Macro-Econometric Model for Medium-Term Socio-Economic Development Planning in Vietnam. Part 2: Application of the Model
DOI:
https://doi.org/10.17059/2019-3-6Keywords:
economic modelling, macro econometric models, forecasting models, models and applications, judgmental method, socio-economic development planning, strategic planning, forecasting procedures, forecasting scenarios, medium-term forecastAbstract
Vietnam is building the market economy while still determining development plans as important tools for managing and operating the economy. In response to the challenges posed by economic globalization, the Vietnamese government has established the necessity to promote the macro economic analysis and forecasting for ensuring the government’s policy-making and socio-economic development planning. A macro-econometric model for medium-term socio-economic development planning in Vietnam was built and called the Vietnam’s model. This paper answers the following questions: which key applications can be implemented using the model? What is the model’s quality of forecasts? The paper briefly presents how to forecast using the model. Then, I demonstrate how to synthesise, validate, balance and combine the model’s forecasts implemented by the ministries and provincial authorities, and with application of judgemental methods. I establish the ways to build baseline and target forecast scenarios. The paper shows how the model can evaluate shocks and economic policies. Additionally, I demonstrate how to adjust planned target indicators in the process of implementing the medium-term socio-economic development plan. The article also evaluates the accuracy of the model’s ex-ante forecasts. Finally, I apply the model’s ex-ante forecasts for producing the official forecasts for building the Socio-Economic Development Plan (SEDP) at national scope for the period from 2016 to 2020.References
Thanh D. V. (2017). Báo cáo tổng hợp: Nghiên cứu phương pháp dự báo kinh tế - xã hội trung hạn ở Liên bang Nga và khả năng ứng dụng vào Việt Nam [Synthesis Report: Research on medium-term socio-economic forecasting methodologies in the Russian Federation and its applicability to Vietnam]. Vietnam: National Library of Science and Technology, 359. (In Vietnamese)
Thanh, D. V. (2019). Macro econometric model for medium-term socio-economic development planning in Vietnam - Part 1: Structure of the model. Ekonomika regiona [Economy of region], 15(1), 121-136.
Kirichenko, I. A. & Smirnov, A. V. (2015). Forecasting procedures for strategic planning in the Russian Federation. Workshop: Forecasting methodology for medium-term Socio-economic development planning in the Russian Federation and its applicability to Vietnam. Jun 25, 2015, Hanoi.
Strizhkova, L. A. (2012). K Voprosu Ucheta Sistemnykh Sviazei pri Sostavlenii Makroekonomicheskogo Prognoza [The role of system relations, in macroeconomic forecasts]. Sbornik Nauchnykh Trudov IMEI [Proceedings of the Institute of Macroeconomic Research], 2, 26-78. (In Russ.)
Yekimova, K.V., Savelyeva, I. P. & Tsalo I. M. (2016). Otsenka vliyaniya izmeneniy konyuktury mirovykh rynkov na regionalnye protsessy [Assessment of the Impact of the Changes in the Situation of the World Markets on the Regional Processes]. Ekonomika regiona [Economy of Region], 12(3), 670-683. (In Russ.)
Zhuravskly, V. P. (2012). O postanovke modeli prognozirovaniya indeksov-deflyatorov vypuska v uvyazke s dannymi stsenarnykh usloviy [Forecast Models of output deflator index as part of scenario conditions]. Sbornik Nauchnykh Trudov IMEI [Proceedings of the Institute of Macroeconomic Research], 2, 154-172. (In Russ.)
Kirichenko, I. A. & Smirnov, A.V. (2013). Vliyanie makroekonomicheskikh pokazateley na formirovanie investitsionnykh protsessov v ekonomike [Influence of Macroeconomic Indicators on the development of investment processes in the economy]. Sbornik Nauchnykh Trudov IMEI [Proceedings of the Institute of Macroeconomic Research], 2, 7- 59. (In Russ.)
Kuranov, A. G. (2012). Analiz ustoychivosti reshniy modeli mezhotraslevogo balansa [Analysis of solution stability in IO models]. Sbornik Nauchnykh Trudov IMEI [Proceedings of the Institute of Macroeconomic Research], 2, 79-94. (In Russ.)
Slobodyanik, S. N. (2012). Metodicheskie problem kompleksnoy otsenki vliyaniya faktora energopotrebleniya na ekonomicheskiy rost i puti ikh resheniya [Methodological issues of relationship between energy consumption and economic growth estimations]. Sbornik Nauchnykh Trudov IMEI [Proceedings of the Institute of Macroeconomic Research], 2, 219-238. (In Russ.)
Shen, L. (2000). China’s Macro Economic Annual Model. In: L. R. Klein, I. Shinichi (Eds.), Econometric Modeling of China (pp. 307-319). Singapore: World Scientific Publishing Co. Pte. Ltd.
Yoshihisa, I. (2000). ICSEAD’s Econometric Model of the Chinese Economy. In: L. R. Klein, I. Shinichi (Eds.), Econometric Modeling of China (pp. 68-150). Singapore: World Scientific Publishing Co. Pte. Ltd.
Youcai, L. (2000). China’s economic model for project FAIR. In: L. R. Klein, I. Shinichi (Eds.), Econometric Modeling of China (pp. 201-245). Singapore: World Scientific Publishing Co. Pte. Ltd.
Rapach, D. E., Strauss, J. K. & Zhou, G. (2010). Out-of-sample equity premium prediction: combination forecasts and links to the real economy. Review of Financial Studies, 23, 821-862.
Franses, P. H. (2014). Expert adjustments of model forecasts: Theory, practice and strategies for improvement. Cambridge: Cambridge University Press, 144.
Lawrence, M., Goodwin, P., O’Connor, M. & Onkal, D. (2006). Judgmental Forecasting: A Review of Progress Over the Last 25 Years. International Journal of Forecasting, 22(3), 493-518.
Edmundson, R. H. (1990). Decomposition: A strategy for judgmental forecasting. Journal of Forecasting, 9, 305-314.
Timmermann, A. (2006). Forcast combinations. In: G. Elliott, C. W. J. Granger, A. Timmermann (Eds.), Handbook of economic forecasting, vol. 1 (pp. 134-197). Amsterdam: Elsevier.
Armstrong, J. & Green, K. C. (2018) Forecasting methods and principles: Evidence-based checklists, Journal of Global Scholars of Marketing Science, 28(2), 103-159. DOI: 10.1080/21639159.2018.1441735.
Fildes, R., Goodwin, P., Lawrence, M. & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25, 3-23.
Lim, J. S. & O’Connor, M. (1995). Judgemental adjustment of initial forecasts: Its effectiveness and biases. Journal of Behavioral Decision Making, 8, 149-168.
Armstrong, J. S. & Collopy, F. (1998). Integration of statistical methods and judgment for time series forecasting: Principles from empirical research. In: G. Wright, & P. Goodwin (Eds.), Forecasting with judgment (pp. 269-293). Wiley.
Deschamps, E. (2004). The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State. International Journal of Forecasting, 20(4), 647-657. DOI: 10.1016/j.ijforecast.2003.11.009.
Kilian, L. (2008). The Economic Effects of Energy Price Shocks. Journal of Economic Literature, 46(4), 871-909.
Klein, L. R. (2009). Background to national economic forecasts and the high-frequency model of the USA. In: L. R. Klein (Ed.), The Making of National Economic Forecasts (pp. 1-26). Cheltenham, UK; Northampton, Massachusetts, USA: Edward Elgar.
Andrei, R. (2009). Short-term forecasting of key indicators of the German economy. In: L. R. Klein (Ed.), The Making of National Economic Forecasts (pp. 121-148). Cheltenham, UK; Northampton, Massachusetts, USA: Edward Elgar.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Do Van Thanh
This work is licensed under a Creative Commons Attribution 4.0 International License.
