What is pure serial correlation?

Pure Serial Correlation This type of serial correlation occurs when the error in one period is correlated with the errors in other periods. The model is assumed to be correctly specified. The most common form of serial correlation is called first-order serial correlation in.

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.

What are serial correlations?

Serial correlation is the relationship between a given variable and a lagged version of itself over various time intervals. It measures the relationship between a variable’s current value given its past values. A variable that is serially correlated indicates that it may not be random.

How does serial correlation affect standard errors?

Serial correlation occurs in time-series studies when the errors associated with a given time period carry over into future time periods. With positive serial correlation, the OLS estimates of the standard errors will be smaller than the true standard errors.

Why is serial correlation bad?

Violations of independence are also very serious in time series regression models: serial correlation in the residuals means that there is room for improvement in the model, and extreme serial correlation is often a symptom of a badly mis-specified model, as we saw in the auto sales example.

What is the most commonly assumed kind of pure serial correlation?

The most commonly assumed kind of serial correlation is first-order serial correlation, in which the current value of the error term is a function of the previous value of the error term.

What is the White test for heteroskedasticity?

In statistics, the White test is a statistical test that establishes whether the variance of the errors in a regression model is constant: that is for homoskedasticity. This test, and an estimator for heteroscedasticity-consistent standard errors, were proposed by Halbert White in 1980.

What does cross correlation tell you?

Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs.

What is the difference between autocorrelation and serial correlation?

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.

Is serial correlation bad?

What is the difference between serial correlation and autocorrelation?

What causes pure heteroskedasticity?

Pure heteroscedasticity refers to cases where you specify the correct model and yet you observe non-constant variance in the residual plots. If the effect of the omitted variable varies throughout the observed range of data, it can produce the telltale signs of heteroscedasticity in the residual plots.

When does impure serial correlation occur in time series regressions?

impure serial correlation often a symptom of some other type of specification error pure serial correlation everything is correctly satisfied, but the error terms are correlated expected correlation between error terms is not equal to 0 When does pure serial correlation often occur? in time series regressions

When to use the Durbin-Watson serial correlation test?

The Durbin-Watson test is often used to test for positive or negative, first-order, serial correlation. It is calculated as follows DW = = e e e j j j N j 2 j N 1 2 2 1 The distribution of this test is difficult because it involves the Xvalues. Originally, Durbin-Watson (1950, 1951) gave a pair of bounds to be used.

How does serial correlation lead to unreliable hypothesis testing?

Serial correlation causes the estimated variances of the regression coefficients to be biased, leading to unreliable hypothesis testing. The t-statistics will actually appear to be more significant than they really are. CHAPTER 9: SERIAL CORRELATION  Page 8 of 19  Testing for First-Order Serial Correlation

How does serial correlation affect the coefficient estimates?

1. pure serial correlation does not cause bias in the coefficient estimates (though it can be with impure) 2. serial correlation causes OLS to no longer be the minimum variance estimator 3. causes the OLS estimates of the SEs to be biased, leading to unreliable hypothesis testing