Microeconometrics is a statistical and mathematical approach to looking at the economic state of a society on an individual level, or the level of just one company instead of using broader economic trends. The data gathered is used to predict economic motivations and activities that are tied to research in the social sciences arena. Some of the statistical methods used include nonlinear modeling, looking for causation instead of just association in data, and making inferences or logical assumptions based on limited distributions of available information. Econometric models at the micro-scale also sometimes simplify analysis to gain a clearer understanding of their meaning through binary approaches, or, testing what happens when a affects b.
Binary models are common in theoretical economics and two types of these models that are frequently used in microeconometrics include the logit and probit models. The logit, or logistic regression model, is a form of regression analysis that takes data and tries to predict outcomes with it, such as basing a customer’s propensity towards purchasing a new car or not on his or her income, age, and family size. The probit model is also a form of linear regression with a simpler binary component to it that tries to predict the maximum likelihood of one of two outcomes, such as whether an individual is married or not based on available probit regression data.
The value of binary econometrics models is built upon the fact that the data is not inadvertently choice-based sampling, where one group is favored over another. Biases can also enter if the choices being studied have only been made by a relatively small sample of the larger population. Compensating for such errors can be done by instead using or including the additive random utility model (ARUM) in the analysis of microeconometrics trends.
Statistical methods at the microeconometrics scale have been around for a long time. Initially, they were used in the mid-1800s to analyze household budget data and research continued with them into the 1950s to study commercial production levels and consumer demand. From the 1980s into the 21st century, the nature of microeconometrics and its focus has changed. This is largely due to the rise of computing power for mathematical analysis alongside much more detailed census data on populations.
Technology like laser scanners in retail stores and corporate analysis of business trends, such as an airline’s records in its online booking of passengers, have led to an explosive capability in microeconometrics. Despite the large databases of information that have arisen and the more complex mathematical models that are used to analyze it, microeconometrics still focuses on several fundamental aspects of analysis. These include the distributional nature of data, nonlinear methods in looking at it, and an attempt to determine causation for actions over simple correlative relationships between the information itself.