A Survey On Forecasting of GDP: An Issue of Relevance in Macro-economics
DOI:
https://doi.org/10.61841/r3ggtc02Keywords:
ARIMA, Economy, GDP, models, policyAbstract
The present paper is a review of various models concerned with forecasting GDP, both in the short and long run. Certain parameters that influence the growth of GDP are identified and analyzed from quarterly, monthly, or yearly data sets. Regression has been heavily used in this context, be it linear or auto-regressive integrated moving average (ARIMA) model. Small bridge equations play an important role to bridge the gap between monthly and quarterly data sets and between quarterly and yearly data sets in the literature of forecasting and nowcasting GDP in economics.
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1) A. Girardi, C. Gayer and A. Reuter, “The Role of Survey Data in Nowcasting Euro Area GDP Growth”, Journal of Forecasting, vol. 35, issue 5, 2015
2) Garnitz J., Lehmann R. and Wohlrabe K., “Forecasting GDP all over the world: Evidence from comprehensive survey data”, MPRA paper no. 81772, 2017
3) I. Visco, A. Bassanini, and S. Scarpetta,Knowledge, technology, and economic growth: an OECD perspective," Research Series, May 2000.
4) J.J. Vanhaelen, L. Dresse, and J. De Mulder,The Belgian industrial confidence indicator: leading indicator of economic activity in the euro area”, Document Series, 2000
5) L. Aucremanne and D. Cornille, "Attractive Prices and Euro-rounding effects on inflation," Documents Series, November 2001.
6) D. Jorgenson, “Lifting the burden: fundamental tax reform and economic growth”, Research Series, May 2002
7) Ch. P. Himmelberg, R. G. Hubbard, and I. Love, "Investment, protection, ownership, and the cost of capital, Reearch series, 2002
8) P. Butzen, C. Fuss, and Ph. Vermeulen,The interest rate and credit channels in Belgium: an investigation with micro-level firm data," Research series, December 2001.
9) D. Cassimon, P.J. Engelen, H. Meersman, M. Van Wouwe, “Investment, uncertainty and irreversibility: evidence from Belgian accounting data", Research Series, 2002
10) L. Trigeorgis, “What do we know about investment under uncertainty?", Research Series, 2004
11) B. Maity, B. Chatterjee, “Forecasting GDP growth rates of India: An Empirical Study, International Journal of Economics and Management Science, vol. 1, pp. 55-58, 2012
12) http://www.igidr.ac.in/pdf/publication/WP-2017-018.pdf
13) A. Subramanian, “India’s GDP mis-estimation: likelihood, magnitudes, mechanisms and implications”, CID Faculty Working Paper No. 354, June 2019
14) T. Iyer, A. Sengupta, “Quarterly forecasting model for India’s economic growth: Bayesian Vector Autoregression approach”, ADB Economics Working Paper Series, 2019
15) R. Changle, S. Matharu, “A study of forecasting the GDP of India using ARIMA model”, SAMVAD International Journal of Management, SIBM Pune, 2015
16) R. Bhattacharya, P. Chakravartti and S. Mundle, “Forecasting India’s Economic Growth: A time-varying parameter regression approach”, working paper series no. 238, National Institute of Public Finance and Policy, New Delhi, 2018
17) Naliniprava Tripathy, Harsh Alipuria, “GDP calculation: Old vs New; here’s how India measures economic growth”, Financial Express, June 12, 2019
18) O. Cepni, I.E. Guney, and N.R. Swanson, “Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macro-economic data surprise factors”, Journal of Forecasting, vol. 39, issue 1, 2019
19) D. Alsinglawi, “Predicting Jordan’s GDP is based on ARIMA modeling”, Italian Journal of Pure and Applied Mathematics, Feb 2019
20) M.R. Abonazel, A.I.A. Elftah, “Forecasting Egyptian GDP using ARIMA models”, Reports on Economics and Finance, vol. 5 no. 1, 2019
21) W. Ning, B. Kuan-Jiang, Y. Zhi-fa, “Analysis and forecast of Shanxi GDP based on the ARIMA model”, Asian Agricultural Research, vol. 2 no. 1, 2010
22) B. Yang. et al., “Application of ARIMA model in the prediction of gross domestic product”, Advances in Intelligent Systems Research, volume 130, 2016
23) V. Agarwal, “GDP modeling and forecasting using ARIMA: An empirical study”, Thesis submitted to Central European University, Hungary, 2018
24) Dr. Chaido Dritsaki, “Forecasting real GDP rate through econometric models: An empirical study from Greece”, Journal of International Business and Economics, vol. 3 no. 1, 2015
25) www.rsudio-pubs-static.s3.amazonaws.com/250143-9f48aa8f32d54b82876822b03973e4f3.html
26) www.towardsdatascience.com/forecasting-gdp-in-the-eurozone-54778f79912e
27) A. Uwimana, BI. Xiuchun, and Z. Shuguang, “Modeling and forecasting Africa’s GDP with time series models”, International Journal of Scientific and Research Publications, vol. 8, issue 4, 2018
28) M.S. Wabomba, M. P. Mutiwari, and M. Fredrick, “Modeling and forecasting Kenyan GDP using ARIMa models”, Science Journal of Applied Mathematics and statistics, vol. 4, issue 2, 2016
29) E.A. Anderi, E. bugudui, “Econometric modeling of GDP time series”, Theoretical and Applied Ecconomics, vol. XVIII, no. 10(563), 2011
30) T. Nyoni, L. Muchingami, “Modeling and forecasting Botswana’s gross domestic product per capita, MPRA paper, 2017
31) M. Zakai, “A time series modeling on GDP of Pakistan”, journal of Contemporary Issues in business research, vol. 3, no. 4, 2014
32) S. Chanaim et. al., “Forecasting GDP in Asian Countries using relevant vector Machines, Thai Journal of Mathematics, Special issue on Structural change modeling and optimization in Ecometrics, 2018
33) M. Iacoviello, "Short-term forecasting: projecting Italian GDP, one quarter to two years ahead”, IMF Working paper 2019
34) M. Simionescu, “The accuracy assessment of macro-economic forecasts based on econometric models of Romania”, Procedia economics and finance, vol. 8, 2014
35) T. xie, “Research on GDP forecasting models of Heilongjiang province in China using long-term series night-time light data”, 2019
36) A.K. Singh, An empirical analysis to assess the GDP projection of Gujrat state of India, JNNCE Journal of Engineering and Management, 2017
37) L. Dvorakova, “CZ GDP prediction via neural networks and Box-Jenkins method”, 39th SHS Web of Conferences, 2017
38) www.christophsax.com/2017/05/02/forecasting-gdp/
39) Economics division, Ministry of Finance and Economic Management, Cook Islands time series, economic forecasting model, working paper no. 18/1, 2018
40) Economics division, Ministry of Finance and Economic Management, Cook Islands time series, economic forecasting model, working paper no. 18/1, 2018
41) J. Yip, “Forecasting economic risk in the EU into 2020”, 2017
42) X. Cui, W. Zhao, “Study of the modified logistic model of Chinese electricity consumption based on the change of GDP growth rate under economic new normal”, Mathematical problems in engineering, 2019
43) H. Zhang, Modeling and forecasting regional GDP in Sweden using auto-regressive models”, Maters thesis, School for technology and business studies, Dalarna university, 2013
44) M. Taht, "Seasonal adjustment and forecasting of quarterly gross domestic product: Estonian experience”, Conference on Seasonality, Luxemborg 2006
45) G. Feng et. al., “Liaoning province economic increasing forecast and analysis based on ARMA model”, IEEE International Conference on Intelligent Networks and Systems, 2010
46) J. Garnitz, R. Lehman, K. Wohlrabe, “Forecasting GDP all over the world using leading indicators based on comprehensive survey data, Applied Economics (Taylor and Francis), vol. 51, 2019
47) S. Bhaduri, S. Pohit, “Nowcasting the Indian Economy: A new approach to Knowing the now GDP”, 2018
48) A. Cuevas, E.M. Quilis, “Forecasting GDP with a dynamic factor model”, Mathworks, 2018
49) D.V. Dinh, “Forecasting domestic credit growth based on ARIMA model: evidence from Vietnam and China”, Management Science Letters, vol. 10, issue 5, 2019
50) D. Guegan, P. Rakotomarolahy, “Alternative methods of forecasting GDP”, 2010
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