Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory.[1]
Statistics And Econometric Models Volume 1 Pdf
In econometrics, as in statistics in general, it is presupposed that the quantities being analyzed can be treated as random variables. An econometric model then is a set of joint probability distributions to which the true joint probability distribution of the variables under study is supposed to belong. In the case in which the elements of this set can be indexed by a finite number of real-valued parameters, the model is called a parametric model; otherwise it is a nonparametric or semiparametric model. A large part of econometrics is the study of methods for selecting models, estimating them, and carrying out inference on them.
The most common econometric models are structural, in that they convey causal and counterfactual information,[2] and are used for policy evaluation. For example, an equation modeling consumption spending based on income could be used to see what consumption would be contingent on any of various hypothetical levels of income, only one of which (depending on the choice of a fiscal policy) will end up actually occurring.
Comprehensive models of macroeconomic relationships are used by central banks and governments to evaluate and guide economic policy. One famous econometric model of this nature is the Federal Reserve Bank econometric model.
Over the recent years, the statistical programming language R has become an integral part of the curricula of econometrics classes we teach at the University of Duisburg-Essen. We regularly found that a large share of the students, especially in our introductory undergraduate econometrics courses, have not been exposed to any programming language before and thus have difficulties to engage with learning R on their own. With little background in statistics and econometrics, it is natural for beginners to have a hard time understanding the benefits of having R skills for learning and applying econometrics. These particularly include the ability to conduct, document and communicate empirical studies and having the facilities to program simulation studies which is helpful for, e.g., comprehending and validating theorems which usually are not easily grasped by mere brooding over formulas. Being applied economists and econometricians, all of the latter are capabilities we value and wish to share with our students.
We use well-established reduced-form econometric techniques5,14 that are commonly used to measure the effects of events6,15 on economic growth rates. Similar to early COVID-19 infections, economic output generally increases exponentially with a variable rate that can be affected by policies and other conditions. Here, this technique aims to measure the total magnitude of the effect of changes in policy, without requiring explicit prior information about fundamental epidemiological parameters or mechanisms, many of which remain uncertain in the current pandemic. Instead, the collective influence of these factors is empirically recovered from the data without modelling their individual effects explicitly (see Methods). Previous research on influenza16, for example, has shown that such statistical approaches can provide important complementary information to process-based models.
To construct the dependent variable, we transform location-specific, subnational time-series data on infections into first differences of their natural logarithm, which is the per-day growth rate of infections (see Methods). We use data from first- or second-level administrative units and data on active or cumulative cases, depending on availability (Supplementary Notes). We employ widely used panel regression models5,14 to estimate how the daily growth rate of infections changes over time within a location when different combinations of large-scale policies are enacted (see Methods). Our econometric approach accounts for differences in the baseline growth rate of infections across subnational locations, which may be affected by time-invariant characteristics, such as demographics, socioeconomic status, culture and health systems; it accounts for systematic patterns in growth rates within countries unrelated to policy, such as the effect of the workweek; it is robust to systematic undersurveillance specific to each subnational unit; and it accounts for changes in procedures to diagnose positive cases (Methods and Supplementary Methods). 2ff7e9595c