Working Paper: CEPR ID: DP4650
Authors: Sylvia Frühwirth-Schnatter; Sylvia Kaufmann
Abstract: We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on marginal likelihoods. A simulation study documents the efficiency gains in estimation and forecasting that are realized when appropriately grouping the time series of a panel. Two economic applications illustrate the usefulness of the method in analysing also extensions to Markov switching within clusters and heterogeneity within clusters, respectively.
Keywords: Clustering; Markov Chain Monte Carlo; Markov Switching; Mixture Modelling; Panel Data
JEL Codes: C11; C33; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Bayesian model selection based on marginal likelihoods (C52) | improvements in forecasting performance (C53) |
| clustering similar time series (C38) | improve estimation and forecasting efficiency (C51) |
| clustering similar time series (C38) | more accurate parameter estimates and forecasts (C51) |
| model's flexibility (C52) | ability to capture economic dynamics (E39) |