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Found 10 papers, 3 papers with code
Functional Partial Least-Squares: Optimal Rates and Adaptation
We consider the functional linear regression model with a scalar response and a Hilbert space-valued predictor, a well-known ill-posed inverse problem.
Econometrics of Machine Learning Methods in Economic Forecasting
This paper surveys the recent advances in machine learning method for economic forecasting.
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.
Tensor Principal Component Analysis
A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data.
Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice
We study the binary choice problem in a data-rich environment with asymmetric loss functions.
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 mixed-frequency IV regression
This paper introduces a high-dimensional linear IV regression for the data sampled at mixed 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.
Is completeness necessary? Estimation in nonidentified linear models
We show that estimators based on spectral regularization converge to the best approximation of a structural parameter in a class of nonidentified linear ill-posed inverse models.
