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.

Using natural logs for variables on both sides of your econometric specification is called a log-log model. This model is handy when the relationship is nonlinear in parameters, because the log transformation generates the desired linearity in parameters (you may recall that linearity in parameters is one of the OLS assumptions).

Avoiding mistakes when you do econometric analysis depends on your ability to apply knowledge you acquired before and during your econometrics class. Following is a rundown of common pitfalls to help you improve your application of econometric analysis. Failing to use your common sense and knowledge of economic theory One of the characteristics that differentiate applied research in econometrics from other applications of statistical analysis is the use of economic theory and common sense to motivate the connection between the independent and dependent variables.

Econometrics students always appreciate a review of the statistical concepts that are most important to succeeding with econometrics. Specifically, you need to be comfortable with probability distributions, the calculation of descriptive statistics, and hypothesis tests. Your ability to accurately quantify economic relationships depends not only on your econometric model-building skills but also on the quality of the data you’re using for analysis and your capacity to adopt the appropriate strategies for estimating models that are likely to violate a statistical assumption.

In econometrics, the procedure used for forecasting can be quite varied. If historical data is available, forecasting typically involves the use of one or more quantitative techniques. If historical data isn't available, or if it contains significant gaps or is unreliable, then forecasting can actually be qualitative.

In econometrics, the regression model is a common starting point of an analysis. As you define your regression model, you need to consider several elements: Economic theory, intuition, and common sense should all motivate your regression model. The most common regression estimation technique, ordinary least squares (OLS), obtains the best estimates of your model if the CLRM assumptions hold.