Is serial correlation a problem?
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. Plotting the residuals is always a good first step.
What is serial correlation example?
Serial correlation occurs in time-series studies when the errors associated with a given period carry over into future periods. For example, when predicting the growth of stock dividends, an overestimate in one year will lead to overestimates in succeeding years.
Can You estimate a model with an AR ( 1 ) error?
The SHAZAM AUTO command can also estimate models with higher order autoregressive errors and models with moving average errors. After estimation with AR (1) errors it is useful to check if the v t errors are serially uncorrelated. The Durbin-Watson test is inappropriate because the transformed model incorporates a lagged dependent variable.
How to detect the presence of AR ( 1 ) errors?
The equation errors have the form: t = t-1 + v t with – 1 < < 1 where ( RHO) is the autoregressive parameter and v t is another random error that is assumed to be zero mean, homoskedastic and serially uncorrelated. Test procedures for detecting the presence of AR (1) errors were discussed earlier in this guide.
How to deal with errors from an AR ( k ) process?
Methods for dealing with errors from an AR ( k) process do exist in the literature, but are much more technical in nature. The first of the three transformation methods we discuss is called the Cochrane-Orcutt procedure, which involves an iterative process (after identifying the need for an AR (1) process):
When do we say that serial correlation is?
What Serial Correlation is. When error terms from different (usually adjacent) time periods (or cross-section observations) are correlated, we say that the error term is.