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### The Role of Casuality in Econometrics

Econometrics is typically used for one of the following objectives: predicting or forecasting future events or explaining how one or more factors affect some outcome of interest. Although some econometrics [more…]

### Econometrics and the Log-Log Model

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 [more…]

### Econometrics and the Log-Linear Model

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 [more…]

### How to Distinguish between Homoskedastic and Heteroskedastic Disturbances

The error term is the most important component of the classical linear regression model (CLRM). Most of the CLRM assumptions that allow econometricians to prove the desirable properties of the OLS estimators [more…]

### A Graphical Inspection of Residuals

Serial correlation in the error term (autocorrelation) is a common problem for OLS regression estimation, especially with time-series and panel data. However, you usually have no way of knowing in advance [more…]

### Projecting Time Trends with OLS

Most economic time series grow over time, but sometimes time series actually decline over time. In either case, you’re looking at a *time trend.* The most common models capturing time trends are either [more…]

### Econometrics and the Probability Density Function (PDF)

A *probability density function* (PDF) shows the probabilities of a random variable for all its possible values. The probabilities associated with specific values [more…]

### Econometrics and the Cumulative Density Function (CDF)

The *cumulative density function* (CDF) of a random variable *X* is the *sum* or *accrual* of probabilities up to some value. It shows how the sum of the probabilities approaches 1, which sometimes occurs at a [more…]

### Bivariate or Joint Probability Density and Econometrics

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 [more…]

### How to Predict the future with Conditional Probability Density

Prediction in econometrics involves some prior knowledge. For example, you may attempt to predict how many “likes” your status update will get on Facebook given the number of “friends” you have and time [more…]

### How to Make Generalizations in Econometrics with Expected Value or Mean

In econometrics, the expected value (or mean) of a random variable provides a measure of central tendency, which means that it provides one measurement of where the data tends to cluster. [more…]

### 10 Components of a Good Econometrics Research Project

Following are the ten components you need to include in any econometrics research project. No matter what the specifics of your class assignment, you’ll probably be expected to come up with a topic, collect [more…]

### Econometrics and the *t* Distribution

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 [more…]

### How to Select Independent Variables for Your Econometric Model

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 [more…]

### The Role of the Breusch-Pagan Test in Econometrics

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 [more…]

### Test for Heteroskedasticity with the White Test

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 [more…]