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Moving average threshold heterogeneous autoregressive (MAT‐HAR) models

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Listed:
  • Kaiji Motegi
  • Xiaojing Cai
  • Shigeyuki Hamori
  • Haifeng Xu

Abstract

We propose moving average threshold heterogeneous autoregressive (MAT‐HAR) models as a novel combination of heterogeneous autoregression (HAR) and threshold autoregression (TAR). The MAT‐HAR has multiple groups of lags of a target series, and a threshold term can appear in each group. The threshold is a moving average of lagged target series, which guarantees time‐varying thresholds and simple estimation via least squares. We show via Monte Carlo simulations that the MAT‐HAR has sharp in‐sample and out‐of‐sample performance. An empirical application on the industrial production of Japan suggests that significant threshold effects exist, and the MAT‐HAR has a higher forecast accuracy than the HAR.

Suggested Citation

  • Kaiji Motegi & Xiaojing Cai & Shigeyuki Hamori & Haifeng Xu, 2020. "Moving average threshold heterogeneous autoregressive (MAT‐HAR) models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1035-1042, November.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:7:p:1035-1042
    DOI: 10.1002/for.2671
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    1. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    3. Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    6. Tong, Howell, 2015. "Threshold models in time series analysis—Some reflections," Journal of Econometrics, Elsevier, vol. 189(2), pages 485-491.
    7. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    8. Shao, Xiaofeng, 2011. "A bootstrap-assisted spectral test of white noise under unknown dependence," Journal of Econometrics, Elsevier, vol. 162(2), pages 213-224, June.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    2. Bucci, Andrea & Sanmarchi, Francesco & Santi, Luca & Golinelli, Davide, 2024. "Evaluating the nonlinear association between PM10 and emergency department visits," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

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