Cobb, Marcus P A (2017): Joint Forecast Combination of Macroeconomic Aggregates and Their Components.
Preview |
PDF
MPRA_paper_76556.pdf Download (411kB) | Preview |
Abstract
This paper presents a framework that extends forecast combination to include an aggregate and its components in the same process. This is done with the objective of increasing aggregate forecasting accuracy by using relevant disaggregate information and increasing disaggregate forecasting accuracy by providing a binding context for the component’s forecasts. The method relies on acknowledging that underlying a composite index is a well defined structure and its outcome is a fully consistent forecasting scenario. This is particularly relevant for people that are interested in certain components or that have to provide support for a particular aggregate assessment. In an empirical application with GDP data from France, Germany and the United Kingdom we find that the outcome of the combination method shows equal aggregate accuracy to that of equivalent traditional combination methods and a disaggregate accuracy similar or better to that of the best single models.
Item Type: | MPRA Paper |
---|---|
Original Title: | Joint Forecast Combination of Macroeconomic Aggregates and Their Components |
Language: | English |
Keywords: | Bottom-up forecasting; Forecast combination; Hierarchical forecasting; Reconciling forecasts |
Subjects: | 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 > E27 - Forecasting and Simulation: Models and Applications E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications |
Item ID: | 76556 |
Depositing User: | Marcus Cobb |
Date Deposited: | 15 Feb 2017 17:18 |
Last Modified: | 06 Oct 2019 12:44 |
References: | Aiolfi, M. and C. A. Favero (2005). Model uncertainty, thick modelling and the predictability of stock returns. Journal of Forecasting 24(4), 233–254. Alessi, L., E. Ghysels, L. Onorante, R. Peach, and S. Potter (2014). Central bank macroeconomic forecasting during the global financial crisis: the european central bank and federal reserve bank of new york experiences. Journal of Business & Economic Statistics 32(4), 483–500. Banbura, M., D. Giannone, and L. Reichlin (2010). Large bayesian vector auto regressions. Journal of Applied Econometrics 25(1), 71–92. Bates, J. M. and C. W. Granger (1969). The combination of forecasts. Operations Research Quarterly 20, 451–468. Benalal, N., J. L. Diaz del Hoyo, B. Landau, M. Roma, and F. Skudelny (2004). To aggregate or not to aggregate? euro area inflation forecasting. Working Paper 374, European Central Bank. Brüggemann, R. and H. Lütkepohl (2013). Forecasting contemporaneous aggregates with stochastic aggregation weights. International Journal of Forecasting 29(1), 60– 68. Burriel, P. (2012). A real-time disaggregated forecasting model for the euro area gdp. Economic Bulletin, 93–103. Chauvet, M. and S. Potter (2013). Forecasting output. Handbook of Economic Forecasting 2(Part A), 141–194. Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International journal of forecasting 5(4), 559–583. Conflitti, C., C. De Mol, and D. Giannone (2015). Optimal combination of survey forecasts. International Journal of Forecasting 31(4), 1096–1103. Dalgaard, E. and C. Gysting (2004). An algorithm for balancing commodity-flow systems. Economic Systems Research 16(2), 169–190. Diebold, F. X. and J. A. Lopez (1996). Forecast evaluation and combination. in Maddala and Rao, eds., Handbook of Statistics (Elsevier, Amsterdam). Drechsel, K. and R. Scheufele (2013). Bottom-up or direct? forecasting german gdp in a data-rich environment. IWH Discussion Papers 7, Halle Institute for Economic Research. 40 ECB (2015). The ECB survey of professional forecasters 2nd Quarter of 2015. European Central Bank. April 2015. Eklund, J. and S. Karlsson (2007). Forecast combination and model averaging using predictive measures. Econometric Reviews 26(2-4), 329–363. Espasa, A. and I. Mayo-Burgos (2013). Forecasting aggregates and disaggregates with common features. International Journal of Forecasting 29(4), 718–732. Espasa, A., E. Senra, and R. Albacete (2002). Forecasting inflation in the european monetary union: A disaggregated approach by countries and by sectors. The European Journal of Finance 8(4), 402–421. Esteves, P. S. (2013). Direct vs bottom–up approach when forecasting gdp: Reconciling literature results with institutional practice. Economic Modelling 33, 416–420. Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2005). The generalized dynamic factor model. Journal of the American Statistical Association 100(471). Garcia, J. A. (2003). An introduction to the ecb’s survey of professional forecasters. ECB Occasional Paper (8). Giacomini, R. and C. W. Granger (2004). Aggregation of space-time processes. Journal of Econometrics 118(1), 7–26. Giannone, D., M. Lenza, D. Momferatou, and L. Onorante (2014). Short-term inflation projections: A bayesian vector autoregressive approach. International Journal of Forecasting 30(3), 635–644. Gomez, V. and A. Maravall (1996). Programs TRAMO and SEATS. Instructions for the User. Working paper, Banco de Espana. 9628, Research Department, Bank of Spain. Granger, C. W. (1987). Implications of aggregation with common factors. Econometric Theory 3(02), 208–222. Granger, C. W. and R. Ramanathan (1984). Improved methods of combining forecasts. Journal of Forecasting 3(2), 197–204. Hahn, E. and F. Skudelny (2008). Early estimates of euro area real gdp growth: a bottom up approach from the production side. Working Paper Series 0975, European Central Bank. Hansen, B. E. (2008). Least-squares forecast averaging. Journal of Econometrics 146(2), 342–350. 41 Hendry, D. F. and K. Hubrich (2011). Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate. Journal of Business & Economic Statistics 29(2). Hsiao, C. and S. K. Wan (2014). Is there an optimal forecast combination? Journal of Econometrics 178, 294–309. Hubrich, K. (2005). Forecasting euro area inflation: Does aggregating forecasts by hicp component improve forecast accuracy? International Journal of Forecasting 21(1), 119–136. Hyndman, R. J., R. A. Ahmed, G. Athanasopoulos, and H. L. Shang (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics & Data Analysis 55(9), 2579–2589. Kapetanios, G., V. Labhard, and S. Price (2008). Forecasting using bayesian and information-theoretic model averaging: an application to uk inflation. Journal of Business & Economic Statistics 26(1), 33–41. Lequiller, F. and D. Blades (2014). Understanding National Accounts: Second Edition. OECD Publishing. Lütkepohl, H. (1987). Forecasting aggregated vector ARMA processes, Volume 284. Springer Science & Business Media. Lütkepohl, H. (2011). Forecasting nonlinear aggregates and aggregates with timevarying weights. Jahrbücher für Nationalökonomie und Statistik, 107–133. Marcellino, M. (2008). A linear benchmark for forecasting gdp growth and inflation? Journal of Forecasting 27(4), 305–340. Marcellino, M., J. H. Stock, and M. W. Watson (2003). Macroeconomic forecasting in the euro area: Country specific versus area-wide information. European Economic Review 47(1), 1–18. Newbold, P. and D. I. Harvey (2002). Forecast combination and encompassing. M.P. Clements and D.F. Hendry, eds., A Companion to Economic Forecasting (Blackwell, Oxford), 268–283. OECD (2009). The situation of quarterly national accounts data transmission to the OECD. Working Party on National Accounts WPNA(2009)1. OECD. Perevalov, N. and P. Maier (2010). On the advantages of disaggregated data: insights from forecasting the us economy in a data-rich environment. Working Papers 10-10, Bank of Canada. 42 Ravazzolo, F. and S. P. Vahey (2014). Forecast densities for economic aggregates from disaggregate ensembles. Studies in Nonlinear Dynamics & Econometrics 18(4), 367–381. Rodrigues, J. F. (2014). A bayesian approach to the balancing of statistical economic data. Entropy 16(3), 1243–1271. Smith, J. and K. F. Wallis (2009). A simple explanation of the forecast combination puzzle. Oxford Bulletin of Economics and Statistics 71(3), 331–355. Stock, J. and M. Watson (1999). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. in R.F. Engle and H. White, eds., Festschrift in Honour of Clive Granger (Cambridge University Press, Cambridge) 1-44. Stock, J. H. and M. W. Watson (1998). Diffusion indexes. Working Paper 6702, NBER. Stock, J. H. and M. W. Watson (2015). Core inflation and trend inflation. Technical report, National Bureau of Economic Research. Stone, R. (1961). Input-output and national accounts. Organisation for European Economic Co-operation Paris. Stone, R. (1962). Multiple classifications in social accounting. Bulletin of the International Statistical Institute 39(3), 215–233. Stone, R., D. G. Champernowne, and J. E. Meade (1942). The precision of national income estimates. The Review of Economic Studies 9(2), 111–125. Timmermann, A. (2006). Forecast combinations. Handbook of economic forecasting 1, 135–196. Wei, X. and Y. Yang (2012). Robust forecast combinations. Journal of Econometrics 166(2), 224–236. Zellner, A. and J. Tobias (2000). A note on aggregation, disaggregation and forecasting performance. Journal of Forecasting 19(5), 457–465. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/76556 |