## How do you do the breusch Godfrey test?

The test is carried out as follows:

- Step 1: Run OLS regression to calculate an estimate of the model.
- Step 2: Using these sample residuals e1, e2, …, en, run an OLS regression for the model.
- Step 3: We now test the null hypothesis.
- The test statistic nR2 is sometimes called the LM (Lagrange multiplier) statistic.

## What does the Breusch-Pagan test tell you?

The Breusch-Pagan test is used to determine whether or not heteroscedasticity is present in a regression model. The test uses the following null and alternative hypotheses: Null Hypothesis (H0): Homoscedasticity is present (the residuals are distributed with equal variance)

**How many lags are in breusch Godfrey?**

2 lags

According to the AIC, 2 lags is suitable. In order to check for autocorrelation in our regression model, we want to do a Breuch-Godfrey test. The test acquire to fill in lag order, and this is when we met insecurity.

**What is the null hypothesis for Durbin-Watson test?**

The Durbin-Watson test tests the null hypothesis that linear regression residuals of time series data are uncorrelated, against the alternative hypothesis that autocorrelation exists.

### How do you test for serial correlation?

The presence of serial correlation can be detected by the Durbin-Watson test and by plotting the residuals against their lags. The subscript t represents the time period. In econometric work, these u’s are often called the disturbances. They are the ultimate error terms.

### What is the null hypothesis for heteroskedasticity?

A graph showing heteroscedasticity; the White test is used to identify heteroscedastic errors in regression analysis. The null hypothesis for White’s test is that the variances for the errors are equal. In math terms, that’s: H0 = σ2i = σ2.

**What is the null hypothesis for Homoscedasticity?**

H0 (null hypothesis): data is homoscedastic. Ha (alternative hypothesis): data is heteroscedastic. Therefore, if the p-value associated to a heteroscedasticity test falls below a certain threshold (0.05 for example), we would conclude that the data is significantly heteroscedastic.

**How do you interpret a breusch Pagan p-value?**

This is the basis of the Breusch–Pagan test. It is a chi-squared test: the test statistic is distributed nχ2 with k degrees of freedom. If the test statistic has a p-value below an appropriate threshold (e.g. p < 0.05) then the null hypothesis of homoskedasticity is rejected and heteroskedasticity assumed.

#### What is autocorrelation in regression?

Autocorrelation means the relationship between each value of errors in the equation. Or in the other hand, autocorrelation means the self relationship of errors. This assumption is popularly found in time-series data.

#### What is the null hypothesis of breusch Pagan test?

The null hypothesis for this test is that the error variances are all equal. The alternate hypothesis is that the error variances are not equal. More specifically, as Y increases, the variances increase (or decrease).

**When testing the null hypothesis of no negative autocorrelation you reject the null hypothesis if?**

If the observed value of the test statistic is less than the tabulated lower bound, then you should reject the null hypothesis of non-autocorrelated errors in favor of the hypothesis of positive first-order autocorrelation. Since 0.24878 is less than 1.377, we reject the null hypothesis.

**What is meant by serial correlation?**

Serial correlation is a statistical term used to describe the relationship – specifically, the correlation – between the current value of a variable and a lagged value of the same variable from earlier time periods.

## What is the difference between serial correlation and autocorrelation?

Serial correlation (also called Autocorrelation) is where error terms in a time series transfer from one period to another. In other words, the error for one time period a is correlated with the error for a subsequent time period b.

## What is Breusch-Pagan test for heteroskedasticity?

Breusch Pagan Test It is used to test for heteroskedasticity in a linear regression model and assumes that the error terms are normally distributed. It tests whether the variance of the errors from a regression is dependent on the values of the independent variables.

**What is the null hypothesis for the breusch Pagan test?**

The test statistic approximately follows a chi-square distribution. The null hypothesis for this test is that the error variances are all equal. The alternate hypothesis is that the error variances are not equal. More specifically, as Y increases, the variances increase (or decrease).

**What is difference between correlation and autocorrelation?**

Autocorrelation is a correlation coefficient. However, instead of correlation between two different variables, the correlation is between two values of the same variable at times Xi and Xi+k.

### What is the difference between autocorrelation and multicollinearity?

Autocorrelation is used for signals or time series. Autocorrelation is the correlation of the signal with a delayed copy of itself. Multicollinearity, which should be checked during MLR, is a phenomenon in which at least two independent variables are linearly correlated (one can be predicted from the other).