A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast

Renhe Wang, Tong Wang, Zhiyong Qian and Shulan Hu

Finance Research Letters, 2023, vol. 58, issue PC

Abstract: We introduce an efficient Markov Chain Monte Carlo sampler in precision-based algorithms for the estimation of the Random Switching Exponential Smoothing model, a versatile forecasting mechanism for time series data characterized with changing trends. Through a series of simulation experiments, RC-MCMC exhibits superior parameter estimation accuracy, particularly for datasets featuring low persistence trends. Furthermore, an empirical evaluation using the Bank for International Settlements’ quarterly time series data on the non-financial sector’s total credit relative to GDP validates the findings. The out-of-sample results indicate that the proposed approach outperforms its counterparts in estimating and forecasting accuracy for trending time series data.

Keywords: Random switching exponential smoothing; Precision-based algorithms; Bayesian estimation; Forecasting; Credit (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008978

DOI: 10.1016/j.frl.2023.104525

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