Combination of forecasts with an application to unemployment rate

M. F. Muniroh, Noriszura Ismail, M. A. Lazim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Combining forecast values based on simple univariate models may produce more favourable results than complex models. In this study, the results of combining the forecast values of Naïve model, Single Exponential Smoothing Model, The Autoregressive Moving Average (ARIMA) model, and Holt Method are shown to be superior to that of the Error Correction Model (ECM).Malaysia’s unemployment rates data are used in this study. The independent variable used in the ECM formulation is the industrial production index. Both data sets were collected for the months of January 2004 to December 2010. The selection criteria used to determine the best model, is the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Initial findings showed that both time series data sets were not influenced by the seasonality effect.

Original languageEnglish
Pages (from-to)787-796
Number of pages10
JournalPertanika Journal of Science and Technology
Volume25
Issue number3
Publication statusPublished - 1 Jul 2017

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unemployment
Unemployment
Malaysia
Patient Selection
error correction
Error correction
Datasets
forecast
rate
industrial production
selection criteria
smoothing
Mean square error
seasonality
Time series
time series analysis
time series

Keywords

  • Combination forecast
  • Error correction model
  • Unemployment rate

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

Combination of forecasts with an application to unemployment rate. / Muniroh, M. F.; Ismail, Noriszura; Lazim, M. A.

In: Pertanika Journal of Science and Technology, Vol. 25, No. 3, 01.07.2017, p. 787-796.

Research output: Contribution to journalArticle

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