The methodology of econometrics is the study of the range of differing approaches to undertaking econometric analysis.

The econometric approaches can be broadly classified into nonstructural and structural. The nonstructural models are based primarily on statistics (although not necessarily on formal statistical models), their reliance on economics is limited (usually the economic models are used only to distinguish the inputs (observable "explanatory" or "exogenous" variables, sometimes designated as x) and outputs (observable "endogenous" variables, y). Nonstructural methods have a long history (cf. Ernst Engel, 1857). Structural models use mathematical equations derived from economic models and thus the statistical analysis can estimate also unobservable variables, like elasticity of demand. Structural models allow to perform calculations for the situations that are not covered in the data being analyzed, so called counterfactual analysis (for example, the analysis of a monopolistic market to accommodate a hypothetical case of the second entrant).

Commonly distinguished differing approaches that have been identified and studied include:

In addition to these more clearly defined approaches, Hoover identifies a range of heterogeneous or textbook approaches that those less, or even un-, concerned with methodology, tend to follow.

Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science. Analysis of data from an observational study is guided by the study protocol, although exploratory data analysis may by useful for generating new hypotheses. Economics often analyzes systems of equations and inequalities, such as supply and demand hypothesized to be in equilibrium. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous-equation models. These methods are analogous to methods used in other areas of science, such as the field of system identification in systems analysis and control theory. Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system.

One of the fundamental statistical methods used by econometricians is regression analysis. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous-equation models.

In recent decades, econometricians have increasingly turned to use of experiments to evaluate the often-contradictory conclusions of observational studies. Here, controlled and randomized experiments provide statistical inferences that may yield better empirical performance than do purely observational studies.

Data sets to which econometric analyses are applied can be classified as time-series data, cross-sectional data, panel data, and multidimensional panel data. Time-series data sets contain observations over time; for example, inflation over the course of several years. Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year. Panel data sets contain both time-series and cross-sectional observations. Multi-dimensional panel data sets contain observations across time, cross-sectionally, and across some third dimension. For example, the Survey of Professional Forecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time (time series observations), and at multiple forecast horizons (a third dimension).