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Dynamic Factor Models (dynamic + factor_models)
Selected AbstractsBayesian analysis of dynamic factor models: an application to air pollution and mortality in São Paulo, BrazilENVIRONMETRICS, Issue 6 2008T. Sáfadi Abstract The Bayesian estimation of a dynamic factor model where the factors follow a multivariate autoregressive model is presented. We derive the posterior distributions for the parameters and the factors and use Monte Carlo methods to compute them. The model is applied to study the association between air pollution and mortality in the city of São Paulo, Brazil. Statistical analysis was performed through a Bayesian analysis of a dynamic factor model. The series considered were minimal temperature, relative humidity, air pollutant of PM10 and CO, mortality circulatory disease and mortality respiratory disease. We found a strong association between air pollutant (PM10), Humidity and mortality respiratory disease for the city of São Paulo. Copyright © 2007 John Wiley & Sons, Ltd. [source] How successful are dynamic factor models at forecasting output and inflation?JOURNAL OF FORECASTING, Issue 3 2008A meta-analytic approach Abstract This paper uses a meta-analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro-area output and for euro-area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well. Copyright © 2008 John Wiley & Sons, Ltd. [source] Factor forecasts for the UKJOURNAL OF FORECASTING, Issue 4 2005Michael J. Artis Abstract Data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. We argue that recent developments in the theory of dynamic factor models enable such large data sets to be summarized by relatively few estimated factors, which can then be used to improve forecast accuracy. In this paper we construct a large macroeconomic data set for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time-series models. We find that just six factors are sufficient to explain 50% of the variability of all the variables in the data set. These factors, which can be shown to be related to key variables in the economy, and their use leads to considerable improvements upon standard time-series benchmarks in terms of forecasting performance. Copyright © 2005 John Wiley & Sons, Ltd. [source] A parametric estimation method for dynamic factor models of large dimensionsJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2009George Kapetanios C32; C51; E52 Abstract., The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, because of the increased availability of large data sets. In this article we propose a new parametric methodology for estimating factors from large data sets based on state,space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also conduct a set of simulation experiments that show that our approach compares well with existing alternatives. [source] | ||||||||||