Econometrics applies statistical analysis to economic data through theoretical and applied methods. Regression analysis is a common technique used to isolate the effects of individual factors, but the quality of analysis is limited by the availability of data.
Econometrics is the application of statistical analysis to economic data. The various methods of econometrics can be divided into two types: theoretical and applied. Simply put, the former is based on testing whether theories work in a mathematical sense, while the latter tests whether theories are confirmed in the real world, as well as for predictions.
Most econometrics methods are simply variants of more general data analysis. Such an analysis involves researching collections of data and trying to identify both patterns and to identify the strength of those patterns and whether they might be causing bizarre outcomes. Some analysts will simply try to find patterns and then consider possible explanations, while others may start with a hypothesis and then look for data to confirm them.
Some methods of econometrics are purely theoretical. They generally involve examining data collection and analysis techniques, rather than the data itself. For example, an econometrics theory project might involve finding ways to improve the accuracy with which a survey sample group represents the entire population.
Other econometrics methods are hands-on, known as applied methods, and work with real-life data. One use of such methods is to take an economic theory, such as that reducing tax rates increases total tax revenue, and see if it works with real data. Another type of applied econometrics is to look at the patterns and relationships shown by past data and then predict what would happen if those patterns continued into the future.
Such techniques are often extremely complex because every economic decision and action is often influenced by multiple factors. Consequently, one of the most common econometric techniques is regression analysis, which is a technique designed to isolate the effects of individual factors. For example, if an economist was unsure whether it was income levels, local tax levels or mortgage rates that were causing a decline in consumer spending, he would cross-reference the data to see what effect various mortgage rates had on people who were on identical or very similar salaries and lived in areas with the same level of local taxes.
Economists are usually forced to use regression analysis because they cannot perform controlled experiments as can be done in science. This means that the quality of the analysis is often limited by the availability of data. For example, a study of 3,000 people may be sufficient for the results to be considered statistically significant in representing the entire population. However, in the example above there may only be a couple hundred people in the study who have similar incomes and local tax levels. This means that any conclusions about how mortgage rates affect their spending could be treated with more caution.
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