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.

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.

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, 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.

If you use natural log values for your independent variables (X) and keep your dependent variable (Y) in its original scale, the econometric specification is called a linear-log model (basically the mirror image of the log-linear model). These models are typically used when the impact of your independent variable on your dependent variable decreases as the value of your independent variable increases.

Because one primary objective of econometrics is to examine relationships between variables, you need to be familiar with probabilities that combine information on two variables. A bivariate or joint probability density provides the relative frequencies (or chances) that events with more than one random variable will occur.

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.

Economists apply econometric tools in a variety of specific fields (such as labor economics, development economics, health economics, and finance) to shed light on theoretical questions. They also use these tools to inform public policy debates, make business decisions, and forecast future events. Following is a list of ten interesting, practical applications of econometric techniques.

In econometrics, the standard estimation procedure for the classical linear regression model, ordinary least squares (OLS), can accommodate complex relationships. Therefore, you have a considerable amount of flexibility in developing the theoretical model. You can estimate linear and nonlinear functions including but not limited to Polynomial functions (for example, quadratic and cubic functions) Inverse functions Log functions (log-log, log-linear, and linear-log) In many cases, the dependent variable in a regression model can be influenced by both quantitative variables and qualitative factors.