Forecasting in the presence of in and out of sample breaks

Jiawen Xu () and Pierre Perron
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Jiawen Xu: Shanghai University of Finance and Economics

No WP2018-014, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: We present a frequentist-based approach to forecast time series in the presence of in-sample and out-of-sample breaks in the parameters of the forecasting model. We first model the parameters as following a random level shift process, with the occurrence of a shift governed by a Bernoulli process. In order to have a structure so that changes in the parameters be forecastable, we introduce two modifications. The first models the probability of shifts according to some covariates that can be forecasted. The second incorporates a built-in mean reversion mechanism to the time path of the parameters. Similar modifications can also be made to model changes in the variance of the error process. Our full model can be cast into a conditional linear and Gaussian state space framework. To estimate it, we use the mixture Kalman filter and a Monte Carlo expectation maximization algorithm. Simulation results show that our proposed forecasting model provides improved forecasts over standard forecasting models that are robust to model misspecifications. We provide two empirical applications and compare the forecasting performance of our approach with a variety of alternative methods. These show that substantial gains in forecasting accuracy are obtained.

Keywords: instabilities; structural change; forecasting; random level shifts; mix- ture Kalman filter. (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2017-01, Revised 2018-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Forecasting in the presence of in and out of sample breaks (2015)
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