MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area

International Journal of Forecasting - Tập 27 - Trang 529-542 - 2011
Vladimir Kuzin1, Massimiliano Marcellino2, Christian Schumacher3
1DIW Berlin, Germany
2European University Institute, Università Bocconi and CEPR, Italy
3Deutsche Bundesbank, Germany

Tài liệu tham khảo

Andreou, E., Ghysels, E., & Kourtellos, A. Regression models with mixed sampling frequencies. Journal of Econometrics (in press). Andreou, E., Ghysels, E., & Kourtellos, A. (2009). Should macroeconomic forecasters look at daily financial data? University of North Carolina. Mimeo. Banerjee, 2005, Leading indicators for euro area inflation and GDP growth, Oxford Bulletin of Economics and Statistics, 67, 785, 10.1111/j.1468-0084.2005.00141.x Bernanke, 2003, Monetary policy in a data-rich environment, Journal of Monetary Economics, 50, 525, 10.1016/S0304-3932(03)00024-2 Bhansali, 2002, Multi-step forecasting, 206 Chevillon, 2005, Non-parametric direct multi-step estimation for forecasting economic processes, International Journal of Forecasting, 21, 201, 10.1016/j.ijforecast.2004.08.004 Clements, 2008, Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States, Journal of Business and Economic Statistics, 26, 546, 10.1198/073500108000000015 Clements, M. P., & Galvão, A. Forecasting US output growth using leading indicators: An appraisal using MIDAS models. Journal of Applied Econometrics. (in press). Ghysels, E., Rubia, A., & Valkanov, R. 2009. Multi-period forecasts of volatility: direct, iterated, and mixed-data approaches. EFA 2009 Bergen Meetings Paper. Available at SSRN: http://ssrn.com/abstract=1344742. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS touch: mixed data sampling regression models. University of North Carolina. Mimeo. Ghysels, 2005, There is a risk-return after all, Journal of Financial Economics, 76, 509, 10.1016/j.jfineco.2004.03.008 Ghysels, 2006, Predicting volatility: getting the most out of return data sampled at different frequencies, Journal of Econometrics, 131, 59, 10.1016/j.jeconom.2005.01.004 Ghysels, 2007, MIDAS regressions: further results and new directions, Econometric Reviews, 26, 53, 10.1080/07474930600972467 Ghysels, E., & Valkanov, R. (2006). Linear time series processes with mixed data sampling and MIDAS regression models. University of North Carolina. Mimeo. Ghysels, 2009, Forecasting professional forecasters, Journal of Business and Economic Statistics, 27, 504, 10.1198/jbes.2009.06044 Giannone, 2008, Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases, Journal of Monetary Economics, 55, 665, 10.1016/j.jmoneco.2008.05.010 Kuzin, V., Marcellino, M., & Schumacher, C. (2009). Pooling versus model selection for nowcasting with many predictors: an application to German GDP. Deutsche Bundesbank Discussion Paper, Series 1: Economic Studies, 03/2009. Marcellino, M., & Musso, A. (2010). Real time estimates of the euro area output gap: reliability and forecasting performance. ECB Working Paper 1157. Marcellino, M., & Schumacher, C. Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP. Oxford Bulletin of Economics and Statistics (in press). Marcellino, 2006, A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series, Journal of Econometrics, 135, 499, 10.1016/j.jeconom.2005.07.020 Mariano, 2003, A new coincident index of business cycles based on monthly and quarterly series, Journal of Applied Econometrics, 18, 427, 10.1002/jae.695 Mariano, 2010, A coincident index, common factors, and monthly real GDP, Oxford Bulletin of Economics and Statistics, 72, 27, 10.1111/j.1468-0084.2009.00567.x Mittnik, 2005, Forecasting German GDP at monthly frequency using monthly IFO business conditions data, 19 Schumacher, 2008, Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data, International Journal of Forecasting, 24, 368, 10.1016/j.ijforecast.2008.03.008 Timmermann, 2006, Forecast combinations, 135, 10.1016/S1574-0706(05)01004-9 Wohlrabe, K. (2009). Forecasting with mixed-frequency time series models. Ph.D. dissertation, University Munich. Zadrozny, 1988, Gaussian-likelihood of continuous-time ARMAX models when data are stocks and flows at different frequencies, Econometric Theory, 4, 108, 10.1017/S0266466600011890