Macroeconomic forecasting using penalized regression methods
We study the suitability of applying lasso-type penalized regression techniques to macroe-
conomic forecasting with high-dimensional datasets. We consider the performances of lasso …
conomic forecasting with high-dimensional datasets. We consider the performances of lasso …
[PDF][PDF] Identification through sparsity in factor models: The ℓ1-rotation criterion
S Freyaldenhoven - 2022 - simonfreyaldenhoven.github.io
Linear factor models are generally not identified. We provide sufficient conditions for
identification: under a sparsity assumption, we can estimate the individual loading vectors …
identification: under a sparsity assumption, we can estimate the individual loading vectors …
Mixed-frequency approaches to nowcasting GDP: An application to Japan
K Chikamatsu, N Hirakata, Y Kido, K Otaka - Japan and the World Economy, 2021 - Elsevier
In this paper, we discuss the approaches to nowcasting Japan's GDP quarterly growth rates,
comparing a variety of mixed frequency approaches including a bridge equation approach …
comparing a variety of mixed frequency approaches including a bridge equation approach …
[图书][B] Nowcasting Japanese GDPs
K Chikamatsu, N Hirakata, Y Kido, K Otaka - 2018 - boj.or.jp
In this paper, we discuss the approaches to nowcasting Japanese GDPs, namely
preliminary quarterly GDP estimates and revised annual GDP estimates. First, we look at …
preliminary quarterly GDP estimates and revised annual GDP estimates. First, we look at …
The sparse dynamic factor model: a regularised quasi-maximum likelihood approach
The concepts of sparsity, and regularised estimation, have proven useful in many high-
dimensional statistical applications. Dynamic factor models (DFMs) provide a parsimonious …
dimensional statistical applications. Dynamic factor models (DFMs) provide a parsimonious …
Comparing the forecast performance of advanced statistical and machine learning techniques using huge big data: evidence from Monte Carlo experiments
This research compares factor models based on principal component analysis (PCA) and
partial least squares (PLS) with Autometrics, elastic smoothly clipped absolute deviation (E …
partial least squares (PLS) with Autometrics, elastic smoothly clipped absolute deviation (E …
Identifying and interpreting the factors in factor models via sparsity: Different approaches
T Despois, C Doz - Journal of Applied Econometrics, 2023 - Wiley Online Library
This paper considers different approaches for identifying the factor structure and interpreting
the factors without imposing their interpretation via restrictions: sparse PCA and factor …
the factors without imposing their interpretation via restrictions: sparse PCA and factor …
sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings
L Mosley, TS Chan, A Gibberd - arXiv preprint arXiv:2303.14125, 2023 - arxiv.org
sparseDFM is an R package for the implementation of popular estimation methods for
dynamic factor models (DFMs) including the novel Sparse DFM approach of Mosley et …
dynamic factor models (DFMs) including the novel Sparse DFM approach of Mosley et …
Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US
F Haase, M Neuenkirch - International Journal of Forecasting, 2023 - Elsevier
The empirical literature of stock market predictability mainly suffers from model uncertainty
and parameter instability. To meet this challenge, we propose a novel approach that …
and parameter instability. To meet this challenge, we propose a novel approach that …