Roberto Pedace

You can use the statistical tools of econometrics along with economic theory to test hypotheses of economic theories, explain economic phenomena, and derive precise quantitative estimates of the relationship between economic variables.To accurately perform these tasks, you need econometric model-building skills, quality data, and appropriate estimation strategies.

Many economic phenomena are dichotomous in nature; in other words, the outcome either occurs or does not occur. Dichotomous outcomes are the most common type of discrete or qualitative dependent variables analyzed in economics. For example, a student who applies to graduate school will be admitted or not. If you're interested in determining which factors contribute to graduate school admission, then your outcome or dependent variable is dichotomous.

In econometrics, a random variable with a normal distribution has a probability density function that is continuous, symmetrical, and bell-shaped. Although many random variables can have a bell-shaped distribution, the density function of a normal distribution is precisely where represents the mean of the normally distributed random variable X, is the standard deviation,and represents the variance of the normally distributed random variable.

In econometrics, you use the chi-squared distribution extensively. The chi-squared distribution is useful for comparing estimated variance values from a sample to those values based on theoretical assumptions. Therefore, it’s typically used to develop confidence intervals and hypothesis tests for population variance.

The Breusch-Pagan (BP) test is one of the most common tests for heteroskedasticity. It begins by allowing the heteroskedasticity process to be a function of one or more of your independent variables, and it’s usually applied by assuming that heteroskedasticity may be a linear function of all the independent variables in the model.

The t distribution is used quite a bit in econometrics. You probably used the t distribution extensively when dealing with means in your statistics class, but in econometrics you also use it for regression coefficients. Before you find out how that works, you should know how the t distribution is derived and its basic properties.

In econometrics, an extremely common test for heteroskedasticity is the White test, which begins by allowing the heteroskedasticity process to be a function of one or more of your independent variables. It’s similar to the Breusch-Pagan test, but the White test allows the independent variable to have a nonlinear and interactive effect on the error variance.

One of the most important decisions you make when specifying your econometric model is which variables to include as independent variables. Here, you find out what problems can occur if you include too few or too many independent variables in your model, and you see how this misspecification affects your results.

A probability density function (PDF) shows the probabilities of a random variable for all its possible values. The probabilities associated with specific values (or events) from a random variable must adhere to the properties where Xj represents the possible values (outcomes) of random variable X. In other words, the chances of any random event occurring must be anywhere from impossible (probability of 0) to certain (probability of 1), and the sum of the probabilities for all events must be 1 (or 100 percent).