The paper develops coresets for panel data regression problems.
problem Efficiently summarize panel data regression problems.
method Introduced coreset construction for panel data regression problems using the Feldman-Langberg framework.
result Constructs coresets of size polynomial in 1/ε and number of parameters, independent of panel data size. The paper improves machine learning for heavy-tailed panel data.
problem Improving estimates for financial and economic data with fat tails.
method Sparse-group LASSO regularization and Fuk-Nagaev concentration inequality.
result Oracle inequalities for panel data estimators.
Paper uses machine learning for nowcasting corporate earnings from mixed-frequency data.
problem Predicting corporate earnings for a large cross-section of firms with different frequency data.
method Structured machine learning regressions with sparse-group LASSO regularization for panel data.
result Machine learning models outperform traditional methods in nowcasting corporate earnings.
Estimates heterogeneous treatment effects in panel data with a new method.
problem Estimating heterogeneous treatment effects in panel data with general treatment patterns.
method Partition observations into clusters with similar treatment effects using a regression tree, then estimate average treatment effects for each cluster.
result Our method achieves superior accuracy compared to alternative approaches.
Paper develops a new estimator for high-dimensional panel data with common shocks.
problem Cross-sectionally dependent errors driven by common shocks in high-dimensional panel data.
method Factor-augmented sparse-group LASSO estimator combining MIDAS aggregation with latent factors.
result The estimator outperforms standard LASSO for prediction and estimation in settings with cross-sectional dependence.
Adaptive PCR improves panel data analysis with uniform guarantees.
problem Adaptive data collection in panel data settings.
method Adapting PCR to online settings using martingale concentration.
result Time-uniform guarantees for adaptive PCR in panel data.
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogen…
Study analyzes GDP growth of CEE countries using time-varying coefficients.
problem Understanding GDP growth patterns of CEE countries post-integration.
method Panel regression with time-varying coefficients.
result Private debt plays a crucial role in economic growth.
R package xtdml uses DML for panel data models with fixed effects.
problem Estimating structural parameters in panel data models with fixed effects.
method Combines machine learning with statistical estimation for inference.
result Demonstrates improved performance in learning nuisance functions.
Develops DML for nonlinear panel data models with fixed effects.
problem Estimating causal effects in nonlinear panel data models with fixed effects.
method Double machine learning (DML) procedures for approximating nuisance functions.
result First-differencing yields the least constraints on fixed effects distribution.
Flexible variable selection handles missing data for better biomarker panels.
problem Identifying relevant features from incomplete data sets.
method Nonparametric variable selection combined with multiple imputation.
result Improved biomarker panels with higher classification and variable selection performance.
The paper tackles temporal coverage bias in financial panel data, proposing a structuring framework to correct for incomplete histories.
problem Incomplete histories of financial instruments lead to biased panel data.
method Formalizes the problem and proposes a coverage-aware structuring framework using structured metadata and an availability matrix.
result The framework reveals substantial distortions in return dynamics and volatility when naive temporal alignment is used.
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.
This paper provides estimation and inference methods for a conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable w…
This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…
Study finds WACC negatively impacts firm profitability in Bangladesh's food industry.
problem Determining the impact of Weighted Average Cost of Capital (WACC) on firm profitability.
method Fixed Effects Panel Regression Model using 12 food and allied industry companies from 2005-2019.
result WACC negatively correlates with firm profitability (ROA), significant relationship.
New method tests Granger non-causality in panel data with cross-sectional dependencies.
problem Testing Granger non-causality in panel data with cross-sectional dependencies.
method Proposes a new approach to aggregate p-values from panel members to test Granger non-causality, showing lower FDR.
result Our approach discovers true causal relations in panel data, unlike state-of-the-art methods.
Study on CEF discount in Bangladesh, finds size and maturity impact, turnover negative.
problem Exploring the discount puzzle in closed-end mutual funds in Bangladesh.
method Fixed effects panel regression with diagnostic tests.
result Fund size and maturity positively impact CEF discount, turnover negatively impacts.
A recent literature in econometrics models unobserved cross-sectional heterogeneity in panel data by assigning each cross-sectional unit a one-dimensional, discrete latent type. Such models have been shown to allow estimation and inference by regression clustering methods. This paper is motivated by the finding that th…
Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.
problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.
In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear …
New method for estimating heterogeneous treatment effects in panel data.
problem Estimating heterogeneous treatment effects in non-stationary, temporally dependent panel data.
method Proposes H1SL and H2SL, synthetic learners for panel data, based on existing non-panel data estimators.
result Established convergence rates for proposed estimators and demonstrated superior performance.
Study examines the impact of employment benefit costs on firm profitability.
problem Impact of employment benefit costs on firm profitability.
method Panel data regression analysis using E-Views.
result There is a significant positive relationship between employment benefit costs and firm profitability.
Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gau…
A new method for online prediction uncertainty quantification in non-exchangeable panel data.
problem Challenges in quantifying predictive uncertainty for non-exchangeable panel data.
method Online conformal prediction framework for non-exchangeable panel data, using similarity weights and adaptive miscoverage levels.
result Improves coverage on worst-covered target units through adaptive interval-width allocation.
Estimates mean and covariance for large, unbalanced stock returns panels.
problem Estimating mean and covariance in large, unbalanced panel data.
method Nonparametric, kernel-based joint estimator for conditional mean and covariance matrices.
result The idiosyncratic risk explains more than 75% of cross-sectional variance.
Study finds optimal board gender diversity for emissions performance.
problem Association between board gender diversity and emissions performance.
method Panel regressions, machine learning, explainable AI.
result Optimal board gender diversity for emissions performance is approximately 35%.
Proposes CoDEAL for estimating heterogeneous treatment effects in panel data models.
problem Estimating heterogeneous treatment effects in causal panel data models with covariate effects.
method Covariate-Adjusted Deep Causal Learning (CoDEAL) integrating neural networks and autoencoders.
result Establishes theoretical guarantees and demonstrates compelling performance in simulations and real data.
The 2007 Midwest Geometry Conference included a panel discussion devoted to open problems and the general direction of future research in fields related to the main themes of the conference. This paper summarizes the comments made during the panel discussion.
Improved causal inference with panel data using deep learning.
problem Causal inference challenges in social science with panel data.
method Adapted N-BEATS deep neural architecture for time series forecasting.
result SyNBEATS estimator outperforms existing methods in panel data settings.
Improved forecasting of investment dynamics across heterogeneous panels using a two-stage model.
problem Forecasting investment dynamics in heterogeneous panels with varying dynamics.
method Two-stage architecture: global pooled AR(1) for shared persistence, local models for residual dynamics.
result Significant improvement in out-of-sample R2 from 0.630 to 0.677, with a gain of 0.047. A new method for linear regression using feature graphs and hierarchical shrinkage.
problem Estimating robust parameters for linear regression models.
method Hierarchical Feature Regression (HFR) estimator that constructs a supervised feature graph to shrink parameters towards group targets.
result Demonstrates good predictive accuracy and versatility compared to other regularization techniques.
Method for factor analysis in short panels without assuming sphericity or Gaussianity.
problem Factor analysis in short panels without assuming sphericity or Gaussianity.
method Pseudo maximum likelihood method and asymptotically uniformly most powerful invariant test.
result Systematic risk explains a large part of cross-sectional total variance in bear markets but is not spanned by observed factors.
Gradient boosting algorithm for spatial panel models improves estimation in high-dimensional settings.
problem Estimation failure in high-dimensional spatial panel models.
method Model-based gradient boosting algorithm for spatial panel models with random and fixed effects.
result Feasibility and interpretability in both low- and high-dimensional settings.
The DAO Report led to a significant shift of ICO activity to Europe.
problem The impact of U.S. regulatory changes on global ICO activity.
method Analysis of a global dataset of ICOs from 2014 to 2021, focusing on the DAO Report's effects.
result A substantial and persistent reallocation of ICO activity to Europe following the DAO Report.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
We analyze linear factor models for asset pricing panels.
problem Characterizing cross-sectional and inter-temporal properties of returns and factors.
method Conditional means and covariances, review of Kozak and Nagel (2024) conditions.
result Low-dimensional factor portfolios can span efficient portfolios in unbalanced panels.
Micro-panel data are collected and analysed in many research and industry areas. Cluster analysis of micro-panel data is an unsupervised learning exploratory method identifying subgroup clusters in a data set which include homogeneous objects in terms of the development dynamics of monitored variables. The supply of cl…
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observati…
Proposes a robust EM algorithm for analyzing incomplete panel count data.
problem Missing reports in panel count data.
method Functional EM algorithm for non-parametric counting process mean function estimation.
result Robust to misspecification of Poisson process assumption and missing completely at random.
Optimal tensor PCA for estimating factors and loadings in high-dimensional panel data.
problem Estimating factors and loadings in high-dimensional panel data with non-negligible correlations.
method Tensor Principal Component Analysis (TPCA) for estimating factors and loadings in a tensor factor model.
result Simple TPCA is optimal for strong factors and can be improved for weak factors with alternating least-squares iterations.
Study finds dividend payout policy positively impacts firm profitability.
problem Determining the optimal dividend payout ratio and its effect on financial performance.
method Panel data analysis of 60 Indian listed firms over 10 years, using ROA as a proxy for profitability.
result Positive and significant relationship between dividend payout policy and firm performance.
Method estimates group structure in panel data using variance information.
problem Estimating group structure in panel data with unknown groups.
method Proposes a method to estimate unobserved groupings for panel data models using variance information.
result Superior performance compared to existing methods in simulations and empirical applications.
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets and share prices of 7,796 worldwide companies for the period 2004 through 2013. We develop a panel reg…
Develops privacy-preserving methods for longitudinal linear regression.
problem Protecting individual information in longitudinal data with privacy-preserving statistics.
method Proposes a user-level private regression estimator and a privatized covariance estimator for longitudinal linear regression under user-level differential privacy.
result Establishes theoretical guarantees for practical user-level differential privacy estimation and inference in longitudinal linear regression.
Simple method for estimating missing panel data entries with confidence intervals.
problem Estimating missing values in panel data with staggered adoption.
method Simple matrix algebra and singular value decomposition for estimation, with data-driven confidence intervals.
result Confidence intervals match non-asymptotic lower bounds, proving instance optimality.
Paper introduces Functional Effects Models to account for individual heterogeneity in panel data.
problem Accounting for preference heterogeneity in panel data with machine learning.
method Functional Effects Models using gradient boosting decision trees and deep neural networks to learn individual-specific preference parameters.
result Functional Effects Models outperform traditional models in learning inter-individual heterogeneity and predictive performance.
High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health ex…