Econometric models transform economic theories into easily understandable quantitative data that is easy for decision-makers and users to interpret. Econometric techniques utilize statistical inference and mathematical equations to create models used for forecasting or analyzing trends; the field of econometrics draws upon ideas and theories from diverse fields like statistics, mathematics, economics and more.
At the core of an econometrics model is selecting variables to study; these can either be endogenous or exogenous to it. Once this step has been taken, an econometrician must then decide on an estimation procedure to estimate any unknown parameters of each variable from available data and test its hypothesis against existing economic predictions – only then can an econometrician confidently declare their model has worked successfully.
An econometrics model is constructed through statistical inference from economic data that has been collected over time or across an entire population (panel or cross-section data). The model incorporates observations such as prices or quantities purchased for consumption or production by firms or households; also an economist will select an economic theory which assumes optimal behavior from economic agents.
In this article, we’ll look at a simple example of consumer spending on clothing. An econometrician would start by determining the relationship between consumer clothing spending and income earned through labor through time series analysis (regression of income on expenditure). They would then set an error term that represents how far off reality the results may be from reality.
If the regression analysis is satisfactory, an econometrician will then develop a mathematical relationship between variables. This relationship should ideally be linear; meaning that changes to one explanatory variable should result in predictable changes to dependent variables like consumer spending. If there are many influences influencing consumer spending habits or another dependent variable such as stock market wealth changes, an econometrician may include a “catchall” variable which attempts to account for all the influences.
Large and sophisticated aggregate econometric models generally consist of 14 to 48 equations that reflect the complexity of an economy being studied. These models include equations for various economic sectors and markets, such as agriculture, manufactures, housing, retail sales, transportation, employment, and GDP. They incorporate many output and input parameters, such as demand for labor, supply of labor and capital, allocation of property income between investment and disposable income and various production functions. These large-scale models represent an amalgam of several streams of work: Leon Walras’ mathematical work in economic dynamics; Karl Pearson’s statistical inference work and Willford King and Simon Kuznets’ projection models for economic projection purposes.