Advanced Studies in Theoretical and Applied Econometrics

From Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).

Access Statistics for this chapter series.
Is something missing from the series or not right? See the RePEc data check for the archive and series.

An Introduction to the Econometrics of Program Evaluation: Giovanni Cerulli
Introduction: Subal C. Kumbhakar, Robin C. Sickles and Hung-Jen Wang
Linear Econometric Models with Machine Learning: Felix Chan and Laszlo Matyas
Fixed Effects Models: László Balázsi, Laszlo Matyas and Tom Wansbeek
Motivations: Chaohua Dong and Jiti Gao
Robust dynamic space–time panel data models using ε $$ \varepsilon $$ -contamination: an application to crop yields and climate change: Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi and Guy Lacroix
Conditional Expectations: Chaohua Dong and Jiti Gao
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
Unbiased estimation of the OLS covariance matrix when the errors are clustered: Tom Boot, Gianmaria Niccodemi and Tom Wansbeek
Methods Based on Selection on Observables: Giovanni Cerulli
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
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
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
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
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
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 E. Papke and Jeffrey Wooldridge
Nonparametric Models with Random Effects: Yiguo Sun, Wei Lin and Qi Li
Internal adjustment costs of firm-specific factors and the neoclassical theory of the firm: V. K. Chetty and James Heckman
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
Nonparametric Models with Fixed Effects: Daniel Henderson and Alexandra Soberon
Poverty, Inequality and Development Studies with Machine Learning: Walter Sosa-Escudero, Maria Victoria Anauati and Wendy Brau
Hotelling tubes, confidence bands and conformal inference: Roger Koenker
Difference-in-Differences with Many Pre- and Post-Treatment Times: Giovanni Cerulli
Indirect inference estimation of stochastic production frontier models with skew-normal noise: Hung-pin Lai and Subal C. Kumbhakar
Synthetic Control Method: Giovanni Cerulli
Multi-dimensional Panels in Quantile Regression Models: Antonio Galvao and Gabriel Montes-Rojas
The noise error component in stochastic frontier analysis: Alecos Papadopoulos
Machine Learning for Asset Pricing: Jantje Sönksen
Multi-Dimensional Models for Spatial Panels: Julie Le Gallo and Alain Pirotte
An alternative corrected ordinary least squares estimator for the stochastic frontier model: Christopher F. Parmeter and Shirong Zhao
The Econometrics of Gravity Models in International Trade: Badi Baltagi, Peter Egger and Katharina Erhardt
Likelihood-based inference for dynamic panel data models: Seung C. Ahn and Gareth M. Thomas
Modelling Housing Using Multi-dimensional Panel Data: Badi Baltagi and Georges Bresson
Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility: Richard T. Baillie, Dooyeon Cho and Seunghwa Rho
Modelling Migration: Raul Ramos
Multi-dimensional Panels in Health Economics with an Application on Antibiotic Consumption: Anikó Bíró, Péter Elek and Nóra Kungl
Does climate change affect economic data?: In Choi
Information loss in volatility measurement with flat price trading: Peter Phillips and Jun Yu
Can Machine Learning Beat Gravity in Flow Prediction?: György Ruzicska, Ramzi Chariag, Olivér Kiss and Miklós Koren
Forecasting in the presence of in-sample and out-of-sample breaks: Jiawen Xu and Pierre Perron
Multivariate models of commodity futures markets: a dynamic copula approach: Sihong Chen, Qi Li, Qiaoyu Wang and Yu Yvette Zhang
Generalized kernel regularized least squares estimator with parametric error covariance: Justin Dang and Aman Ullah
Predicting binary outcomes based on the pair-copula construction: Kajal Lahiri and Liu Yang
Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks: Kerda Varaku and Robin C. Sickles
DS-HECK: double-lasso estimation of Heckman selection model: Masayuki Hirukawa, Di Liu, Irina Murtazashvili and Artem Prokhorov
Simultaneity in binary outcome models with an application to employment for couples: Bo E. Honoré, Luojia Hu, Ekaterini Kyriazidou and Martin Weidner