Advanced Studies in Theoretical and Applied Econometrics

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Fixed Effects Models: László Balázsi, Laszlo Matyas and Tom Wansbeek
Motivations: Chaohua Dong and Jiti Gao
Analysis of Business Surveys: The Mannheim Years: Klaus Zimmermann
Introduction: Subal C. Kumbhakar, Robin C. Sickles and Hung-Jen Wang
Linear Econometric Models with Machine Learning: Felix Chan and Laszlo Matyas
An Introduction to the Econometrics of Program Evaluation: Giovanni Cerulli
Robust dynamic space–time panel data models using ε $$ \varepsilon $$ -contamination: an application to crop yields and climate change: Badi Baltagi, Georges Bresson, Anoop Chaturvedi and Guy Lacroix
Conditional Expectations: Chaohua Dong and Jiti Gao
Still the ‘Dismal Science’ Two Centuries after and the Environment Malthus? Marc Nerlove’s Research on Population and the Environment: John Rust
When and How Much Do Fixed Effects Matter?: Felix Chan, Laszlo Matyas and Agoston Reguly
Nonlinear Econometric Models with Machine Learning: Felix Chan, Mark Harris, Ranjodh B. Singh and Wei (Ben) Ern Yeo
Regressions and Series Estimation: Chaohua Dong and Jiti Gao
Methods Based on Selection on Observables: Giovanni Cerulli
Unbiased estimation of the OLS covariance matrix when the errors are clustered: Tom Boot, Gianmaria Niccodemi and Tom Wansbeek
Random Effects Models: László Balázsi, Badi Baltagi, Laszlo Matyas and Daria Pus
Refined GMM estimators for simultaneous equations models with network interactions: Peter Egger and Ingmar R. Prucha
Density Estimation and Its Applications: Chaohua Dong and Jiti Gao
Re-estimating Supply Elasticities of Selected Agricultural Commodities: Felix Chan, Elizabeth L. Jackson, Richard Dwumfour and László Mátyás
The Use of Machine Learning in Treatment Effect Estimation: Robert Lieli, Yu-Chin Hsu and Agoston Reguly
Identification and estimation of categorical random coefficient models: Zhan Gao and Mohammad Pesaran
Estimation of Sparse Variance-Covariance Matrix: Felix Chan and Ramzi Chariag
Partially Linear Single-Index Regression Models: Chaohua Dong and Jiti Gao
Discrete Games: A Historical Perspective: Paul A. Bjorn, Isabelle Perrigne and Quang Vuong
Forecasting with Machine Learning Methods: Marcelo Medeiros
Dynamic panel GMM estimators with improved finite sample properties using parametric restrictions for dimension reduction: Chirok Han and Hyoungjong Kim
Models with Endogenous Regressors: László Balázsi, Maurice J. G. Bun, Felix Chan and Mark Harris
Additive Nonparametric Models: Chaohua Dong and Jiti Gao
Measuring ‘Income’ Inequality and Distribution of Outcomes: Esfandiar Maasoumi and Yisroel Cahn
Testing for correlation between the regressors and factor loadings in heterogeneous panels with interactive effects: George Kapetanios, Laura Serlenga and Yongcheol Shin
Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods: William Crown
Methods Based on Selection on Unobservables: Giovanni Cerulli
Dynamic Models and Reciprocity: Maurice J. G. Bun, Felix Chan, Mark Harris and Wei Ern (Ben) Yeo
Semiparametric Moment Restriction Models: Chaohua Dong and Jiti Gao
Econometrics of Networks with Machine Learning: Oliver Kiss and Gyorgy Ruzicska
The Wizard of OZ (Opportunity Zones): Spatial Spillovers in Place-Based Programs: Dibya Deepta Mishra, Robin C. Sickles and Yanfei Sun
Assessing the impacts of pandemic and the increase in minimum down payment rate on Shanghai housing prices: Hongjun Li, Zheng Li and Cheng Hsiao
Random Coefficients Models: Monika Avila Marquez, Jaya Krishnakumar and László Balázsi
On the Estimation of Forecaster Loss Functions Using Density Forecasts: Kajal Lahiri, Fushang Liu and Wuwei Wang
Fairness in Machine Learning and Econometrics: Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes
Local Average Treatment Effect and Regression-Discontinuity-Design: Giovanni Cerulli
A simple, robust test for choosing the level of fixed effects in linear panel data models: Leslie Papke and Jeffrey Wooldridge
Estimating Dynamic Probit Models with Higher-order Time- and Network-lag Structure and Correlated Random Effects: Peter H. Egger and Michaela Kesina
Internal adjustment costs of firm-specific factors and the neoclassical theory of the firm: V. K. Chetty and James Heckman
Nonparametric Models with Random Effects: Yiguo Sun, Wei Lin and Qi Li
Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance: Ekaterina Seregina
Proportional incremental cost probability functions and their frontiers: Frédérique Fève, Jean-Pierre Florens and Leopold Simar
Horizontal Regression or Vertical Regression to Generate Counterfactuals?: Cheng Hsiao, Jing Kong, Yimeng Xie and Qiankun Zhou
Nonparametric Models with Fixed Effects: Daniel Henderson and Alexandra Soberon
Nonparametric Correlated Random-Effects Models: Daniel Henderson, Emma Kate Henry and Alexandra Soberon
Poverty, Inequality and Development Studies with Machine Learning: Walter Sosa-Escudero, Maria Victoria Anauati and Wendy Brau