Science, Tech, Math › Social Sciences What You Should Know About Econometrics Share Flipboard Email Print shironosov/iStock/Getty Images Social Sciences Economics U.S. Economy Employment Supply & Demand Psychology Sociology Archaeology Ergonomics Maritime By Mike Moffatt Professor of Business, Economics, and Public Policy Ph.D., Business Administration, Richard Ivey School of Business M.A., Economics, University of Rochester B.A., Economics and Political Science, University of Western Ontario Mike Moffatt, Ph.D., is an economist and professor. He teaches at the Richard Ivey School of Business and serves as a research fellow at the Lawrence National Centre for Policy and Management. our editorial process Mike Moffatt Updated April 15, 2018 There are many ways to define econometrics, the simplest of which is that they are statistical methods used by economists to test hypotheses using real-world data. More specifically, it quantitatively analyzes economic phenomena in relation to current theories and observations in order to make concise assumptions about large data sets. Questions like "Is the value of the Canadian dollar correlated to oil prices?" or "Does fiscal stimulus really boost the economy?" can be answered by applying econometrics to datasets on Canadian dollars, oil prices, fiscal stimulus, and metrics of economic well-being. Monash University defines econometrics as "a set of quantitative techniques that are useful for making economic decisions" while The Economist's "Dictionary of Economics" defines it as "the setting up of mathematical models describing mathematical models describing economic relationships (such as that the quantity demanded of a good is dependent positively on income and negatively on price), testing the validity of such hypotheses and estimating the parameters in order to obtain a measure of the strengths of the influences of the different independent variables." The Basic Tool of Econometrics: Multiple Linear Regression Model Econometricians use a variety of simple models in order to observe and find correlation within large data sets, but the most essential of these is the multiple linear regression model, which functionally predicts the value of the two dependent variables as a function of the independent variable. Visually, the multiple linear regression model can be viewed as a straight line through data points that represent paired values of the dependent and independent variables. In this, econometricians attempt to find estimators that are unbiased, efficient, and consistent in predicting the values represented by this function. Applied econometrics, then, uses these theoretical practices to observe real-world data and formulate new economic theories, forecast future economic trends, and develop new econometric models which establish a basis for estimating future economic events as they relate to the data set observed. Using Econometric Modeling to Evaluate Data In tandem with the multiple linear regression model, econometricians use a variety of econometric models to study, observe, and form concise observations of large data sets. The “Economics Glossary” defines an econometric model as one “formulated so that its parameters can be estimated if one makes the assumption that the model is correct.” Basically, econometric models are observational models that allow for quickly estimating future economic trends based on current estimators and exploratory data analysis. Econometricians often use these models to analyze systems of equations and inequalities such as the theory of supply and demand equilibrium or predicting how a market will change based off of economic factors like the actual value of domestic money or the sales tax on that particular good or service. However, since econometricians cannot typically use controlled experiments, their natural experiments with data sets lead to a variety of observational data issues including variable bias and poor causal analysis that leads to misrepresenting correlations between dependent and independent variables.