Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/59456
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dc.contributor.authorArmah, Nii Ayien_US
dc.contributor.authorSwanson, Norman R.en_US
dc.date.accessioned2011-06-14en_US
dc.date.accessioned2012-06-25T11:57:09Z-
dc.date.available2012-06-25T11:57:09Z-
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/10419/59456-
dc.description.abstractIn economics, common factors are often assumed to underlie the co-movements of a set of macroeconomic variables. For this reason, many authors have used estimated factors in the construction of prediction models. In this paper, we begin by surveying the extant literature on diffusion indexes. We then outline a number of approaches to the selection of factor proxies (observed variables that proxy unobserved estimated factors) using the statistics developed in Bai and Ng (2006a,b). Our approach to factor proxy selection is examined via a small Monte Carlo experiment, where evidence supporting our proposed methodology is presented, and via a large set of prediction experiments using the panel dataset of Stock and Watson (2005). One of our main empirical findings is that our smoothed approaches to factor proxy selection appear to yield predictions that are often superior not only to a benchmark factor model, but also to simple linear time series models which are generally difficult to beat in forecasting competitions. In some sense, by using our approach to predictive factor proxy selection, one is able to open up the black box often associated with factor analysis, and to identify actual variables that can serve as primitive building blocks for (prediction) models of a host of macroeconomic variables, and that can also serve are policy instruments, for example. Our findings suggest that important observable variables include: various S&P500 variables, including stock price indices and dividend series; a 1-year Treasury bond rate; various housing activity variables; industrial production; and exchange rates.en_US
dc.language.isoengen_US
dc.publisher|aDep. of Economics, Rutgers, the State Univ. of New Jersey |cNew Brunswick, NJen_US
dc.relation.ispartofseries|aWorking Papers, Department of Economics, Rutgers, the State University of New Jersey |x2011,05en_US
dc.subject.jelC22en_US
dc.subject.jelC33en_US
dc.subject.jelC51en_US
dc.subject.ddc330en_US
dc.subject.keyworddiffusion indexen_US
dc.subject.keywordfactoren_US
dc.subject.keywordforecasten_US
dc.subject.keywordmacroeconometricsen_US
dc.subject.keywordparameter estimation erroren_US
dc.subject.keywordproxyen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwTheorieen_US
dc.subject.stwSchätzungen_US
dc.subject.stwUSAen_US
dc.titleSeeing inside the black box: Using diffusion index methodology to construct factor proxies in largescale macroeconomic time series environmentsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn662027000en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

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