Department of Economics and Business Economics

Diffusion Indexes with Sparse Loadings

Research output: Working paperResearch

Standard

Diffusion Indexes with Sparse Loadings. / Kristensen, Johannes Tang.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2013.

Research output: Working paperResearch

Harvard

Kristensen, JT 2013 'Diffusion Indexes with Sparse Loadings' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Kristensen, J. T. (2013). Diffusion Indexes with Sparse Loadings. Aarhus: Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2013-22

CBE

Kristensen JT. 2013. Diffusion Indexes with Sparse Loadings. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Kristensen, Johannes Tang Diffusion Indexes with Sparse Loadings. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2013-22). 2013., 40 p.

Vancouver

Kristensen JT. Diffusion Indexes with Sparse Loadings. Aarhus: Institut for Økonomi, Aarhus Universitet. 2013 Jul 5.

Author

Kristensen, Johannes Tang. / Diffusion Indexes with Sparse Loadings. Aarhus : Institut for Økonomi, Aarhus Universitet, 2013. (CREATES Research Papers; No. 2013-22).

Bibtex

@techreport{1fa0139f101c43b897214333e2127d0c,
title = "Diffusion Indexes with Sparse Loadings",
abstract = "The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad-hoc procedures or abandon the underlying theoretical factormodel. In this paper we will take a different approach to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC.",
keywords = "Forecasting, FactorsModels, Principal Components Analysis, LASSO",
author = "Kristensen, {Johannes Tang}",
year = "2013",
month = "7",
day = "5",
language = "English",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Diffusion Indexes with Sparse Loadings

AU - Kristensen, Johannes Tang

PY - 2013/7/5

Y1 - 2013/7/5

N2 - The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad-hoc procedures or abandon the underlying theoretical factormodel. In this paper we will take a different approach to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC.

AB - The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad-hoc procedures or abandon the underlying theoretical factormodel. In this paper we will take a different approach to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC.

KW - Forecasting, FactorsModels, Principal Components Analysis, LASSO

M3 - Working paper

BT - Diffusion Indexes with Sparse Loadings

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -