, thereby enabling it to estimate large TVP models with an integrated variable selection prior. Empirically, we embark on a detailed exploration of three out-of-sample predictive applications in the fields of macroeconomics and finance: 1) the estimation of US GDP and inflation via a trivariate VAR model; 2) the forecasting of monthly returns of the S$\&$P500 index, which integrates a comprehensive set of 143 predictors; and 3) the nowcasting of US GDP using a TVP VAR model enriched with mixed-frequency variables. Consistently, across these analytical domains, findings suggest that TVP models bolster predictive capabilities, surpassing both their fixed-parameter counterparts and other advanced methodologies.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124632

Copyright and License Information

Copyright 2024 Zhendong Sun

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