Title:Forecasting government bond yields with large Bayesian vector autoregressions Author(s):CARRIERO, Andrea; KAPETANIOS, George; MARCELLINO, MassimilianoDate:2012Citation:
- Journal of Banking and Finance, 2012, Vol. 36, No. 7, pp. 2026-2047
Type:ArticleAbstract:We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector ...
Title:Dating Business Cycles: a Methodological Contribution with an Application to the Euro Area Author(s):ARTIS, Michael J.; MARCELLINO, Massimiliano; PROIETTI, TommasoDate:2004Citation:
- Oxford Bulletin of Economics and Statistics, 2004, 66, 4, 537-565
Title:Factor Analysis in a Model with Rational Expectations Author(s):BEYER, Andreas; FARMER, Roger E. A.; HENRY, Jérôme; MARCELLINO, MassimilianoDate:2008-01-01Citation:
- Econometrics Journal, 2008, 11, 2, 271-286
Type:ArticleAbstract:DSGE models are characterized by the presence of expectations as explanatory variables. To use these models for policy evaluation, the econometrician must estimate the parameters of expectation terms. Standard estimation ...
Title:Forecasting Euro Area Variables with German Pre-EMU Data Author(s):BRUEGGEMANN, Ralf; LUETKEPOHL, Helmut; MARCELLINO, MassimilianoDate:2008Citation:
- Journal of Forecasting, 2008, 27, 6, 465-481.
Title:Pooling versus model selection for nowcasting GDP with many predictors : empirical evidence for six industrialized countries Author(s):KUZIN, Vladimir; MARCELLINO, Massimiliano; SCHUMACHER, ChristianDate:2013Citation:
- Journal of applied econometrics, 2013, Vol. 28, No. 3, pp. 392-411
Title:Unrestricted mixed data sampling (MIDAS) : MIDAS regressions with unrestricted lag polynomials Author(s):FORONI, Claudia; MARCELLINO, Massimiliano; SCHUMACHER, ChristianDate:2015Citation:
- Journal of the Royal Statistical Society : series A, statistics in society, 2015, Vol. 178, No. 1, pp. 57-82
Type:ArticleAbstract:Mixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low frequency variable by high frequency variables and their lags. When the difference in sampling frequencies between the ...
Title:Factor based identification-robust inference in IV regressions Author(s):KAPETANIOS, George; KHALAF, Lynda; MARCELLINO, MassimilianoDate:2016Citation:
- Journal of applied econometrics, 2016, Vol. 31, No. 5, pp. 821–842
Type:ArticleAbstract:Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade-offs resulting from weak or many instruments. ...
Title:Forecasting economic activity with targeted predictors Author(s):BULLIGAN, Guido; MARCELLINO, Massimiliano; VENDITTI, FabrizioDate:2015Citation:
- International journal of forecasting, 2015, Vol. 31, No. 1, pp. 188-206
Type:ArticleAbstract:In this paper we explore the forecasting performances of methods based on a pre-selection of monthly indicators from large panels of time series. After a preliminary data reduction step based on different shrinkage techniques, ...
Title:Forecasting with a DSGE model of a small open economy within the monetary union Author(s):MARCELLINO, Massimiliano; RYCHALOVSKA, YuliyaDate:2014Citation:
- Journal of forecasting, 2014, Vol. 33, No. 5, pp. 315-338
Type:ArticleAbstract:In this paper we lay out a two-region dynamic stochastic general equilibrium (DSGE) model of an open economy within the European Monetary Union. The model, which is built in the New Keynesian tradition, contains real and ...
Title:Regime switches in the risk-return trade-off Author(s):GHYSELS, Eric; GUERIN, Pierre; MARCELLINO, MassimilianoDate:2014Citation:
- Journal of empirical finance, 2014, Vol. 28, pp. 118-138
Type:ArticleAbstract:This paper deals with the estimation of the risk–return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk–return relation through a Markov-switching specification. We ...