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on Econometric Time Series |
| By: | Sylvia Kaufmann (Study Center Gerzensee); Markus Pape (Ruhr-University Bochum) |
| Abstract: | Factor modelling extracts common information from a high-dimensional data set into few common components, where the latent factors usually explain a large share of data variation. Exploratory factor estimation induces sparsity into the loading matrix to associate units or series with those factors most strongly associated with them, eventually determining factor interpretation. We motivate geometrically under which circumstances it may be necessary to consider the existence of multiple sparse factor loading matrices with similar degrees of sparsity for a given data set. We propose two MCMC approaches for Bayesian inference and corresponding post-processing algorithms to uncover multiple sparse representations of the factor loadings matrix. We investigate both approaches in a simulation study. Applied to data on country-specific gross domestic product and U.S. price components series, we retrieve multiple sparse factor representations for each data set. Both approaches prove useful to discriminate between pervasive and weaker factors. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:szg:worpap:2304&r=ets |
| By: | M. Hashem Pesaran; Ron P. Smith |
| Abstract: | Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.14582&r=ets |