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Found 7 papers, 4 papers with code
Econometrics of Machine Learning Methods in Economic Forecasting
This paper surveys the recent advances in machine learning method for economic forecasting.
Tuning-free testing of factor regression against factor-augmented sparse alternatives
This study introduces a bootstrap test of the validity of factor regression within a high-dimensional factor-augmented sparse regression model that integrates factor and sparse regression techniques.
Panel Data Nowcasting: The Case of Price-Earnings Ratios
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies.
Sparse plus dense MIDAS regressions and nowcasting during the COVID pandemic
The common practice for GDP nowcasting in a data-rich environment is to employ either sparse regression using LASSO-type regularization or a dense approach based on factor models or ridge regression, which differ in the way they extract information from high-dimensional datasets.
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies.
Machine Learning Time Series Regressions with an Application to Nowcasting
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies.
High-Dimensional Granger Causality Tests with an Application to VIX and News
We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals.
