Dorji, Karma Minjur Phuntsho (2024): Exploring Nowcasting Techniques for Real-Time GDP Estimation in Bhutan.
Preview |
PDF
MPRA_paper_121380.pdf Download (1MB) | Preview |
Abstract
In various policy institutions, current estimates of quarterly GDP growth are frequently employed to advise decision makers on the current state of the economy. The bridge equation serves as a fundamental model for nowcasting, elucidating GDP growth through the utilization of time-aggregated business cycle indicators. Recent academic literature has shown significant interest in an alternative method for nowcasting known as mixed-data sampling, abbreviated as MIDAS. Given this context, the paper examines the following questions: How can we estimate the annual GDP of Bhutan through MIDAS and bridge equations? Do they matter for nowcasting GDP growth in practice? By addressing these questions, the study aims to to provide insights into the application and comparative efficacy of these nowcasting techniques in an empirical context.
Item Type: | MPRA Paper |
---|---|
Original Title: | Exploring Nowcasting Techniques for Real-Time GDP Estimation in Bhutan |
English Title: | Exploring Nowcasting Techniques for Real-Time GDP Estimation in Bhutan |
Language: | English |
Keywords: | Bridge equations, Mixed-data Sampling (MIDAS), GDP, nowcasting. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General |
Item ID: | 121380 |
Depositing User: | Mr. Karma Minjur Phuntsho Dorji |
Date Deposited: | 06 Jul 2024 13:17 |
Last Modified: | 06 Jul 2024 13:17 |
References: | Armesto, M.T., Engemann, K.M. & Owyan, M.T., 2010. Forecasting with Mixed Frequencies. Federal Reserve Bank of St. Louis Review, pp.521-536. Angelini, E., Henry, J., & Marcellino, M. (2006). Interpolation with a large information set. Journal of Economic Dynamics and Control, 30, 2693-2724. Auerbach, A.J. (1982) 'The Index of Leading Indicators: "Measurement with Theory," Thirty five Years Later', The Review of Economic and Statistics, November, Vol. 64, No. 4. Andreou, C., Ghysels, E., & Kourtellos, A. (2010). Regression models with mixed sampling frequencies. Journal of Econometrics, 158(2), 246-261. Baffigi, A., Golinelli, R., & Parigi, G. (2004). Bridge Models to Forecast the Euro Area GDP. International Journal of Forecasting, 20, 447-460. Bańbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Nowcasting and the Real Time Data Flow. European Central Bank Working Paper, No 1564. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. Bragoli, D., & Fosten, J. (2017). Nowcasting Indian GDP. Oxford Bulletin of Economics and Statistics, 79(2), 260-282. Chipman, J. S. (2014). Gauss-Markov Theorem. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 577-582). Springer. Chowdhury, M. Z., & Turin, T. C. (2020). Variable selection strategies and its importance in clinical prediction modelling. Family Medicine and Community Health, e000262. https://doi.org/10.1136/fmch-2020-000262. Claudia, F., Massimiliano, M., & Christian, S. (2011). U-MIDAS: MIDAS regressions with Unrestricted Lag Polynomials. Discussion Paper Series 1: Economic Studies, 2011(35). Deutsche Bundesbank. Clements, M., & Galvão, A. (2008). Macroeconomic Forecasting with mixed frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics, 26, 546-554. Clements, M. P. & Galvao, A. B. (2009). Forecasting US output growth using leading indicators: An appraisal using MIDAS models. Journal of Applied Econometrics, 24(7), 1187-1206. Denton, F. (1971) "Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization," Journal of the American Statistical Association, 82, pp. 99–102. Den Reijer, A. and Johansson, A., 2019. Nowcasting Swedish GDP with a large and unbalanced data set. Empirical Economics, 57(4), pp.1351-1373. Di Fonzo, T. (1994) "Temporal Disaggregation of a System of Time Series when the Aggregate Is Known: Optimal vs. Adjustment Methods." In: Workshop on Quarterly National Accounts, pp. 63-77. Paris, Dec. 1994. Eurostat. Department of Macro-Fiscal and Development Finance. (2022). Macroeconomic Situation Report: First Quarter Update FY 2022-23. [Online] Available at: https://www.mof.gov.bt/wpcontent/uploads/2023/02/MacroeconomiSituationReport06022023.pdf. Ferrara, L., Marsilli, C., & Ortega, J.-P. (2014). Forecasting growth during the great recession: is financial volatility the missing ingredient? Economic Modelling, 36, 44-50. Ferrara, L., Guegan, D. and Rakotomarolahy, P., 2010. GDP nowcasting with ragged-edge data: a semi-parametric modeling. Journal of Forecasting, 29(1-2), pp.186-199. Foroni, C., Marcellino, M., & Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society A, 178(1), 57-82. Giannone, D., Agrippino, S. M., & Modugno, M. (2013). Nowcasting China's Real GDP. CIRANO. Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS Regressions: Further Results and New Directions. Econometric Review, 26(1), 53–90. Gross National Happiness Centre Bhutan, 2016. Annual Report 2016. Gross National Happiness Centre Bhutan. Hyndman, R.J. & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package R. Journal of Statistical Software, 27(3), pp.1-22. doi: https://doi.org/10.18637/jss.v027.i03. Hopp, D. (2022). Benchmarking Econometric and Machine Learning Methodologies in Nowcasting. UNCTAD Research Paper No. 83. Higgins, P. (2014). GDPNow: A Model for GDP "Nowcasting". Federal Reserve Bank of Atlanta Working Paper Series, 2014 - 7. Koopmans, T.C. (1947). Measurement Without Theory. The Review of Economics and Statistics, 29(3). Kuzin, V., Marcellino, M., & Schumacher, C. (2011). MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area. International Journal of Forecasting, 27(2), 529-542. Lewis, D. J., Mertens, K., Stock, J. H., & Trivedi, M., 2020. Measuring Real Activity Using a Weekly Economic Index. New York City: Federal Reserve Bank of New York. Ministry of Finance. (2020). National Budget Report FY 2020-21. Bhutan. Marcellino, M. (1999). Some Consequences of Temporal Aggregation in Empirical Analysis. Journal of Business & Economic Statistics, 17(1), 129-136. Ministry of Finance. (2023). National Budget Report FY 2023-24. Bhutan. National Statistics Bureau. (2023). National Accounts Statistics. [Online] Available at: https://www.nsb.gov.bt/publications/national-account-report/Preis, T., & Moat, H. S. (2014). Adaptive Nowcasting of Influenza Outbreaks using Google Searches. R Soc Open Sci, doi: 10.1098/rsos.140095. Royal Monetary Authority of Bhutan. (2021). Annual Report 2021. Schumacher, C. (2016). A comparison of MIDAS and bridge equations. International Journal of Forecasting, 32(2), 257-270. Smith, G. (2018). Step Away from Stepwise. Journal of Big Data, 5, p.32. Stock, J. H. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147-162. Timmermann, A. (2006). Forecast Combinations. In G. Elliott, C.W.J. Granger, A. Timmermann (Eds.), Handbook of Economic Forecasting, Vol. 1. Elsevier, Amsterdam. Wallis, K.F., 1986. Forecasting with an econometric model: The ‘Ragged Edge’ problem. Journal of Forecasting, 5(1), pp.1-13. Zheng, I. Y., & Rossiter, J. (2006). Using Monthly Indicators to Predict Quarterly GDP. Bank of Canada Working Paper, 2006-26. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121380 |