What determines the growth of services sector in Pakistan? A comparison of ARDL bound testing and time varying parametric estimation with general to specific approach

Muhammad AJMAIR, Khadim HUSSAIN, Sabahat AKRAM, Ambreen ZEB

Abstract


Abstract. This empirical study followed time varying parametric approach (Kalman Filter) and auto regression distributed lag (ARDL) with general to specific approach to find out relevant macroeconomic determinants of Pakistan’s services sector’s growth. To our best of knowledge, no author has made such study that employed these estimation techniques to find out determinants of services sector growth in Pakistan while employing general to specific approach. Current study bridges this gap. Annual data was taken from World Development Indicators (2014) during period 1976-2014. Main findings of the study are that rolling regression estimates of explanatory variables justify the use of Kalman filtering approach. Results show that inflation has negative effect on services sector output growth in case of TVP approach. This result does match with ARDL results.  Net foreign direct investment has positive and significant effect on services sector output growth in both techniques of estimation. Gross national expenditures with positive effect are the relevant significant determinants of services sector output growth at five percent significance level in case of TVP approach while relationship was insignificant in case of ARDL estimation. Impact of remittances received on services sector growth is negative in case of time varying parametric approach. This result is different from ARDL results where relationship is positive and significant at five percent level of significance. All the one step ahead state vectors confirmed the stability of models in case of time varying parametric approach. Cumulative sum of recursive residuals (CUSUM) and cumulative sum of recursive residuals square (CUSUMQ) also confirmed the stability of results of auto regression distributed lag. Based on these empirical findings, we conclude that government should focus on service sector growth augmenting factors while formulating any policy relevant to the concerned sector. 

Keywords. Services sector, Kalman filter, Rolling regression.

JEL. C22, O11, O40.

Keywords


Services sector; Kalman filter; Rolling regression.

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References


Agostino, A.D., Serafini, R. & Warmedinger, M.W. (2006). Sectoral explanations of employment in Europe the role of services. European Central Bank, Working Paper Series, No.625.

Asteriou, D. & Hall, S.G. (2007). Applied Econometrics - A Modern Approach, Basingstoke: Palgrave Macmillan.

Dickey, D.A., & Fuller, W.A., (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072. doi. 10.2307/1912517

Engle, R.F., & Granger, C.W. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276. doi. 10.2307/1913236

Gilal, A.M., & Chandio, R. (2013). Exchange market pressure and intervention index for Pakistan: Evidence from a time-varying parameter approach, GSTF Journal on Business Review, 2(4), 18-24. doi. 10.5176/2010-4804_2.4.246

Greiner, A. (2005). Models of economic growth and mathematical models in economics, Encyclopedia of Life Support Systems (EOLSS), Vol. II.

Gordon, J., & Gupta, P. (2003) Understanding India's services revolution, IMF-NCAER Conference, A tale of two giants: India’s and China’s experience with reform. pp.1-34.

Hussain, I. (2012). Economic Reforms in Pakistan: One Step Forward, Two Steps Backwards, A lecture at PIDE.

Hyndman, R., & Snyder, R. (2001). Kalman Filter. [Retrieved from].

Inglesi-Lotz, R. (2011). The evolution of price elasticity of electricity demand in South Africa: A Kalman Filter application. Energy Policy, 39(6), 3690-3696. doi. 10.1016/j.enpol.2011.03.078

Jain, D., Nair, K.S., & Jain, V. (2015). Factors affecting GDP (Manufacturing, Services, Industry): An Indian perspective, Annual Research Journal of Symbiosis Centre for Management Studies Pune, 3, 38-56.

Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration-with applications to the demand for money, Oxford Bulletin of Economics and Statistics, 52(2), 169-210. doi. 10.1111/j.1468-0084.1990.mp52002003.x

Kim, K.H., Zhou, Z., & Wub, W.B. (2010). Non-stationary structural model with time-varying demand elasticities, Journal of Statistical Planning and Inference, 140(12), 3809-3819. doi. 10.1016/j.jspi.2010.04.045

Lucas, R.E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3-42. doi. 10.1016/0304-3932(88)90168-7

Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. First Edition, Springer-Verlag Berlin Heidelberg, Germany.

Moosa, I.A. (1997). A cross-country comparison of Okun’s coefficient, Journal of Comparative Economics, 24(3), 335-356. doi. 10.1006/jcec.1997.1433

Morisson, G.W. & Pike, D.H. (1977). Kalman Filter applied to statistical forecasting, Journal of Management Sciences, 23(7), 768-774. doi. 10.1287/mnsc.23.7.768

Nkoro, E., & Uko, K.A. (2016). Autoregressive distributed lag (ARDL) cointegration technique: application and interpretation, Journal of Statistical and Econometric Methods, 5(4), 63-91.

Oyakhilomen, O., & Zibah, R.G. (2014). Agricultural production and economic growth in Nigeria: implications for rural poverty alleviation. Quarterly Journal of International Agriculture, 53(4), 207-223.

Pesaran, M.H., Shin, Y., & Smith, R.J. (2001). Bounds testing approaches to the analysis of level relationship, Journal of Applied Econometrics, 16, 289-326. doi. 10.1002/jae.616

Phillips, P.C.B. & Perron, P. (1988). Testing for unit roots in time series regression, Biometrika, 75(2), 335-346. doi. 10.1093/biomet/75.2.335

Singh, M., & Kaur, K. (2014). India’s services sector and its determinants: An empirical investigation, Journal of Economics and Development Studies, 2(2), 385-406.

Slade, M.E. (1989). Modeling stochastic and cyclical components of technical change: An application of the Kalman Filter, Journal of Econometrics, 41(3), 363-383. doi. 10.1016/0304-4076(89)90067-5

Stockman, A. (1981). Anticipated inflation and the capital stock in a cash in-advance economy. Journal of Monetary Economics, 8(3), 387-393. doi. 10.1016/0304-3932(81)90018-0

Taban, S. (2010). An examinations of governmental spending economic growth nexus for Turkey using the bound test approach, International Research Journal of Finance and Economics, 48, 184-193.

Thamae, R.I., Thamae, L.Z., & Thamae, T.M. (2015). Dynamics of electricity demand in Lesotho: A Kalman Filter approach. Studies in Business and Economics, 10(1), 1-10. doi. 10.1515/sbe-2015-0012

Tsadkan, A. (2013). The nexus between public spending and economic growth in Ethiopia: Empirical investigation. Unpublished Master Thesis, Addis Ababa University.

Viren, M. (2001). The Okun curve is non-linear, Economics Letters, 70(2), 253-57. doi. 10.1016/S0165-1765(00)00370-0

Wu, Y. (2007) Service sector growth in China and India: A comparison. China: An International Journal, 5(1), 1-20. doi. 10.1142/S021974720700009X




DOI: http://dx.doi.org/10.1453/ter.v4i3.1425

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