High-Dimensional Methods with Applications to Forecasting and Policy Evaluation
Paper Session
Saturday, Jan. 4, 2025 2:30 PM - 4:30 PM (PST)
- Chair: Jann Lorenz Spiess, Stanford University
Can Machines Learn Weak Signals?
Abstract
In high-dimensional regression scenarios with low signal-to-noise ratios, we assess the predictive performance of several prevalent machine learning algorithms. Theoretical insights show Ridge regression’s superiority in exploiting weak signals, surpassing a zero benchmark. In contrast, Lasso fails to exceed this baseline, indicating its learning limitations. Simulations reveal that Random Forest generally outperforms Gradient Boosted Regression Trees when signals are weak. Moreover, Neural Networks with l2-regularization excel in capturing nonlinear functions of weak signals. Our empirical analysis across six economic datasets suggests that the weakness of signals, not necessarily the absence of sparsity, may be Lasso’s major limitation in economic predictions.Forecasting GDP Growth Rate: A Large Panel Micro-Level Data Approach
Abstract
Economists and econometricians typically use aggregate macroeconomic and financial data for inflation prediction. However, aggregation often results in a loss of valuable information, diminishing key features like heterogeneity, interactions, nonlinearity, and structural breaks. We propose a novel microeconometric approach to inflation forecasting, making use of a large panel of individual stock prices. By employing machine learning algorithms, we can effectively exploit this micro-level information to achieve substantially more accurate inflation forecasts. Our findings highlight the advantages and potential of utilizing micro-level data for macro prediction, diverging from conventional macro-forecasting approaches that rely on aggregate data to forecast macro variables.Inference for CP Tensor Factor Model
Abstract
High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Specifically, our focus lies on the factor model that admits CP-type tensor decomposition, allowing for loading vectors that may not be orthogonal. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our estimator exhibits robustness to weak dependence among factors and weak correlation across different dimensions in the idiosyncratic shocks. We establish an inferential theory, demonstrating consistency and asymptotic normality under relaxed assumptions. Within a unified framework, we consider two tests for the number of factors in a tensor factor model and justify their consistency. Through a simulation study and two empirical applications featuring sorted portfolios and international trade flows, we illustrate the advantages of our proposed estimator over existing methodologies in the literature.JEL Classifications
- C45 - Neural Networks and Related Topics
- C53 - Forecasting and Prediction Methods; Simulation Methods