conometrics can be defined as the application of mathematical statistics tools, and related techniques, to economic problems such as the analysis of economic data and the testing of economic theories and models. Although economists are not often able to collect primary experimental data, econometric tools are available that can readily be applied to secondary data, whether they be cross-sectional or time series in nature. In some ways, the analysis of this type of secondary data gives economists a better understanding of real world phenomena and processes than would more controlled -- but smaller in scope -- experimental studies that allow for the manipulation of variables. The combination of secondary data and econometric data allow economists to develop and test empirical models to better understand economies and make forecasts.
Economics is a social science focusing on the creation, allocation, and utilization of goods and services; the distribution of wealth; and the allocation of resources as well as the theory and management of economic systems. One of the primary goals of economics is to understand and explain how economies work and how economic decisions are made. To advance understanding in these areas, economics is concerned with the theories, principles, and models of economic systems. As a result, many economists are engaged in the development, testing, and application of economic theories with the ultimate goal of being better able to understand and predict real-world behavior. To help in such endeavors, economists apply the scientific method to better parse the large quantities of data available and understand the processes that underlie their action.
To help them better understand the nature and actions of economies, economists are concerned with the development of testable theories and concomitant models that are based on empirical evidence. As shown in Figure 1, the theory building process begins with inductive reasoning in which inferences and general principles are drawn from specific observations or cases. This type of reasoning is a foundation of the scientific method and enables the development of testable hypotheses from particular facts and observations. For example, one might observe that employees with greater levels of training and education are more likely to have successful careers. However, unless one is able to operationally define these terms and articulate the exact theorized nature or the relationship between the variables of training/education and career success, this preliminary theory is nothing more than an opinion. Although it may be a considered opinion based on empirical evidence, un-testable theories are of little use to science on their own. The theory building process, therefore, goes on and builds on the work of the inductive reasoning process by applying deductive reasoning. This is a type of logical reasoning in which it is demonstrated that a conclusion must necessarily follow from a sequence of premises, the first of which is a self-evident truth or agreed-upon data point or condition. Deductive reasoning is the foundation upon which predictions are drawn from general laws or theories.
As shown in Figure 1, both inductive and deductive reasoning are essential to the theory building process. Without careful observation of real-world phenomena and the development of these observations into testable theories and models, economics -- or any science -- cannot advance. Although one may be convinced, for example, that training and education are positively linked with eventual career success and salary, unless one can articulate a testable hypothesis and subject this preliminary theory to the rigors of the scientific method, this theory cannot be confirmed. For example, even in this simple example, there are many other variables that may influence career success such as job experience, intellectual capacity, native skills and abilities, and interest. Depending on their importance in determining career success, they, too, need to be included in the model.
To determine whether a model actually adequately and accurately reflects the phenomena and processes of the real world, it needs to be tested. Econometrics is a subfield of economics that is concerned with the application of quantitative tools to analyze economic data, validate theories, and test models of economic behavior. Econometrics is more than the mere measurement and capture of economic data as the word implies. Econometrics can be defined as the application of the tools of mathematical statistics and related techniques to economic problems, including the analysis of data and the testing of theories and models. Econometric tools are used to estimate economic relationships, test economic theories, evaluate economic policies, and forecast important macroeconomic variables (e.g., interest rates, inflation rates, gross domestic product). Econometric testing is an important component of economics because it helps economists to determine the adequacy and accuracy of their theories. Without econometrics and the objective checks that it provides for the reasonableness of models, it would be difficult (if not impossible) to test the validity of economic models and their strength in forecasting real-world situations. The application of econometrics to test economic theories and models is an important part of the theory building process for economics.
In the physical sciences, one can often experimentally manipulate variables to establish the relationship between the independent variable and the dependent variable. For example, in metallurgy, one might be interested in the strength of a given metal after being subjected to various temperature ranges. In the social sciences -- including economics -- however, the direct manipulation of variables is often not possible not only for logistical reasons (e.g., difficulty collecting data, inability to manipulate variables), but for ethical ones as well. For example, not only would it be logistically impossible to deprive the people in a given country or economy from the education they need to succeed in a career, such an action would be considered morally and ethically reprehensible as well. Therefore, economists and other social scientists are often forced to rely on secondary analysis in order to collect data and test their theories. Secondary analysis is a further analysis of existing data that have typically been collected by a different researcher. The intent of secondary analysis is to use existing data in order to develop conclusions or knowledge in addition to or different from those resulting from the original analysis of the data. Secondary analysis may be qualitative or quantitative in nature and may be used by itself or combined with other research data to reach conclusions.
Types of Data
To develop and test practical models to be used in forecasting, most economists rely on two types of data: cross-sectional and time series. Cross-sectional data are quantifiable observations or measurements on a wide variety of variables during one time period rather than across time periods. For example, one might...
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