Nowcasting Slovak GDP by a Small Dynamic Factor Model

Tóth, Peter (2017): Nowcasting Slovak GDP by a Small Dynamic Factor Model. Forthcoming in: Ekonomický časopis / Journal of Economics , Vol. 65, No. 2 (2017)

Abstract

The aim of this paper is to estimate a small dynamic factor model (DFM) for nowcasting GDP growth in Slovakia. The model predicts the developments of real activity based on monthly indicators, such as sales, employment, employers’ health care contributions, export and foreign surveys. The forecast accuracy of the model prevails over naive models that ignore monthly data. This result holds especially on the shortest horizon of one quarter ahead and on the evaluation period including the crisis of 2008-2009. Thus we may conclude that our small DFM is a valuable indicator of business cycle turning points in Slovakia. Further, the model allows for frequent and automatic updates of the GDP forecast each time new monthly data becomes available. This makes it useful for institutions which monitor the developments of monthly indicators of real activity.

• Item Type: MPRA Paper
• Language: English
• Keywords: dynamic factor model, real activity, short-term forecasting
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E23 - Production; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications

References

Andreou, E., E. Ghysels and A. Kourtellos (2011), “Forecasting with Mixed-Frequency Data”, In: Clements, M. P. and D. Hendry (Eds.), The Oxford Handbook of Economic Forecasting, Oxford University Press.

Alvarez, R., M. Camacho and G. Pérez-Quirós (2012), “Finite Sample Performance of Small Versus Large Scale Dynamic Factor Models”, Working Papers 1204, Banco de España.

Angelini, E., G. Camba-Mendez, D. Giannone, L. Reichlin and G. Rünstler (2011), “Short-Term Forecasts of Euro Area GDP Growth”, The Econometrics Journal, vol. 14(1), p. C25–C44.

Arnoštová, K., D. Havrlant, L. Růžička and P. Tóth (2011), “Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators”, Czech Journal of Economics and Finance (Finance a úvěr), vol. 61(6), p. 566-583.

Auroba, S. B., F. X. Diebold and C. Scotti (2009), “Real-Time Measurement of Business Conditions”, Journal of Business and Economic Statistics, vol. 27(4), p. 417-427.

Baffigi, A., R. Golinelli and G. Parigi (2004), “Bridge Models to Forecast the Euro Area GDP”, International Journal of Forecasting, vol. 20(3), p. 447–460.

Bańbura, M., D. Giannone and L. Reichlin (2011), “Nowcasting”, In: Clements, M. P. and D. F. Hendry (Eds.), The Oxford Handbook of Economic Forecasting, Oxford University Press.

Bańbura, M., D. Giannone, M. Modugno, and L. Reichlin (2013), “Now-casting and the Real-Time Data Flow”, In: Elliott, G., C. Granger and A. Timmermann (Eds.), 2013. Handbook of Economic Forecasting, Elsevier, Edition 1, Volume 2, Part A, Chapter 4.

Bańbura, M., and M. Modugno (2014), “Maximum Likelihood Estimation of Factor Models on Data Sets With Arbitrary Pattern of Missing Data”, Journal of Applied Econometrics, vol. 29(1), p. 133-160.

Bańbura, M. and G. Rünstler (2011), “A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP”, International Journal of Forecasting, vol. 27(2), p. 333-346, April.

Barhoumi, K., O. Darné and L. Ferrara (2014), “Dynamic Factor Models: A Review of the Literature”, OECD Journal: Journal of Business Cycle Measurement and Analysis, vol. 2013(8), Issue 2, p. 73-107.

Bessonovs, A. (2015), “Suite of Latvia's GDP forecasting models”, Working Papers 2015/01, Latvijas Banka. Boivin, J. and S. Ng (2006), “Are More Data Always Better for Factor Analysis?”, Journal of Econometrics, vol. 132(1), p. 169-194.

Camacho, M. and G. Pérez-Quirós (2010), “Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth”, Journal of Applied Econometrics, vol. 25(4), p. 663-694.

Camacho, M. and G. Pérez-Quirós (2011), “Spain-Sting: Spain Short-Term Indicator of Growth”, The Manchester School, vol. 79(S1), p. 594-616.

Camacho, M., G. Pérez-Quirós, and P. Poncela (2013), “Short-Term Forecasting for Empirical Economists. A Survey of the Recently Proposed Algorithms,” Working Papers 1318, Banco de España.

Chamberlain, G. and M. Rothschild (1983), “Arbitrage, Factor Structure and Mean-Variance Analysis in Large Asset Markets”, Econometrica, 51(5), pp. 1305-1324.

Dempster, A., N. Laird and D. Rubin (1977), “Maximum Likelihood Estimation from Incomplete Data”, Journal of the Royal Statistical Society, Series B (Methodological), vol. 39(1), p. 1-38.

Diron, M. (2008), “Short-Term Forecasts of Euro Area Real GDP Growth: An Assessment of Real-Time Performance Based on Vintage Data”, Journal of Forecasting, vol. 27(5), p. 371-390.

Doz, C., D. Giannone and L. Reichlin (2011), “A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering”, Journal of Econometrics, vol. 164(1), p. 188-205.

Doz, C., D. Giannone and L. Reichlin (2012), “A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models”, Review of Economics and Statistics, vol. 94(4), p. 1014-1024.

Engle, R. F., and M. W. Watson (1981), “A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates”, Journal of the American Statistical Association, vol. 76(376), p. 774-781.

Feldkircher, M., F. Huber, J. Schreiner, M. Tirpák, P. Tóth and J. Wörz (2015), “Bridging the Information Gap: Small-Scale Nowcasting Models of GDP Growth for Selected CESEE Countries”, Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, p. 56-75.

Forni, M. and L. Reichlin (1998), “Let's Get Real: A Factor-Analytic Approach to Disaggregated Business Cycle Dynamics”, Review of Economic Studies, vol. 65(3), p. 453-473.

Forni, M., M. Hallin, M. Lippi and L. Reichlin (2000), “The Generalized Dynamic-Factor Model: Identification and Estimation”, Review of Economics and Statistics, vol. 82(4), p. 540-554.

Forni, M., M. Hallin, M. Lippi and L. Reichlin (2003), “Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?”, Journal of Monetary Economics, vol. 50(6), p. 1243-1255.

Forni, M., M. Hallin, M. Lippi and L. Reichlin (2004), “The Generalized Factor Model: Consistency and Rates”, Journal of Econometrics, vol. 119, p. 231-255.

Forni, M., M. Hallin, M. Lippi and L. Reichlin (2005), “The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting”, Journal of the American Statistical Association, vol. 100(1), p. 830-840.

Franta, M., D. Havrlant, M. Rusnák (2014), “Forecasting Czech GDP Using Mixed-Frequency Data Models”, Working Papers 2014/08, Czech National Bank, Research Department.

Giannone, D., L. Reichlin and D. H. Small (2008), “Nowcasting: The Real Time Informational Content of Macroeconomic Data”, Journal of Monetary Economics, vol. 55(4), p. 665-676.

Ghysels, E., P. Santa-Clara and R. Valkanov (2004), “The MIDASTouch: Mixed Data Sampling Regression Models”, Working Paper, University of California at Los Angeles, Anderson Graduate School of Management.

Ghysels, E., A. Sinko and R. Valkanov (2007), “MIDAS Regressions: Further Results and New Directions”, Econometric Reviews, vol. 26(1), p. 53-90.

Havrlant, D., P. Tóth and J. Wörz, (2016), “On the Optimal Number of Indicators – Nowcasting GDP Growth in CESEE”, Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, p. 54–72.

Huček, J., A. Karšay and M. Vávra (2015), “Short-term Forecasting of Real GDP Using Monthly Data”, Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.

Ingenito, R. and B. Trehan (1996), “Using monthly data to predict quarterly output”, Economic Review, Federal Reserve Bank of San Francisco, p. 3-11.

Kabundi, A., E. Nel and F. Ruch (2016), “Nowcasting Real GDP growth in South Africa”, Economic Research Southern Africa, Working Paper No.581., National Treasury of South Africa.

Kľúčik, M. and J. Juriová (2010), “Slowdown or Recession? Forecasts Based on Composite Leading Indicator”, Central European Journal of Economic Modelling and Econometrics, vol. 2(1), p. 17-36.

Liu, P., T. Matheson and R. Romeu (2011), “Real-Time Forecasts of Economic Activity for Latin America Economies”, IMF Working Paper 11/98.

Marcellino, M. and C. Schumacher (2010), “Factor-MIDAS for Nowcasting and Forecasting with Ragged Edge Data: A Model Comparison for German GDP”, Oxford Bulletin of Economics and Statistics, vol. 72(4), p. 518-550.

Mariano, R. and Y. Murasawa (2003), “A New Coincident Index of Business Cycles Based on Monthly and Quarterly Data”, Journal of Applied Econometrics, vol. 18(4), p. 427–443.

Modugno, M., B. Soybilgen and E. Yazgan (2016), “Nowcasting Turkish GDP and news decomposition” International Journal of Forecasting, vol. 32(4), p. 1369-1384.

Poncela, P. and E. Ruiz (2012), “More is not always better: back to the Kalman filter in dynamic factor models”, DES - Working Papers. Statistics and Econometrics, Universidad Carlos III de Madrid. Departamento de Estadística.

Porshakov, A., A. Ponomarenko and A. Sinyakov (2016), “Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model” Journal of the New Economic Association, vol. 30(2), p. 60-76.

Radovan, J. (2017), “Short-Term Forecasting of Slovenian GDP Using Monthly Information”, Bank of Slovenia Working Papers 1/2017.

Rünstler, G., K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua, K. Ruth and C. Van Nieuwenhuyze (2009), “Short-Term Forecasting of GDP Using Large Datasets. A Pseudo Real-Time Forecast Evaluation Exercise”, Journal of Forecasting, vol. 28(7), p. 595-611.

Rünstler, G. and F. Sédillot (2003), “Short-term estimates of euro area real GDP by means of monthly data” Working Paper Series 0276, European Central Bank. Rusnák, M. (2016), “Nowcasting Czech GDP in Real Time”, Czech National Bank Working Paper 6/2013.

Schumacher, C. and J. Breitung (2008), “Real-time Forecasting of German GDP Based on a Large Factor Model With Monthly and Quarterly Data”, International Journal of Forecasting, vol. 24(3), p. 386-398.

Shumway, R. H. and D. S. Stoffer (1982), “An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm”, Journal of Time Series Analysis, vol. 3(4), p. 253-264.

Stock, J. H. and M. W. Watson (1989), “New Indexes of Coincident and Leading Economic Indicators”, NBER Macroeconomics Annual, NBER.

Stock, J. H. and M. W. Watson (2002a), “Macroeconomic Forecasting Using Diffusion Indexes”, Journal of Business & Economic Statistics, vol. 20(2), 147–162.

Stock, J. H. and M. W. Watson (2002b), “Forecasting Using Principal Components From a Large Number of Predictors”, Journal of the American Statistical Association, vol. 97(1), p. 1167-1179.

Stock, J. H. and M. W. Watson (2011) “Dynamic factor models”, In: Clements, M.P., and D. F. Hendry (Eds.), The Oxford Handbook on Economic Forecasting. Oxford University Press.

Tóth, P. (2014a), “Model pre krátkodobý výhľad ekonomickej aktivity”, Manuál IFP č.7, Inštitút finančnej politiky, Ministerstvo financií Slovenskej republiky.

Tóth, P. (2014b), “Malý dynamický faktorový model na krátkodobé prognózovanie HDP. [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP.]”, MPRA Paper 63713, University Library of Munich, Germany.

Wang, J.-M., T.-M. Gao and R. McNown (2009), “Measuring Chinese Business Cycles with Dynamic Factor Models”, Journal of Asian Economics, vol. 20(2), March 2009, p. 89-97.

Watson, M. W. and R. F. Engle (1983), “Alternative Algorithms for the Estimation of Dynamic Factor, Mimic and Varying Coefficient Regression Models”, Journal of Econometrics, vol. 23(3), p. 385–400.

Yiu, M. S. and K. K. Chow (2011), “Nowcasting Chinese GDP: Information Content of Economic and Financial Data”, Working Papers 042011, Hong Kong Institute for Monetary Research.

Zadrozny, P. (1990), “Estimating a Multivariate ARMA Model with Mixed-Frequency Data: An Application to Forecasting US GNP at Monthly Intervals”, Working Paper Series 90-6, Federal Reserve Bank of Atlanta.