Authors:
Nasir Saleem,Atif Akbar,A. H. M. Rahmatullah Imon,Abu Sayed Md Al Mamun,DOI NO:
https://doi.org/10.26782/jmcms.2022.02.00006Keywords:
AIC,Linear Trend Model,Time Series Models,Gross,Domestic Product,Forecasting,Abstract
The purpose of this study was to forecast the Gross Domestic Product (GDP) of Pakistan. GDP of Pakistan was observed and analyzed by using time series analysis techniques and Box-Jenkins methodology. These methods were used for analysis, estimation, and forecasting purposes. Data of GDP of Pakistan was collected from (1961-2020). The model selected had the lowest Akaike Information Criteria (AIC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Percentage Error (MPE), Schwarz Bayesian Information Criteria (SBIC), Schwarz Bayesian Criteria (SBC), values and high R2. It was used for forecasting the GDP of Pakistan for the next 55 years from 2021-to 2075. Data were analyzed by using SPSS-21, Eviews-3, and Statgraphics-16. We have found that the best model is the Linear trend model. Based on this selected model, we have found that the GDP of Pakistan would become 2.51199 in 2035 and would become less in 2075 as compared to 2025.Refference:
I. Ahmed, V., & Amjad, R. (1984). The management of Pakistan’s economy 1947-82.
II. Badmus, M. A., & Ariyo, O. S. (2011). Forecasting cultivated areas and production of maize in Nigerian using ARIMA Model. Asian Journal of Agricultural Sciences, 3(3), 171-176.
III. Aprigliano, V., Ardizzi, G., & Monteforte, L. (2019). Using payment system data to forecast economic activity. 60th issue (October 2019) of the International Journal of Central Banking.
IV. Aastveit, K. A., Albuquerque, B., & Anundsen, A. K. (2020). Changing supply elasticities and regional housing booms.
V. Adnan, Z., Chowdhury, M., & Mallik, G. (2019). Foreign direct investment and total factor productivity in South Asia. Theoretical & Applied Economics, 2(2).
VI. Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (2020). The structure of economic news (No. w26648). National Bureau of Economic Research.
VII. Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509-1526.
VIII. Carlsen, M., & Storgaard, P. E. (2010). Dankort payments as a timely indicator of retail sales in Denmark (No. 66). Danmarks Nationalbank Working Papers.
IX. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 158(3), 419-444.
X. Dashti, H. M., Al-Zaid, N. S., Mathew, T. C., Al-Mousawi, M., Talib, H., Asfar, S. K., & Behbahani, A. I. (2006). Long term effects of ketogenic diet in obese subjects with high cholesterol level. Molecular and cellular biochemistry, 286(1), 1-9.
XI. DeLeeuw, J. (1992). Introduction to Akaike (1973) information theory and an extension of the maximum likelihood principle. In Breakthroughs in statistics (pp. 599-609). Springer, New York, NY.
XII. Falki, N. (2009). Impact of foreign direct investment on economic growth in Pakistan. International Review of Business Research Papers, 5(5), 110-120
XIII. Gilbert, J. M., & Balouchi, F. (2008). Comparison of energy harvesting systems for wireless sensor networks. International Journal of automation and computing, 5(4), 334-347.
XIV. Ghosh Dastidar, S., Mohan, S., & Chatterji, M. (2013). The relationship between public education expenditure and economic growth: The case of India.
XV. Galbraith, J. W., & Tkacz, G. (2018). Nowcasting with payments system data. International Journal of Forecasting, 34(2), 366-376.
XVI. Gujarati, D. N., Bernier, B., & Bernier, B. (2004). Econométrie (pp. 17-5). Brussels: De Boeck.
XVII. Hussain, S., Siddique, S., & Saboor, Q. A. (2003). Heart rate variability in early phase of acute myocardial infarction and convalescence. Journal of the College of Physicians and Surgeons–pakistan: JCPSP, 13(2), 67-69.
XVIII. Jain, A., & Pennacchiotti, M. (2010, August). Open entity extraction from web search query logs. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) (pp. 510-518).
XIX. Liewkhim, (2005). Time series modeling and forecasting of black papper prices
XX. Makridakis, C., & Nochetto, R. H. (2003). Elliptic reconstruction and a posteriori error estimates for parabolic problems. SIAM journal on numerical analysis, 41(4), 1585-1594.
XXI. Murry, D. A., & Nan, G. D. (1994). A definition of the gross domestic product-electrification interrelationship. The Journal of energy and development, 19(2), 275-283.
XXII. McLeod, A. I., & Hipel, K. W. (1978). Simulation procedures for Box‐Jenkins models. Water Resources Research, 14(5), 969-975.
XXIII. Mallick, L., Das, P. K., & Pradhan, K. C. (2016). Impact of educational expenditure on economic growth in major Asian countries: Evidence from econometric analysis. Theoretical & Applied Economics, 23(2).
XXIV. Mastromarco, C., & Zago, A. (2012). On modeling the determinants of TFP growth. Structural Change and Economic Dynamics, 23(4), 373-382.
XXV. Nelson, C. R. (1972). The prediction performance of the FRB-MIT-PENN model of the US economy. The American Economic Review, 62(5), 902-917.
XXVI. Nihan, N. L., & Holmesland, K. O. (1980). Use of the Box and Jenkins time series technique in traffic forecasting. Transportation, 9(2), 125-143.
XXVII. Rahmaddi, R., & Ichihashi, M. (2011). Exports and economic growth in Indonesia: A causality approach based on multi-variate error correction model. 17(2), 53-73.
XXVIII. Raju, S., & Balakrishnan, M. (2019). Nowcasting economic activity in India using payment systems data. Journal of Payments Strategy & Systems, 13(1), 72-81.
XXIX. Rodrigues, M. T. D. (2017). Efeitos de um programa domiciliário de exercício físico de oito semanas em pacientes com fibromialgia (Doctoral dissertation).
XXX. Schmitz, A., & Watts, D. G. (1970). Forecasting wheat yields: an application of parametric time series modeling. American Journal of Agricultural Economics, 52(2), 247-254.
XXXI. Thavaneswaran, A., & Abraham, B. (1988). Estimation for non‐linear time series models using estimating equations. Journal of Time Series Analysis, 9(1), 99-108.
XXXII. Tomić, S., & Dressel, M. (2015). Ferroelectricity in molecular solids: a review of electrodynamic properties. Reports on Progress in Physics, 78(9), 096501.
XXXIII. Thorsrud, L. A. (2020). Words are the new numbers: A newsy coincident index of the business cycle. Journal of Business & Economic Statistics, 38(2), 393-409.
XXXIV. Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John wiley & sons.
XXXV. Tsay, A. A. (2002). Risk sensitivity in distribution channel partnerships: implications for manufacturer return policies. Journal of Retailing, 78(2), 147-160.
XXXVI. Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John wiley & sons.