Multiple regression serial correlation

It seems like my data is OK. I received your e-mail. I answered that today: the data that I sent you has no missing values. It is seen because of the Excel format for the variable X5 I used that format because of the magnitud of that variable. I am going to explore using a normal approximation in this case.

In this case when the hat matrix is evaluated it looks like the size becomes too large. For the next release I plan to perform the calculation in a different way to avoid this problem. I am trying to run a regression analysis in which i have 50 time periods, one dep variable and 4 independent variables. I havr conducted data transformations. The minimum I got is dw 1. Is this acceptable? My objective is not to forecast but to find the contributing variable.

I am not sure what you mean by the minimum value of DW, but if DW is 1. Please let me know its equation. Mentioned K as of dependent variables and after the example K as of Independent variables. Could you please check and clarify where K used in this test. Thanks for identifying this typo. I have corrected the referenced webpage. It is used to calculate the predicted y values and it is used in the Durbin-Watson table of critcial values.

Reporting to the Durbin Watson table for alpha 0. JeanMarc, Since the test statistic is between the two critical values, the test is inconclusive. Is this simply the results of the Durbin Watson test? Aside from looking it up in tables someone else figured out already…. Kai, Significance testing for Durbin-Watson is not included in the latest release of the Real Statistics software. I plan to add this shortly, probably in the next release. Kai, I have now included the Durbin-Watson table on the website.

In the next release of the software due out in the next few days I will provide a function that gives the critical values for sample sizes up to 5, elements and up to 20 independent variables. I will also provide a function that carries out the significance test. Kai, Significance testing for Durbin-Watson is available in Release 3. The testing is done using a table lookup. Luan, Your teacher is correct.

While I can think of situations where serial correlation could be used for non-time-series data, in practice it is used with time-series data. My teacher said that Durbin Watson test can only be used for time series data, not for cross sectional data.

When I read this post I very wonder if my teacher was seriously wrong. Can you help me explain this more details? Skip to content.

Charles Reply. Hello Sun, The df are 9 and 10 since this is regression through the origin. Please advise. Charles, There is a typo in the modified version of the Breusch-Godfrey test statistic.

Thanks, -Sun Reply. Hi Charles! I would appreciate it if you help me regarding this question. Best regards, Leonard Reply. See the following webpage re interpolation: Interpolation Charles Reply. Best, Reply. Zaiontz, I have a question. Thank you Hyeongsin Reply.

Thank you for ur reply. Best regards Davide Reply. David Reply. David, I would need to see the data to answer your question. Dave Reply. Thank you so much, Elyas Reply. Thanks, Luca Reply. I am sorry Reply. Hi Charles… Thank you for the greetings and also for support. Hello dr. Charles Could you help me with my doubt bellow? Thanks vary much. Weidson Reply. Charles Thanks vary much by your fast response.

Thank you. William Agurto. The modified equation is then estimated, assuming that heteroskedasticity is no longer a problem. Serial Correlation Autocorrelation Autocorrelation occurs when the assumption that regression errors are uncorrelated across all observations is violated. Types of Serial Correlation Positive serial correlation This is a serial correlation in which positive regression errors for one observation increases the possibility of observing a positive regression error for another observation.

Negative serial correlation This is serial correlation in which a positive regression error for one observation increases the likelihood of observing a negative regression error for another observation.

Effects of Serial Correlation Autocorrelation does not cause bias in the coefficient estimates of the regression. Testing for Serial Correlation The first step of testing for serial correlation is by plotting the residuals against time. Durbin-Watson Test The Durbin Watson tests the null hypothesis of no serial correlation against the alternative hypothesis of positive or negative serial correlation.

The Durbin Watson statistic can take on values ranging from 0 to 4. This is illustrated below. Correcting Autocorrelation We can correct serial correlation by: i. Modifying the regression equation to eliminate the serial correlation. Question Consider a regression model with 80 observations and two independent variables. The most appropriate decision is: A. Reject the null hypothesis of positive serial correlation. Fail to reject the null hypothesis of positive serial correlation.

Declare that the test results are inconclusive. Reading 2: Multiple Regression LOS 2 k Explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference. Featured Swaps. Subscribe to our newsletter and keep up with the latest and greatest tips for success. Our videos feature professional educators presenting in-depth explanations of all topics introduced in the curriculum. Professor Forjan is brilliant.

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