An Initial Study on the Forecast Model for Unemployment Rate in Malaysia
An Initial Study on the Forecast Model for Unemployment Rate Mohd Nadzri Mohd Nasir, Kon Mee Hwa and Huzaifah Mohammad1
Abstract The purpose of the article is to determine the most suitable technique to generate the forecast of unemployment rate using data from the series of Labour Force Surveys. The models understudied are based on Univariate Modelling Techniques i.e. Naïve with Trend Model, Average Change Model, Double Exponential Smoothing and Holt’s Method Model. These models are normally used to determine the short-term forecasts (one quarter ahead) by analyzing the pattern such as quarterly unemployment rates. The performances of the models are validated by retaining a portion of the quarterly observations as holdout samples. In addition, comparisons are made to see how well the historical and forecasted data matched and correlated. The selection of the most suitable model was indicated by the smallest value of mean square error (MSE). Based on the analysis, Holt’s Method Model is the most suitable model for forecasting quarterly unemployment rates. Keywords: Univariate Modelling Techniques; Forecast Model; Mean Square Error.
Introduction Forecasting is defined as the prediction of future events based on known past values of relevant variables (Makridakis, S., Wheelright, S. C. & Hyndman, R. J., 1998). Forecasting unemployment rate accurately is important because it helps economists to have a better idea of what the future economy holds (Lewis, R., & Brown, C., 2001). Besides, it is also important for the government in terms of decision and policy making. With the support of stable economic growth, Malaysia experienced low unemployment rates in the 1990s with the lowest recorded in 1997 at 2.4 per cent. From 1999 onwards, the unemployment rate has increased as a result of the financial crisis and subsequent economic downturn. Univariate Modelling Techniques are methods for analyzing data on a single variable at a time. Examples of Univariate Modelling
Abstract The purpose of the article is to determine the most suitable technique to generate the forecast of unemployment rate using data from the series of Labour Force Surveys. The models understudied are based on Univariate Modelling Techniques i.e. Naïve with Trend Model, Average Change Model, Double Exponential Smoothing and Holt’s Method Model. These models are normally used to determine the short-term forecasts (one quarter ahead) by analyzing the pattern such as quarterly unemployment rates. The performances of the models are validated by retaining a portion of the quarterly observations as holdout samples. In addition, comparisons are made to see how well the historical and forecasted data matched and correlated. The selection of the most suitable model was indicated by the smallest value of mean square error (MSE). Based on the analysis, Holt’s Method Model is the most suitable model for forecasting quarterly unemployment rates. Keywords: Univariate Modelling Techniques; Forecast Model; Mean Square Error.
Introduction Forecasting is defined as the prediction of future events based on known past values of relevant variables (Makridakis, S., Wheelright, S. C. & Hyndman, R. J., 1998). Forecasting unemployment rate accurately is important because it helps economists to have a better idea of what the future economy holds (Lewis, R., & Brown, C., 2001). Besides, it is also important for the government in terms of decision and policy making. With the support of stable economic growth, Malaysia experienced low unemployment rates in the 1990s with the lowest recorded in 1997 at 2.4 per cent. From 1999 onwards, the unemployment rate has increased as a result of the financial crisis and subsequent economic downturn. Univariate Modelling Techniques are methods for analyzing data on a single variable at a time. Examples of Univariate Modelling