Forecasting, Cointegration and Causality Analysis of Unemployment in Pakistan Using Time Series Models

서지정보

· 발행기관 : 영남대학교 박정희새마을연구원
· 수록지 정보 : 새마을학연구 / 2권 / 2호 / 135 ~ 176페이지
· 저자명 : 무하마드 나집 울라, 김기수

초록

One of the major issues for policy makers in Pakistan is handling the continuing increase of the level of unemployment. Thus, forecasting unemployment rate is imperative to policy makers. This study aims to explore the best forecasting model among ARIMA, ARFIMA and exponential smoothing for forecasting unemployment. Secondly, this study analyzed unemployment using time-series techniques, measured long and short run relationship with population growth, labor force participation rate, crop production, and investigated the causality between unemployment and other variables. Time series data ranging from 1965 to 2014 is collected from Pakistan Economic Survey for analysis. This study evaluated the forecasting performance of the three models by using the forecast accuracy criterion such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil’s U statistics. Double Exponential Smoothing model is chosen as the best forecasted model for unemployment rate on the basis of forecast accuracy criterion. Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test is used for checking stationarity in the variables. At level, the variables were nonstationary and become stationary at first difference. The results of Johnson cointegration and Vector Error Correction model (VECM) indicated that there exists long and short run cointegration relationship between unemployment rate and other variables. Granger Causality test shows bi-directional causality running from crop production toward population growth.

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