Econometrics explained with Professor Barry Reilly of Sussex University

by | Feb 8, 2017

Barry Reilly is currently a Professor of Econometrics at the University of Sussex.  He has previously held posts at the Economic and Social Research Institute (Dublin), University College Dublin, and at the University of St.Andrews.

Professor Barry Reilly is currently convener for the MSc Economics courses and deputy head of department.

Barry Reilly’s research interests lie in the field of applied econometrics and labour economics.  The research emphasis has been on labour market outcomes in developing economies with a focus on gender issues, ethnicity, and informal sector activity.  He has in the past undertaken commissioned research for the World Bank and UNICEF on topics in these areas. More recent research is focussed around the economics of sports.

A rundown on econometrics

“Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations. More precisely, it is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference.” An introductory economics textbook describes econometrics as allowing economists “to sift through mountains of data to extract simple relationships.” The first known use of the term “econometrics” (in cognate form) was by Polish economist Paweł Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense in which it is used today.

Econometric theory uses statistical theory to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. An estimator is unbiased if its expected value is the true value of the parameter; it is consistent if it converges to the true value as sample size gets larger, and it is efficient if the estimator has lower standard error than other unbiased estimators for a given sample size. Ordinary least squares (OLS) is often used for estimation since it provides the BLUE or “best linear unbiased estimator” (where “best” means most efficient, unbiased estimator) given the Gauss-Markov assumptions. When these assumptions are violated or other statistical properties are desired, other estimation techniques such as maximum likelihood estimation, generalized method of moments, or generalized least squares are used. Estimators that incorporate prior beliefs are advocated by those who favor Bayesian statistics over traditional, classical or “frequentist” approaches.

Applied econometrics uses theoretical econometrics and real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting.

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 be useful for generating new hypotheses.

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ALL RIGHTS RESERVED © INVSTR LTD. 2017

Risk Disclosure:

Invstr is a technology platform, not a registered broker-dealer or investment adviser. Invstr does not offer its own recommendations of any security or provide its own research to any user regarding any security transaction or order. Brokerage services, including fractional trading of US securities, are provided to Invstr users by DriveWealth LLC, a regulated member of FINRA/SIPC. DriveWealth may not establish investment accounts to residents of certain jurisdictions. For more information, including disclaimers, risk and transaction fees click here. Please note, investing involves risk and investments may lose value. Past performance does not guarantee future results.

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