Editorial Aims and Scope

Editorial Aims

The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research. Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem. Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Econometrics publishes high-quality survey and tutorial monographs of the field.

Each issue of Foundations and Trends® in Econometrics comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.

Editorial Scope

Foundations and Trends® in Econometrics publishes survey and tutorial articles on the following topics:

  • Econometric Models
    • Identification
    • Model Choice and Specification Analysis
    • Non-linear Regression Models
  • Simultaneous Equation Models
  • Estimation Frameworks
  • Biased Estimation
  • Computational Problems
  • Microeconometrics
  • Treatment Modeling
  • Discrete Choice Modeling
  • Models for Count Data
  • Duration Models
  • Limited Dependent Variables
  • Panel Data
  • Time Series Analysis
    • Dynamic Specification
    • Inference and Causality
    • Continuous Time Stochastic Models
    • Modeling Non-linear Time Series
    • Unit Roots
    • Cointegration
  • Latent Variable Models
  • Qualitative Response Models
  • Hypothesis Testing
  • Econometric Theory
    • Interactions-based Models
    • Duration Models
  • Financial Econometrics
  • Measurement Error in Survey Data
  • Productivity Measurement and Analysis
  • Semiparametric and Nonparametric Estimation
  • Bootstrap Methods
  • Nonstationary Time Series
  • Robust Estimation