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SPURIOUS REGRESSION ANALYSIS ON TIME SERIES DATA FROM FACTORS AFFECTING INDONESIAN HUMAN DEVELOPMENT INDEXS IN 1990 – 2017
Abstract
In a spurious regression conditions occur linear regression equations that are not stationary on the mean and variance. If the variables are not stationary, there will be cointegration, so it can be concluded that there is a long-term equilibrium relationship between the two research variables and in the short term there is a possibility of an imbalance, so to overcome it in this study using the Error Correction Model. The purpose of this study is to apply a cointegration test to see whether there is a long-term non-equilibrium relationship between the time series between the Human Development Index and life expectancy at birth, average school year for adults aged 25 years and over and gross national income per capita. The data used in this study are time series data between 1990-2017. The statistical management is carried out using Eviews 10. Based on the results obtained, it was concluded that 81.7% and it can be said that the types of independent variables included in the model are already good, because only 18.3% of the diversity of the dependent variable is influenced by the independent variables outside this research model.
Keywords: spurious regression, stationary, cointegration, error correction model, equilibrium
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Aljandali, A. and Tatahi, M. (2018). Economic and Financial Modelling with Eviews. A Guide for Students and Professionals. Switzerland: Springer International Publishing.
Andersen, L. C., and Carlson, K. M. (1974). St. Louis model revisited. Int. Econ. Rev. 15, 305-327.
Basuki, A. T. and Prawoto, N. (2016). Analisis Regresi Dalam Penelitian Ekonomi dan Bisnis. Jakarta: PT.
Raja Grafindo Persada.
Box, G. and Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco, CA:Holden-Day.
Cheung, Y.W., and Lai, K. S. (1995). Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3), 277–280.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427. doi:10.2307/2286348
Engineer, M., King, I. and Roy, N. (2008). The human development index as a criterion for optimal planning. Indian Growth and Development Review 1 (2), 172-192.
Engle, R.F. and C.W.J. Granger (1987). Co-integration and error correction: representation, estimation and testing. Econometrica 55, 251–76.
Gatt, E. (2005). The Human Development Index and Small States. Bank of Valletta Review, No. 32
Granger, C. W. J., Newbold, P. (1986). Forecasting Economic Time Series (Second Edition). San Diego, California: Academic Pres, Inc.
Gujarati, D. N. and Porter, D. C. (2010). Essentials of Econometrics (Fourth Edition). New York: McGraw- Hill Companies, Inc.
Hou, J., Walsh, P. P., & Zhang, J. (2015). The Dynamics of Human Development Index. The Social Science Journal, 52(3), 331–347. Doi: 10.1016/j.soscij.2014.07.003.
Insukindro (1991). Regresi Linier Lancung dalam Analisis Ekonomi: Suatu Tinjauan dengan Satu Studi Kasus di Indonesia. Jurnal Ekonomi dan Bisnis Indonesia, 6: 75-88.
Lemy, D. M., Bernarto, I., & Tulung, J.E. (2013). Pengaruh Bauran Komunikasi Pemasaran Jasa Terhadap Proses Pengambilan Keputusan Mahasiswa Memilih Perguruan Tinggi Swasta di Sulawesi Utara. Jurnal
Manajemen: Derema
Phillips, P.C.B. (1986). Understanding Spurious Regressions in Econometrics. Journal of Econometrics 33 (1986) 311-340. North-Holland.
Startz, R. (2019). EViews Illustrated. Irvine, CA: IHS Global Inc. M
Stock, J. H. (2015). Time Series: Economic Forecasting. International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Volume 24, 337–340.
DOI: https://doi.org/10.35794/jmbi.v7i3.30608
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