arXiv subjects

Sylvia Klosin

Publications and source records attributed to Sylvia Klosin.

5 recordsLinked to original sources

arXiv2026

Bagged Polynomial Regression and Neural Networks

Climate and environmental applications increasingly rely on high-dimensional prediction from remote sensing and other scientific data. Neural networks (NN) can deliver strong accuracy in these settings, but they are often hard to audit and hard to align with domain knowledge. As an alternative, we propose bagged polynomial regression with random projections (BPR), an econometrics-native ensemble that averages many regularized low-degree polynomial models fit on randomly selected covariate groups. We provide novel finite-sample and asymptotic risk bounds and show how covariate partitioning can improve rates for smooth target functions by controlling dictionary basis growth. Rate improvements may be particularly relevant for the estimation of marginal effects. In an application to satellite-based crop classification using optical and radar imagery, BPR matches NN accuracy while remaining straightforward to diagnose. We provide practical transparency tools, coefficient summaries and partial-dependence diagnostics, that show BPR captures intuitive feature relationships that NNs do not.

stat.ML↗

arXiv2025

Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application

Economists often estimate continuous treatment effects in panel data using linear two-way fixed effects models (TWFE). When the treatment-outcome relationship is nonlinear, TWFE is misspecifed and potentially biased for the average partial derivative (APD). We develop an automatic double/de-biased machine learning (ADML) estimator that is consistent for the population APD while allowing additive unit fixed effects, nonlinearities, and high dimensional heterogeneity. We prove asymptotic normality and add two refinements - optimization based de-biasing and analytic derivatives - that reduce bias and remove numerical approximation error. Simulations show that the proposed method outperforms high order polynomial OLS and standard ML estimators. Our estimator leads to significantly larger (by 50%), but equally precise, estimates of the effect of extreme heat on corn yield compared to standard linear models.

econ.EM↗

arXiv2024

Dynamic Biases of Static Panel Data Estimators

This paper identifies an important bias - termed dynamic bias - in fixed effects panel estimators that arises when dynamic feedback is ignored in the estimating equation. Dynamic feedback occurs if past outcomes impact current outcomes, a feature of many settings ranging from economic growth to agricultural and labor markets. When estimating equations omit past outcomes, dynamic bias can lead to significantly inaccurate treatment effect estimates, even with randomly assigned treatments. This dynamic bias in simulations is larger than Nickell bias. I show that dynamic bias stems from the estimation of fixed effects, as their estimation generates confounding in the data. To recover consistent treatment effects, I develop a flexible estimator that provides fixed-T bias correction. I apply this approach to study the impact of temperature shocks on GDP, a canonical example where economic theory points to an important feedback from past to future outcomes. Accounting for dynamic bias lowers the estimated effects of higher yearly temperatures on GDP growth by 10% and GDP levels by 120%.

econ.EM↗

arXiv2022

The long-term effect of childhood exposure to technology using surrogates

We study how childhood exposure to technology at ages 5-15 via the occupation of the parents affects the ability to climb the social ladder in terms of income at ages 45-49 using the Danish micro data from years 1961-2019. Our measure of technology exposure covers the degree to which using computers (hardware and software) is required to perform an occupation, and it is created by merging occupational codes with detailed data from O*NET. The challenge in estimating this effect is that long-term outcome is observed over a different time horizon than our treatment of interest. We therefore adapt the surrogate index methodology, linking the effect of our childhood treatment on intermediate surrogates, such as income and education at ages 25-29, to the effect on adulthood income. We estimate that a one standard error increase in exposure to technology increases the income rank by 2\%-points, which is economically and statistically significant and robust to cluster-correlation within families. The derived policy recommendation is to update the educational curriculum to expose children to computers to a higher degree, which may then act as a social leveler.

econ.EM↗

arXiv2021

Automatic Double Machine Learning for Continuous Treatment Effects

In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods.

econ.EM↗