دراسة قياسية للنماذج الديناميكية مع تطبيقها على التنبؤ بالعمالة فى مصر

Khaleel, Tarek Mohamed and Shehata, Emad Abd Elmessih (2004): دراسة قياسية للنماذج الديناميكية مع تطبيقها على التنبؤ بالعمالة فى مصر. Published in: Egyptian Statistical Society, The 29th International Conference for Statistics Computer Science and its applications , Vol. 29, (April 2004): pp. 133-154.

Abstract

There are many econometric methods for forecasting by different economic variables in the future. recently, the procedures of dynamic forecasting either for univariate or multivariate models were available for estimation on the software packages, i.e., EVIEWS, SAS, and SHAZAM. The research problem of the study, concerned with the different types of such dynamic models, with respect to, estimation, choosing the best fit model for forecasting by the economic variables, i.e., labor and wages on the agricultural and national level. So the objective study, is to concentration and determination the best forecasting model among univariate and multivariate dynamic time series models. The time series data on the agricultural and national level were collected from the ministry of planning during the period (1975-2002). The methodology framework discussed the theoretical and mathematical approach for the dynamic univariate models, i.e., autoregressive integrated moving average (ARIMA), and multivariate models, i.e., vector autoregressive (VAR), vector error correction model (VECM), and state space model (SSM). The dynamic models contain four stages that have, identification, i.e., stationarity and cointegration tests, model selection criteria for determination the lag length, causality test, and choosing the techniques of estimation,also estimation stage, diagnostic stage for model accuracy, and forecasting stage. The study estimated the dynamic models by maximum likelihood estimation (MLE) for (ARIMA) models, and by seemingly unrelated regression for (VAR) and (VECM) models, during the period (1975-2002), and forecasting by labor and wage through the period (2003-2012). The estimation and forecasting results, indicated that the agricultural labor will increase at decreasing rate, also the relative share of agricultural labor and the total agricultural wages will decrease during the period subject to forecasting. The national labor will increase at increasing rate. Finally the study recommended by cultivation crops, adoption technology, and encouragement the investment in projects that have intensive labor, the expanding in reclamation and cultivation new lands and national projects, also increasing wages that reflect the labor productivity and performance level for increasing the efficiency of labor input.

• Item Type: MPRA Paper
• English Title: An Econometric Study of Dynamic Models with Application on Forecasting Labor in Egypt
• Language: Arabic
• Keywords: Forecasting, Labor, ARIMA, VAR, VECM, State Space
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling

References

(1) Box, George, Gwilym M. Jenkins, & Gregory C. Reinsel “Time Series Analysis: Forecasting and Control” 3rd ed, Prentice-Hall Inc., New Jersey, USA, 1994.

(2) Breusch, Trevor “Testing for Autocorrelation in Dynamic Linear Models” Aust. Econ. Papers, Vol. 17, 1978; 334-355.

(3) Cochrane, D. & Orcutt G. “Application of Least Squares Regressions to Relationships Containing Autocorrelated Error Terms” J. Am. Stat. Assoc., Vol. 44, No. 1, 1944; 32-61.

(4) David M. Lilien, et al “EViews -User's Reference Manual Version 3” McGraw-Hill Book Company Inc., New York, USA, 1998.

(5) Dickey, David & Wayne A. Fuller “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root” Econometrica, Vol. 49, No. 4, July, 1981; 1057-1072.

(6) Engle,R. &Granger C. “Co-integration and Error Correction: Representation, Estimation, and Testing” Econometrica, Vol. 55, 1987; 251–276.

(7) Engle, Robert, David Hendry & Richard J. “Exogeneity” Econometrica, Vol. 51, 1983; 277-304.

(8) Godfrey, L. “Testing for Higher Order Serial Correlation in Regression Equations when the Regressors Include lagged Dependent Variables” Econometrica, Vol., 46, 1978; 1303-1310.

(9) Granger, C. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods” Econometrica, Vol. 37, July, 1969; 424-438.

(10) Guilkey, David & Peter Schmidt “Estimation of Seemingly Unrelated Regressions with Vector Autoregressive Errors” J. Am. Stat. Assoc., Vol. 68, Sept., 1973; 642-647.

(11) Hamilton, J. “State Space Models” in Chap.50 of Handbook of Econometrics, Vol. 4, North-Holland Publishing Company, Amsterdam, Netherlands, 1994.

(12) Hendry, Daved “Maximum Likelihood Estimation of Systems of Simultaneous Regression Equations with Errors Generated by a Vector Autoregressive Process” Inter. Econ. Rev, Vol. 12, 1971; 257-272.

(13) Johansen, Soren “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models” Econometrica, Vol.59, 1991; 1551–1580.

(14) Lutkepohl, H.“Comparison of Criteria for Estimating the Order of a Vector Autoregressive Process” J.Time Series Analysis,Vol.6,1985;35-52.

(15) Phillips, P. & Perron P. “Testing for a Unit Root in Time Series Regression” Biometrika, Vol. 75, 1988; 335–346.

(16) SAS Institute, Inc. “Statistical Analysis System: SAS-User's Reference Manual Version 8.10” Cary, NC: SAS Institute, Inc., 2000.

(17) White, Kenneth “SHAZAM Econometric Computer Program-User's Reference Manual Version 8” McGraw-Hill Book Company Inc., New York, USA, 1997.

(18) Zellner, Arnold “An Efficient Method of Estimating Seemingly Unrelated Regressions and Test for Aggregation Bias” J. Am. Stat. Assoc., Vol. 57, June, 1962; 348-368.