Econometrics

Econometrics is the branch of economics that uses statistical methods to quantify and test economic theories and to forecast future observations. It seeks to give empirical content to relationships proposed in economic models by estimating parameters, testing hypotheses, and evaluating policy effects using real-world data. Econometric analysis rests on a combination of economic theory for model specification and statistical inference for estimation and inference.

Typical tasks include estimating how consumer demand responds to prices and income, measuring the effect of education on earnings, or assessing the impact of policy interventions. The main estimation approaches include ordinary least squares regression for linear relationships, instrumental variables to address endogeneity, generalized method of moments for models defined by moment conditions, and maximum likelihood or Bayesian methods for probabilistic models. Time series, cross-sectional, and panel data techniques are used depending on the data structure; common models include ARIMA and vector autoregressions for macro series, and fixed-effects or random-effects models for panel data.

Econometrics also deals with data challenges such as measurement error, sample selection, nonstationarity, and model misspecification. Issues of endogeneity and identification require careful instrument choice and robustness checks. Diagnostic tests, confidence intervals, and hypothesis tests form core tools for inference. Causal econometrics emphasizes strategies for identifying causal effects, including natural experiments, randomization, and policy evaluation methods.

The field originated in the early 20th century with efforts to quantify economic relationships and has evolved alongside advances in statistics, computing, and available data. It underpins empirical research across economics, including labor, development, finance, and international economics, and is essential for evidence-based policymaking and forecasting.