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.

If you use natural log values for your dependent variable (Y) and keep your independent variables (X) in their original scale, the econometric specification is called a log-linear model. These models are typically used when you think the variables may have an exponential growth relationship. For example, if you put some cash in a saving account, you expect to see the effect of compounding interest with an exponential growth of your money!

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.

After you acquire data and choose the best econometric model for the question you want to answer, use formulas to produce the estimated output. In some cases, you have to perform these calculations by hand (sorry). However, even if your problem allows you to use econometric software such as STATA to generate results, it's nice to know what the computer is doing.

Getting a grasp on perfect multicollinearity, which is uncommon, is easier if you can picture an econometric model that uses two independent variables, such as the following: Suppose that, in this model, where the alphas are constants. By substitution, you obtain which indicates that the model collapses and can’t be estimated as originally specified.

In econometrics, a specific version of a normally distributed random variable is the standard normal. A standard normal distribution is a normal distribution with a mean of 0 and a variance of 1. It’s useful because you can convert any normally distributed random variable to the same scale, which allows you to easily and quickly calculate and compare probabilities.