The chapter on econometrics, authored by Peter Kennedy, provides an overview of the field, emphasizing its interdisciplinary nature. Econometricians are often economists, mathematicians, accountants, applied statisticians, and theoretical statisticians, each bringing unique skills to the table. The primary focus is on the development of econometric theory, which involves the application of statistical methods to economic data. The linear regression model is a fundamental tool, extended to handle various nonlinear scenarios. Key differences between econometrics and statistics include the use of real-world data and the incorporation of strategic behavior in economic models. Examples illustrate these differences, such as the impact of self-selection on wage equations, the use of random utility models for transportation choices, the concept of cointegration in time series analysis, and the development of ARCH for volatility modeling. The chapter also discusses identification issues, simultaneous equation bias, and the estimation of demand curves. For a more detailed exploration, references to Geweke et al. (2008) and Kennedy (2008) are provided.The chapter on econometrics, authored by Peter Kennedy, provides an overview of the field, emphasizing its interdisciplinary nature. Econometricians are often economists, mathematicians, accountants, applied statisticians, and theoretical statisticians, each bringing unique skills to the table. The primary focus is on the development of econometric theory, which involves the application of statistical methods to economic data. The linear regression model is a fundamental tool, extended to handle various nonlinear scenarios. Key differences between econometrics and statistics include the use of real-world data and the incorporation of strategic behavior in economic models. Examples illustrate these differences, such as the impact of self-selection on wage equations, the use of random utility models for transportation choices, the concept of cointegration in time series analysis, and the development of ARCH for volatility modeling. The chapter also discusses identification issues, simultaneous equation bias, and the estimation of demand curves. For a more detailed exploration, references to Geweke et al. (2008) and Kennedy (2008) are provided.