[
Aastveit, K.A., Foroni, C. and Ravazzolo, F. 2016. Density forecasts with MIDAS models.Journal of Applied Econometrics, 32(4): 783–801.10.1002/jae.2545
]Search in Google Scholar
[
Abdić, A., Resić, E., Abdić, A., and Rovčanin, A. 2020. Nowcasting GDP of Bosnia and Herzegovina: a comparison of forecast accuracy models. South East European Journal of Economics and Business,15 (2): 1-14.10.2478/jeb-2020-0011
]Search in Google Scholar
[
Andreou, E., Ghysels, E. and Kourtellos, A. 2013. Should Macroeconomic Forecasters Use Daily Financial Data and How?.Journal of Business & Economic Statistics, 31(2):240-251.10.1080/07350015.2013.767199
]Search in Google Scholar
[
Andreou, E., Gagliardini, P., Ghysels, E. and Rubin, M. 2019. Inference in Group Factor Models With an Application to Mixed-FrequencyData.Econometrica, 10.3982/ECTA14690, 87(4):1267-1305.10.3982/ECTA14690
]Search in Google Scholar
[
Bai, J., Ghysels, E. and Wright, J.H. 2013. State Space Models and MIDAS Regressions.Econometric Reviews, 32(7):779-813.10.1080/07474938.2012.690675
]Search in Google Scholar
[
Bańbura, M., Giannone, D., Modugno, M. and Reichlin, L. 2013. Now-casting and the real-time data flow, Working Paper SeriesNo. 1564, European Central Bank.10.2139/ssrn.2284274
]Search in Google Scholar
[
Barsoum, F., and Stankiewicz, S. 2013. Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes. Working Paper Series of the Department of Economics 2013-10, University of Konstanz.
]Search in Google Scholar
[
Chiu, C.W., Eraker, B., Foerster, A.T., Kim, T.B. and Seoane, H.D. 2011. Estimating VAR’s Sampled at Mixed or Irregular Spaced Frequencies: A bayesian approach, Research Working Paper RW 11-11, Federal Reserve Bank of Kansas City.
]Search in Google Scholar
[
Clements, M. and Galvão, A. 2008. Macroeconomic forecasting with mixed frequency data: forecasting output growth in the United States. Journal of Business and Economic Statistics, 26:546–554.10.1198/073500108000000015
]Search in Google Scholar
[
Clements, M. and Galvão, A. 2009. Forecasting US Output Growth Using Leading Indicators: An Appraisal Using MIDAS Models. Journal of Applied Econometrics,24(7): 1187–1206.10.1002/jae.1075
]Search in Google Scholar
[
Diebold, F. X. and Mariano,R. S. 1995. Comparing predictive accuracy.Journal of Business & Economic Statistics, 13:253–263.10.1080/07350015.1995.10524599
]Search in Google Scholar
[
Drechsel, K. and Scheufele, R. 2012. Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment, Working Papers, No 2012-16, Swiss National Bank.
]Search in Google Scholar
[
Duarte, C. 2014. Autoregressive augmentation of MIDAS regressions. Working Papers w201401, Banco de Portugal, Economics and Research Department.
]Search in Google Scholar
[
Ferrara, L., Marsilli, C. and Ortega, J-P. 2014. Forecasting Growth During the Great Recession: Is Financial Volatility the Missing Ingredient?.Economic Modelling, 36: 44-50,Working Paper No. 454, Banque de France.10.1016/j.econmod.2013.08.042
]Search in Google Scholar
[
Foroni, C. and Marcellino, M. 2013. A survey of econometric methods for mixed-frequency data. Department of Economics 2013/02. European University Institute.10.2139/ssrn.2268912
]Search in Google Scholar
[
Foroni, C. and Marcellino, M. 2014. A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates.International Journal of Forecasting, 30(3):554-568.10.1016/j.ijforecast.2013.01.010
]Search in Google Scholar
[
Foroni, C., Marcellino, M. and Schumacher, C. 2011. U-MIDAS: MIDAS regressions with unrestricted lag polynomials. Discussion Paper Series 1: Economic Studies 35, Deutsche Bundesbank, Research Centre.10.2139/ssrn.2785452
]Search in Google Scholar
[
Foroni, C., Marcellino, M. and Stevanovic, D. 2018. Mixed Frequency Models with MA Components. Deutsche Bundesbank Discussion Paper No. 02/2018.10.2139/ssrn.3289564
]Search in Google Scholar
[
Ghysels, E., Santa-Clara, P. and Valkanov, R. 2004. The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO Working Papers 2004s-20, CIRANO.
]Search in Google Scholar
[
Ghysels, E., Santa-Clara, P. and Valkanov, R. 2005. There is a risk-return trade-off after all. Journal of Financial Economics, 76:509–548. doi:10.1016/j.jfineco.2004.03.00810.1016/j.jfineco.2004.03.008
]Search in Google Scholar
[
Ghysels, E., Santa-Clara, P. and Valkanov, R. 2006. Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131:59–95.10.1016/j.jeconom.2005.01.004
]Search in Google Scholar
[
Kapetanios, G., Marcellino, M. and Petrova, K. 2018. Analysis of the most recent modelling techniques for big data with particular attention to Bayesian ones. Eurostat Statistical Working Papers, 2018 edition.
]Search in Google Scholar
[
Kuzin, V., Marcellino, M. and Schumacher, C. 2011. MIDAS vs. Mixed frequency VAR: Nowcasting GDP in the euro-area. International Journal of Forecasting, 27(2):529–542.10.1016/j.ijforecast.2010.02.006
]Search in Google Scholar
[
Leboeuf, M. and Morel, L. 2014. Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions. Discussion Papers, Bank of Canada.
]Search in Google Scholar
[
Marsilli, C. 2014. Mixed-Frequency Modeling and Economic Forecasting. Economics and Finance. Université de Franche-Comté, 2014. English. NNT: 2014BESA2023. tel-01645421.
]Search in Google Scholar
[
Mikosch, H. and Neuwirth, S. 2015. Real-time forecasting with a MIDAS VAR. BOFIT Discussion Papers 13/2015, Bank of Finland, Institute for Economies in Transition.10.2139/ssrn.2588905
]Search in Google Scholar
[
Mittnik, S. and Zadrozny, P.A. 2004. Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data. CESifo Working Paper Series No. 1203, CESifo Group Munich.10.2139/ssrn.556075
]Search in Google Scholar
[
Mariano, R. and Murasawa, Y. 2010. A Coincident Index, Common Factors, and Monthly Real GDP, Oxford Bulletin of Economics and Statistics, 72(1):27-46.10.1111/j.1468-0084.2009.00567.x
]Search in Google Scholar
[
Schorfheide, F. and Song, D. 2015. Real-Time Forecasting With a Mixed-Frequency VAR.Journal of Business & Economic Statistics, 33(3):366–380.10.1080/07350015.2014.954707
]Search in Google Scholar