# Durbin Watson Statistic

Search Dictionary

## Definition of 'Durbin Watson Statistic'

The Durbin-Watson statistic is a statistical test used to test for autocorrelation in a linear regression model. Autocorrelation is the correlation of the errors in a regression model with the independent variables. The Durbin-Watson statistic is a measure of the serial correlation of the errors in a regression model.

The Durbin-Watson statistic is calculated as follows:

```

DW = (?e^2_t - ?e_t^2) / (?e_t^2)

```

where:

* e^2_t is the squared error for observation t

* e_t is the error for observation t

* ? is the summation operator

The Durbin-Watson statistic can take values between 0 and 4. A value of 0 indicates that there is perfect positive autocorrelation, a value of 4 indicates that there is perfect negative autocorrelation, and a value of 2 indicates that there is no autocorrelation.

The critical values for the Durbin-Watson statistic are typically 1.5 and 2.5. A value of the Durbin-Watson statistic that is less than 1.5 or greater than 2.5 indicates that there is significant autocorrelation in the model.

The Durbin-Watson statistic can be used to test for autocorrelation in both time series data and cross-sectional data. However, it is more commonly used to test for autocorrelation in time series data.

The Durbin-Watson statistic is a useful tool for detecting autocorrelation in regression models. However, it is important to note that the Durbin-Watson statistic is not always reliable. The Durbin-Watson statistic can be affected by the number of observations in the model, the type of regression model, and the presence of outliers.

If the Durbin-Watson statistic indicates that there is significant autocorrelation in the model, it is important to take steps to correct the autocorrelation. One way to correct autocorrelation is to use a different type of regression model, such as a generalized least squares model. Another way to correct autocorrelation is to use a different method of estimation, such as weighted least squares.

The Durbin-Watson statistic is a valuable tool for detecting autocorrelation in regression models. However, it is important to use the Durbin-Watson statistic with caution and to be aware of its limitations.

The Durbin-Watson statistic is calculated as follows:

```

DW = (?e^2_t - ?e_t^2) / (?e_t^2)

```

where:

* e^2_t is the squared error for observation t

* e_t is the error for observation t

* ? is the summation operator

The Durbin-Watson statistic can take values between 0 and 4. A value of 0 indicates that there is perfect positive autocorrelation, a value of 4 indicates that there is perfect negative autocorrelation, and a value of 2 indicates that there is no autocorrelation.

The critical values for the Durbin-Watson statistic are typically 1.5 and 2.5. A value of the Durbin-Watson statistic that is less than 1.5 or greater than 2.5 indicates that there is significant autocorrelation in the model.

The Durbin-Watson statistic can be used to test for autocorrelation in both time series data and cross-sectional data. However, it is more commonly used to test for autocorrelation in time series data.

The Durbin-Watson statistic is a useful tool for detecting autocorrelation in regression models. However, it is important to note that the Durbin-Watson statistic is not always reliable. The Durbin-Watson statistic can be affected by the number of observations in the model, the type of regression model, and the presence of outliers.

If the Durbin-Watson statistic indicates that there is significant autocorrelation in the model, it is important to take steps to correct the autocorrelation. One way to correct autocorrelation is to use a different type of regression model, such as a generalized least squares model. Another way to correct autocorrelation is to use a different method of estimation, such as weighted least squares.

The Durbin-Watson statistic is a valuable tool for detecting autocorrelation in regression models. However, it is important to use the Durbin-Watson statistic with caution and to be aware of its limitations.

Do you have a trading or investing definition for our dictionary? Click the Create Definition link to add your own definition. You will earn 150 bonus reputation points for each definition that is accepted.

Is this definition wrong? Let us know by posting to the forum and we will correct it.

Emini Day Trading /
Daily Notes /
Forecast /
Economic Events /
Search /
Terms and Conditions /
Disclaimer /
Books /
Online Books /
Site Map /
Contact /
Privacy Policy /
Links /
About /
Day Trading Forum /
Investment Calculators /
Pivot Point Calculator /
Market Profile Generator /
Fibonacci Calculator /
Mailing List /
Advertise Here /
Articles /
Financial Terms /
Brokers /
Software /
Holidays /
Stock Split Calendar /
Mortgage Calculator /
Donate

Copyright © 2004-2023, MyPivots. All rights reserved.

Copyright © 2004-2023, MyPivots. All rights reserved.