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, 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.

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.

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!

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 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.

If you believe that the outcome (dependent variable) you’re modeling is likely to approach some value asymptotically (as X approaches zero or infinity), then an inverse function may be the way to go. Inverse functions can be useful if you’re trying to estimate a Phillips curve (the inverse relationship between inflation and unemployment rates) or a demand function (the inverse relationship between price and quantity demanded), among other economic phenomena where the variables are related inversely.

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.