How Should Conditional Mean and Variance Model be Changed if Residuals Exhibit Autocorrelation?
1 回表示 (過去 30 日間)
古いコメントを表示
I have a time series Y that I know exhibits autocorrelation and heteroscedasticity. Using the estimate function, I fit a conditional mean and variance model to Y. I then use the infer function and get the residuals from the model fit to Y.
Two questions: 1) If the residuals exhibit autocorrelation, how should I change the conditional mean and variance model that was just fit (add more AR or MA lags?)? 2) If the residuals exhibit heteroscedasticity, how should I change the model (add more GARCH or ARCH terms to the variance model?)? Thank you.
0 件のコメント
採用された回答
Roger Wohlwend
2014 年 9 月 22 日
It depends on the autocorrelation. If the autocorrelation occurs at a certain lag, then add a MA term at that lag. If the autocorrelation is a several lags, add AR terms. Another method is that you add AR and MA lags until the autocorrelation disapears. Check the T-values of the coefficients and remove those terms with insignificant coefficients. It is a bit of a trial-and-error process. Add terms and see if you can remove the autocorrelation. However, keep an eye on the T-values. The same process applies for removing the heteroscedasticity.
0 件のコメント
その他の回答 (0 件)
参考
カテゴリ
Help Center および File Exchange で Conditional Variance Models についてさらに検索
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!