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. Mathematical Economics

    Mathematical economics is a form of economics that relies on ...
  2. Ragnar Frisch

    Ragnar Frisch was a Norwegian economist and joint winner in 1969 ...
  3. Multiple Linear Regression - MLR

    Multiple linear regression (MLR) is a statistical technique that ...
  4. Daniel L. McFadden

    Daniel L. McFadden is an American econometrician and co-winner ...
  5. Stepwise Regression

    Stepwise regression is the step-by-step iterative construction ...
  6. Least Squares Method

    The least squares method is a statistical technique to determine ...
Related Articles
  1. Trading

    The linear regression of time and price

    This investment strategy can help investors be successful by identifying price trends while eliminating human bias.
  2. Investing

    Seven Controversial Investing Theories

    Find out information about seven controversial investing theories that attempt to explain and influence the market as well as the actions of investors.
  3. Small Business

    Calculating (Small) Company Credit Risk

    Determining creditworthiness of smaller and medium-sized corporations isn't as easy as for larger companies, but these tips can help.
  4. Taxes

    Comparing Regressive, Proportional and Progressive Taxes

    Learn about the basic differences between three common tax systems.
  5. Investing

    Redefining Investor Risk

    Changing the way you think about time and risk can change the way you invest.
  6. Investing

    Stock and Flow Variables Explained: A Closer Look at Apple

    The difference between stock and flow variables is an essential concept in finance and economics. We illustrate with financial statements from Apple Inc.
  7. Insights

    Dow Theory

    Learn about the foundation upon which technical analysis is based.
  8. Insights

    Can Investors Trust Official Statistics?

    The official statistics in some countries need to be taken with a grain of salt. Find out why you should be skeptical.
RELATED FAQS
  1. What's the difference between agency theory and stakeholder theory?

    Learn how agency theory and stakeholder theory are used in business to understand common business communication problems ... Read Answer >>
  2. 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 >>
  3. What is the chaos theory?

    The chaos theory is a complicated and disputed mathematical theory that seeks to explain the effect of seemingly insignificant ... Read Answer >>
  4. What is the difference between financial forecasting and financial modeling?

    Understand the difference between financial forecasting and financial modeling, and learn why a company should conduct both ... Read Answer >>
  5. What is the difference between financial forecasting and financial modeling?

    Understand the difference between financial forecasting and financial modeling, and learn why a company should conduct both ... Read Answer >>
Hot Definitions
  1. Portfolio

    A portfolio is a grouping of financial assets such as stocks, bonds and cash equivalents, also their mutual, exchange-traded ...
  2. Gross Profit

    Gross profit is the profit a company makes after deducting the costs of making and selling its products, or the costs of ...
  3. Diversification

    Diversification is the strategy of investing in a variety of securities in order to lower the risk involved with putting ...
  4. Intrinsic Value

    Intrinsic value is the perceived or calculated value of a company, including tangible and intangible factors, and may differ ...
  5. Current Assets

    Current assets is a balance sheet item that represents the value of all assets that can reasonably expected to be converted ...
  6. Volatility

    Volatility measures how much the price of a security, derivative, or index fluctuates.
Trading Center