Econometrie

Econometrics is the discipline that applies statistical and mathematical methods to economic data to give empirical content to economic theories. Its aims include estimating quantitative relationships, testing hypotheses, forecasting key variables, and evaluating policy interventions. Econometric analysis seeks reliable inferences about how economic agents respond to changes in prices, policy, and other factors.

The term and field emerged in the early 20th century through the work of Ragnar Frisch and Jan Tinbergen. Initially focused on simple regression to estimate relationships, econometrics evolved to handle dynamic settings, simultaneous equations, and causal inference, with advances in probability theory, experimental design, and computing.

A model translates economic theory into an estimable relationship. Estimation yields parameter values; inference assesses uncertainty. Challenges include endogeneity, measurement error, and misspecification. Good practice combines theory, data diagnostics, and robustness checks to support credible conclusions.

Common methods include ordinary least squares for linear relationships; generalized least squares; maximum likelihood; and generalized method of moments. Instrumental variables and two-stage least squares address endogeneity. Dynamic models use autoregressive and moving average processes, vector autoregressions, cointegration, and error-correction models. Microeconometrics covers discrete choice, duration models, and treatment effects with panel data.

Data can be cross-sectional, time series, or panel. Applications span macroeconomic forecasting, demand and labor studies, finance, development, and policy evaluation. Limitations include data quality, external validity, measurement error, and challenges in identifying causal effects from observational data.

Practitioners use software such as R, Stata, EViews, Python, and MATLAB. The field evolves with new methods for causal analysis, high-frequency data, and computational efficiency.