Direct comparison
Economics vs Econometrics: Key Differences
Economics studies how choices allocate scarce resources; econometrics is the statistical toolkit that measures it. Compare aims, methods, data and careers.
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How do Economics, Econometrics compare side by side?
The table below compares Economics, Econometrics across 12 procurement-relevant dimensions, from definition through careers and when to use which.
Side-by-side comparison
| Dimension | Economics | Econometrics |
|---|---|---|
| Definition | The social science that studies how individuals, businesses, governments and societies allocate scarce resources among competing uses, and how markets, prices, institutions and policy shape the outcomes. | The field that applies statistical and mathematical methods to economic data to test economic theories, estimate the size of economic relationships and forecast economic outcomes. |
| Relationship | The parent discipline. Econometrics is its primary empirical toolkit, and most econometrics faculty and PhD training sit inside economics departments. | A methodological field between economics and statistics. It borrows regression, estimation theory and hypothesis testing from statistics and applies them to economic questions. |
| Core question | What should be produced, how, and for whom? How do choices under scarcity aggregate into markets and economies, and are the outcomes efficient and fair? | How can an economic relationship be measured credibly from data? How large is an effect, how precise is the estimate, and what can be forecast? |
| Theory versus estimation | Builds and tests theory: models of choice, markets, growth and policy. Includes positive economics (what happens) and normative economics (what should happen). | Develops and applies estimation and inference. Theoretical econometrics proves properties of estimators; applied econometrics answers a substantive empirical question with established methods. |
| Main methods | Mathematical modelling and optimisation, game theory, comparative analysis, experiments, historical analysis, and empirical work that relies on econometric methods. | Regression (OLS with robust and cluster-robust standard errors), instrumental variables, panel-data estimators, time-series models, discrete-choice models and quasi-experimental designs such as difference-in-differences and regression discontinuity. |
| Causal inference | Asks causal questions and, since the 1990s, increasingly demands research designs that support causal rather than merely correlational claims (natural experiments, randomized trials, quasi-experiments), sometimes called the credibility revolution. | Supplies much of the machinery for it: tools for extracting causal relationships from observational data, and diagnostics for when a plain regression will mislead (endogeneity, omitted-variable bias, simultaneity). |
| Data | Any evidence bearing on economic behaviour, such as national statistics, surveys, administrative records, experimental data and historical sources, alongside theoretical reasoning that needs no data. | Almost always non-experimental economic data, typically cross-sections, time series or panel data. Time dependence and repeated observation of the same units violate the independence assumptions of introductory statistics. |
| Typical tools | Mathematical and computational modelling tools plus whatever statistical software the empirical work needs. | Stata and R are the most common in academic work, Python is growing, particularly alongside machine learning, and EViews and MATLAB remain common for specialised time-series and structural modelling. |
| Typical work | Analysing labour markets, trade, taxation, monetary and fiscal policy, regulation and growth; advising governments, central banks, international organisations and firms. | Estimating elasticities and treatment effects, evaluating whether a program changed an outcome, forecasting inflation or output, and developing new estimators and inference methods. |
| Training and math | A bachelor's degree covers microeconomics, macroeconomics, statistics or econometrics and calculus. A US PhD typically takes around five to six years, with core theory and econometrics first and dissertation research after. | Usually taught inside economics, at PhD level as a core econometrics sequence, or via a statistics or biostatistics PhD. Needs calculus, linear algebra and probability, and at PhD level real analysis and asymptotic theory. |
| Funders and societies | In the US, NSF's Economics Program within the Division of Social and Economic Sciences (SES) is the primary federal funder of basic research; mission agencies, the Federal Reserve System and foundations such as Russell Sage and Sloan also play roles. The American Economic Association is the principal society. | The same NSF Economics Program funds econometric research tied to economic applications. SES's Methodology, Measurement and Statistics program funds methods work, and NSF's Division of Mathematical Sciences Statistics program funds some theory at the boundary with statistics. The Econometric Society publishes Econometrica. |
| Careers and when to use which | Academia, central banks and finance ministries, international organisations, statistical agencies, think tanks, finance, consulting and technology. Use "economics" for the discipline, a substantive question or a policy field. | Academic economics and statistics departments, central banks, international organisations, statistical agencies, quantitative finance, technology experimentation teams and economic consulting. Use "econometrics" for the estimation method, identification strategy or statistical theory behind a result. |
Common questions
Common questions about Economics vs Econometrics
What is the main difference between economics and econometrics?
+
Economics studies how individuals, firms and governments allocate scarce resources and how markets and policy shape outcomes. Econometrics applies statistical and mathematical methods to economic data to test theories, estimate the size of economic relationships and forecast. Economics provides the questions and theory; econometrics provides much of the empirical measurement.
Is econometrics a part of economics?
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In practice, largely yes. Econometrics is economics' primary empirical toolkit and is usually taught and researched inside economics departments. It is also a distinct methodological field with its own theory, and it shares a mathematical foundation with statistics, so it is best seen as a bridge between the two.
Do economists need to know econometrics?
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Empirical economists do. Almost every empirical economics paper uses econometric methods, and economics PhD programs typically include a core econometrics sequence in the first one to two years. Purely theoretical economists use it less, though they still need to read empirical work critically.
How is econometrics different from statistics?
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Econometrics uses the statistical toolkit (regression, estimation, hypothesis testing) but focuses on economic data, which is mostly non-experimental and often a time series or panel. It has developed specialised methods such as instrumental variables, panel estimators and time-series techniques for those conditions. Statistics is the broader parent field.
What software is used in econometrics?
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Stata and R are the most common in academic econometrics. Python has grown quickly for applied and industry work, especially where econometric methods are combined with machine learning, and EViews and MATLAB remain common for specialised time-series and structural modelling.
How much math do the two fields need?
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Undergraduate economics typically requires calculus and introductory statistics, while undergraduate econometrics typically adds linear algebra and probability. At PhD level both lean on substantially more, including real analysis, probability theory and optimisation, and econometric theory adds asymptotic theory for the properties of estimators.
Which funders support each field?
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In the US, NSF's Economics Program within SES is the primary federal funder of basic economics research and of econometric research tied to economic applications. Methodology, Measurement and Statistics and the Statistics program in the Division of Mathematical Sciences fund methods work. Check each solicitation for current scope and deadlines before advising a PI.
