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Advanced Studies in Theoretical and Applied Econometrics

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Fixed Effects Models
László Balázsi, Laszlo Matyas and Tom Wansbeek
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
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
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
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
Identification and estimation of categorical random coefficient models
Zhan Gao and M. Hashem Pesaran
The Use of Machine Learning in Treatment Effect Estimation
Robert Lieli, Yu-Chin Hsu and Agoston Reguly
Estimation of Sparse Variance-Covariance Matrix
Felix Chan and Ramzi Chariag
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
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
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
Local Average Treatment Effect and Regression-Discontinuity-Design
Giovanni Cerulli
Fairness in Machine Learning and Econometrics
Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes
A simple, robust test for choosing the level of fixed effects in linear panel data models
Leslie E. Papke and Jeffrey M. Wooldridge
Internal adjustment costs of firm-specific factors and the neoclassical theory of the firm
V. K. Chetty and James J. 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
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
Modelling Migration
Raul Ramos
Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility
Richard T. Baillie, Dooyeon Cho and Seunghwa Rho
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
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