Cobb, Marcus P A (2017): Aggregate Density Forecasting from Disaggregate Components Using Large VARs.
Download (845kB) | Preview
When it comes to point forecasting there is a considerable amount of literature that deals with ways of using disaggregate information to improve aggregate accuracy. This includes examining whether producing aggregate forecasts as the sum of the component’s forecasts is better than alternative direct methods. On the contrary, the scope for producing density forecasts based on disaggregate components remains relatively unexplored. This research extends the bottom-up approach to density forecasting by using the methodology of large Bayesian VARs to estimate the multivariate process and produce the aggregate forecasts. Different specifications including both fixed and time-varying parameter VARs and allowing for stochastic volatility are considered. The empirical application with GDP and CPI data for Germany, France and UK shows that VARs can produce well calibrated aggregate forecasts that are similar or more accurate than the aggregate univariate benchmarks.
|Item Type:||MPRA Paper|
|Original Title:||Aggregate Density Forecasting from Disaggregate Components Using Large VARs|
|Keywords:||Density Forecasting; Bottom-up forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
|Depositing User:||Marcus Cobb|
|Date Deposited:||15 Feb 2017 17:12|
|Last Modified:||15 Feb 2017 17:13|
Bache, I. W., J. Mitchell, F. Ravazzolo, and S. P. Vahey (2010). Macro-modelling with many models. Twenty Years of Inflation Targeting: Lessons Learned and Future Prospects. Chapter 16.
Banbura, M., D. Giannone, and L. Reichlin (2010). Large bayesian vector auto regressions. Journal of Applied Econometrics 25(1), 71–92.
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.
Berkowitz, J. (2001). Testing density forecasts, with applications to risk management. Journal of Business & Economic Statistics 19(4), 465–474.
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.
Carriero, A., T. E. Clark, and M. Marcellino (2015). Bayesian vars: specification choices and forecast accuracy. Journal of Applied Econometrics 30(1), 46–73.
Carriero, A., G. Kapetanios, and M. Marcellino (2009). Forecasting exchange rates with a large bayesian var. International Journal of Forecasting 25(2), 400–417.
Diebold, F. X., T. A. Gunther, and A. S. Tay (1998). Evaluating density forecasts with applications to financial risk management. International Economic Review 39, 863–883.
Diebold, F. X. and R. S. Mariano (1995). Comparing predictive accuracy. Journal of Business & economic statistics (13).
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.
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.
Geweke, J. and G. Amisano (2010, April). Comparing and evaluating Bayesian predictive distributions of asset returns. International Journal of Forecasting 26(2), 216–230.
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.
Gneiting, T., F. Balabdaoui, and A. E. Raftery (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69(2), 243–268.
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.
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).
Hubrich, K. (2005). Forecasting euro area inflation: Does aggregating forecasts by hicp component improve forecast accuracy? International Journal of Forecasting 21(1), 119–136.
Koop, G. and D. Korobilis (2013). Large time-varying parameter vars. Journal of Econometrics 177(2), 185–198.
Koop, G. M. (2013). Forecasting with medium and large bayesian vars. Journal of Applied Econometrics 28(2), 177–203.
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., 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.
Mitchell, J. and S. G. Hall (2005). Evaluating, comparing and combining density forecasts using the klic with an application to the Bank of England and niesr fancharts of inflation. Oxford bulletin of economics and statistics 67(s1), 995–1033.
Mitchell, J. and K. F. Wallis (2011). Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness. Journal of Applied Econometrics 26(6), 1023–1040.
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.
Raftery, A. E., M. Kárny, and P. Ettler (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics 52(1), 52–66.
Ravazzolo, F. and S. P. Vahey (2014). Forecast densities for economic aggregates from disaggregate ensembles. Studies in Nonlinear Dynamics & Econometrics 18(4), 367–381.
Zellner, A. and J. Tobias (2000). A note on aggregation, disaggregation and forecasting performance. Journal of Forecasting 19(5), 457–465.