The Forecast Performance of Long Memory and Markov Switching Models
AbstractRecent research has focused on the links between long memory and structural change, stressing the long memory properties that may arise in models with parameter changes. In this paper, we contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: a Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968-1998. Although long memory models may capture some in-sample features of the data, when shifts occur in the series considered, their forecast performance is relatively poor, when compared with simple linear and Markov switching models. Moreover, our findings, in a more general framework, are in accordance with the works of Clements and Hendry (1998) and Clements and Krolzig (1998), reinforcing the idea that simple linear time series models remain useful tools for prediction.
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Bibliographic InfoPaper provided by NIPE - Universidade do Minho in its series NIPE Working Papers with number 2/2000.
Date of creation: 2000
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Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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