What is 'Econometrics'

Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypotheses in economics, and for forecasting future trends from historical data. It subjects real-world data to statistical trials and then compares and contrasts the results against the theory or theories being tested. Depending on if you are interested in testing an existing theory or using existing data to develop a new hypothesis based on those observations, econometrics can be subdivided into two major categories: theoretical and applied. Those who routinely engage in this practice are commonly known as econometricians.

BREAKING DOWN 'Econometrics'

Econometrics analyzes data using statistical methods in order to test or develop economic theory. These methods rely on statistical inferences to quantify and analyze economic theories by leveraging tools such as frequency distributions, probability and probability distributions, statistical inference, correlation analysis, simple and multiple regression analysis, simultaneous equations models and time series methods.

Econometrics was pioneered by Lawrence Klein, Ragnar Frisch and Simon Kuznets. All three won the Nobel Prize in economics in 1971 for their contributions. Today, it is used regularly among academics as well as practitioners such as Wall Street traders and analysts.

An example of the application of econometrics is to study the income effect using observable data. An economist may hypothesize that as a person increases his income, his spending will also increase. If the data show that such an association is present, a regression analysis can then be conducted to understand the strength of the relationship between income and consumption and whether or not that relationship is statistically significant - that is, it appears to be unlikely that it is due to chance alone.

The Methodology of Econometrics

The first step to econometric methodology is to obtain and analyze a set of data and define a specific hypothesis that explains the nature and shape of the set. This data may be, for example, the historical prices for a stock index, observations collected from a survey of consumer finances, or unemployment and inflation rates in different countries. If you are interested in the relationship between the annual price change of the S&P 500 and the unemployment rate, you'd collect both sets of data. Here, you want to test the idea that higher unemployment leads to lower stock market prices. Stock market price is therefore your dependent variable and the unemployment rate is the independent or explanatory variable. The most common relationship is linear, meaning that any change in the explanatory variable will have a positive correlated with the dependent variable, in which case a simple regression model is often used to explore this relationship, which amounts to generating a best fit line between the two sets of data and then testing to see how far each data point is, on average, from that line. Note that you can have several explanatory variables in your analysis, for example changes to GDP and inflation in addition to unemployment in explaining stock market prices. When more than one explanatory variable is used, it is referred to as multiple linear regression - a model that is the most commonly used tool in econometrics.

Several different regression models exist that are optimized depending on the nature of the data being analyzed and the type of question being asked. The most common example is the ordinary least-squares (OLS) regression, which can be conducted on several types of cross-sectional or time-series data. If you're interested in a binary (yes-no) outcome - for instance, how likely you are to be fired from a job (yes, you get fired, or no, you do not) based on your productivity - you can use a logistic regression or a probit model. Today, there are hundreds of models that an econometrician has at his disposal.

Econometrics is now conducted using statistical analysis software packages designed for these purposes, such as STATA, SPSS, or R. These software packages can also easily test for statistical significance to provide support that the empirical results produced by these models are not merely the result of chance. R-squared, t-testsp-values, and null-hypothesis testing are all methods used by econometricians to evaluate the validity of their model results.

Econometrics is sometimes criticized for relying too heavily on the interpretation of data without linking it to established economic theory. It is crucial that the findings revealed in the data are able to be adequately explained by a theory, even if that means developing your own theory of the underlying processes. Regression analysis also does not prove causation, and just because two data sets show an association, it may be spurious: for example, drowning deaths in swimming pools increase with GDP. Does a growing economy cause people to drown? Of course not, but perhaps more people buy pools when the economy is booming.

RELATED TERMS
  1. Econometrician

    An econometrician uses mathematics and statistics to model, study ...
  2. Ragnar Frisch

    Ragnar Frisch was a Norwegian economist and joint winner in 1969 ...
  3. Daniel L. McFadden

    Daniel L. McFadden is an American econometrician and co-winner ...
  4. Error Term

    An error term is a variable in a statistical model, which is ...
  5. Homoskedastic

    Homoskedastic refers to a condition in which the variance of ...
  6. Zvi Griliches

    Zvi Griliches was an economist who specialized in statistical ...
Related Articles
  1. Investing

    Business forecasting: Understanding the basics

    Discover the methods behind financial forecasts and the risks inherent when we seek to predict the future.
  2. Trading

    4 ways to forecast currency changes

    Whether you are a business or a trader, forecasting currency exchanges helps guide your decisions to minimize risks and maximize returns.
  3. Investing

    Why Colleges Want Economics to Be a STEM Major

    The answer has less to do with philosophy and more to do with immigration policies.
  4. Insights

    The Uncertainty Of Economics: Exploring The Dismal Science

    Learning about the study of economics can help you understand why you face contradictions in the market.
  5. Investing

    Modern Portfolio Theory Vs. Behavioral Finance

    Or: How financial markets would work in an ideal world vs. how they work in the real world.
  6. Investing

    Arbitrage Pricing Theory: It's Not Just Fancy Math

    What are the main ideas behind arbitrage pricing theory? Find out how this model estimates the expected returns of a well-diversified portfolio.
  7. Trading

    Build a Profitable Trading Model In 7 Easy Steps

    Trading models can provide a powerful tool for building profit. Traders can use and customize existing trading models or build an original model. This article provides seven steps to building ...
  8. Investing

    Redefining Investor Risk

    Changing the way you think about time and risk can change the way you invest.
  9. Insights

    5 Nobel Prize-Winning Economic Theories You Should Know About

    Here are 5 prize-winning economic theories that you’ll want to be familiar with.
  10. Tech

    Predictive Analytics Drives Return for Investors

    A new industry of predictive analysis has developed to make sense of big data and give investors real-time buy and sell recommendations based on the patterns forming in the data long before traditional ...
RELATED FAQS
  1. What is the difference between linear regression and multiple regression?

    Learn the difference between linear regression and multiple regression and how multiple regression encompasses not only linear ... Read Answer >>
  2. What is the most important type of data used in business analytics?

    Consider what makes data useful in business analytics, and why companies should search for the types of data that provide ... Read Answer >>
  3. How can you calculate correlation using Excel?

    Find out how to calculate the Pearson correlation coefficient between two data arrays in Microsoft Excel through the CORREL ... Read Answer >>
  4. What are the differences between weak, strong and semi-strong versions of the Efficient ...

    Discover how the efficient market theory is broken down into three versions, the hallmarks of each and the anomalies that ... Read Answer >>
  5. What are some examples of ways that sensitivity analysis can be used?

    Understand the concept of sensitivity analysis and learn about the wide variety of disciplines to which it can be applied. Read Answer >>
Trading Center