2 citations · 2 across the 4 of their papers we have counts for
4 papers
Combining Forecasts under Structural Breaks Using Graphical LASSO
Tae-Hwy Lee, Ekaterina Seregina
In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different f…
Inferential Theory for Granular Instrumental Variables in High Dimensions
Saman Banafti, Tae-Hwy Lee
The Granular Instrumental Variables (GIV) methodology exploits panels with factor error structures to construct instruments to estimate structural time series models with endogenei…
Learning from Forecast Errors: A New Approach to Forecast Combinations
Tae-Hwy Lee, Ekaterina Seregina
Forecasters often use common information and hence make common mistakes. We propose a new approach, Factor Graphical Model (FGM), to forecast combinations that separates idiosyncra…
Optimal Portfolio Using Factor Graphical Lasso
Tae-Hwy Lee, Ekaterina Seregina
Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for a portfolio allocation problem. The assumption…