Double Machine Learning for Partially Linear Models with Endogeneity and Multivariate Sample Selection
Bogdan Potanin (HSE University) has published an article titled "Double machine learning for a partially linear model with endogenous treatments and multivariate sample selection" in the journal Statistics and Computing. The paper proposes double machine learning (DML) estimators for a partially linear model with endogenous treatments and multivariate sample selection. Asymptotic normality of the estimators is proved under mild regularity conditions, and their finite sample properties are studied on simulated data. The proposed approach is extended to the case of endogenous switching and is illustrated by estimating the Engel curve using RLMS-HSE data.
Confidence Intervals for Bubble Onset and Recovery Dates
Eiji Kurozumi and Anton Skrobotov have published an article titled "Confidence Sets for the Emergence, Collapse, and Recovery Dates of a Bubble" in the journal Econometric Reviews. The paper proposes, for the first time, a method for constructing statistically justified confidence sets for the dates of financial bubble emergence, collapse, and recovery. The authors employ test inversion procedures for breakpoint location, which allows for controlled coverage rates with reasonable interval lengths. The effectiveness of the approach is demonstrated using Japanese Nikkei 225 index data.
Professor Marco Barassi at the Centre for Big Data in Economics and Finance at HSE University
The Centre for Big Data in Economics and Finance at HSE University hosted Professor Marco Barassi of the University of Birmingham. During his visit he delivered a plenary talk at a special session of the 8th Applied Econometrics conference and also presented a paper at the International Research Seminar of the Faculty of Economic Sciences.
Art market pricing in emerging economies
Taisia Pimenova, Valeria Kolycheva, Alexander Semenov, and Dmitry Grigoriev have published the article «Art pricing in the emerging markets: An empirical analysis» in Emerging Markets Review . The paper presents an empirical analysis of the impact of public sentiment expressed on social media on artwork prices in emerging markets. Using a dataset of 3,282 paintings by Russian and Chinese artists, the authors demonstrate that both positive and negative public opinion significantly affect prices, while also identifying the moderating roles of collectors' investment intentions and geopolitical risk.
Bayesian Adaptive Sparse Copula: Tackling the Curse of Dimensionality in Multivariate Data
Martin Burda and Artem Prokhorov have published the article «Bayesian Adaptive Sparse Copula» in the Journal of Computational and Graphical Statistics. The paper introduces a new approach to Bayesian nonparametric estimation of multivariate densities. The authors propose a random Bernstein polynomial prior augmented with a spike-and-slab shrinkage structure, which preserves the advantages of multiscale decision tree methods while alleviating the curse of dimensionality.
Treatment effects under endogeneity and non-random selection: Estimating the impact of stress on addictive substance use
Anastasia Gergenreter has published an article «Estimation of treatment effects on ordinal variables in multivariate ordered choice models» in Applied Econometrics . The study offers a fresh perspective on estimating treatment effects for ordinal outcomes in settings where non-random selection is present and the conditional independence assumption no longer holds.
Family Matters: New Research Reveals How Children Transform Women’s Labor Supply Decisions
A research team comprising Sofiia Dolgikh and Bogdan Potanin has published a paper titled “Fertility and labor supply in Mexico” in the Journal of Economic Studies. The article estimates wage elasticities of labor supply of Mexican married women with different numbers of children and analyzes the treatment effect of fertility on labor supply of these women.
Breakthrough Hybrid Model Merges Deep Learning and Production Theory, Outperforming Standard Benchmarks
A research team comprising Zheng Wei, Huiyan Sang, Artem Prokhorov, and Yu Ma has published a paper titled “Shape-Aware Deep Learning for Models of Production” in Journal of Productivity Analysis.
The study proposes a breakthrough method that combines the power of Deep Neural Networks (DNNs) with fundamental economic principles.
Study Reveals Critical Flaws in Standard Methods for Assessing Firm Efficiency
An international research team including Subal C. Kumbhakar, A. Peresetsky, Y. Shchetynin, and A. Zaytsev has published a paper “Technical efficiency and inefficiency: Reliability of standard SFA models and a misspecification problem.” The study uncovers a fundamental issue in Stochastic Frontier Analysis (SFA) models used to evaluate the performance of firms and industries.
New Method for Pinpointing Breaks in Economic Trends
Researchers from the Centre for Big Data in Economics and Finance have developed a new method for accurately identifying structural breaks in economic and financial time series. Their paper, "Change-Point Detection in Time Series Using Mixed Integer Programming," introduces a framework based on Mixed Integer Optimization (MIO).