- HOW TO CONVERT STATA 13 TO STATA 12 HOW TO
- HOW TO CONVERT STATA 13 TO STATA 12 PDF
- HOW TO CONVERT STATA 13 TO STATA 12 SERIAL
- HOW TO CONVERT STATA 13 TO STATA 12 SERIES
Only a few limitations apply, such as the file size.
HOW TO CONVERT STATA 13 TO STATA 12 PDF
To convert PDF to DOCX is completely free on PDF2Go.
HOW TO CONVERT STATA 13 TO STATA 12 HOW TO
We can show you how to convert PDF to DOCX, thus turning your PDFs into editable Word documents. With the free online DOCX converter from PDF2Go, you can do it. The following results will appear as shown below.Turning a Microsoft Word document into a PDF is a fairly easy task – the other way around, converting PDF to Word, is not. Therefore if k is 1, then the results of the Breusch-Godfrey test and Durbin Watson test will be the same.įollow the below command for the Breusch Godfrey LM test in STATA. This is unlike the Durbin Watson test which allows testing for only correlation between t and t-1.
HOW TO CONVERT STATA 13 TO STATA 12 SERIAL
Another advantage of this test is that it allows researchers to test for serial correlation through a number of lags besides one lag that is a correlation between the residuals between time t and t-k (where k is the number of lags). The Durbin Watson test relies upon the assumption that the distribution of residuals are normal whereas the Breusch-Godfrey LM test is less sensitive to this assumption. Breusch-Godfrey LM test for autocorrelationīreusch-Godfrey LM test has an advantage over the classical Durbin Watson D test. These are the “critical values” (figure below).ĭurbin Watson d statistics from the STATA command is 2.494, which lies between 4-dl and 4, implying there is a negative serial correlation between the residuals in the model. In the Durbin Watson table two numbers are present– dl and du. In the dataset, the number of observations is 84 and the number of parameters is 2 (GFC and PFC). Durbin Watson test for autocorrelationĭurbin Watson test depends upon 2 quantities the number of observations and the number of parameters to test.
HOW TO CONVERT STATA 13 TO STATA 12 SERIES
Like the previous article ( Heteroscedasticity test in STATA for time series data), first run the regression with the same three variables Gross Domestic Product (GDP), Private Final Consumption (PFC) and Gross Fixed Capital Formation (GFC) for the time period 1997 to 2018. This article focuses on two common tests for autocorrelation the Durbin Watson D test and the Breusch Godfrey LM test. It is therefore important to test for autocorrelation and apply corrective measures if it is present. The presence of autocorrelation in the data causes and correlate with each other and violate the assumption, showing bias in the OLS estimator. Where Cov is the covariance and ϵ is the residual. It is one of the main assumptions of OLS estimator according to the Gauss-Markov theorem that in a regression model: Cov(ϵ_(i,) ϵ_j )=0 ∀i,j,i≠j, An autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other. This article shows a testing serial correlation of errors or time series autocorrelation in STATA. It also showed how to apply a correction for heteroscedasticity so as not to violate the Ordinary Least Squares (OLS) assumption of constant variance of errors. The previous article showed how to perform heteroscedasticity tests of time series data in STATA. Rashmi Sajwan and Priya Chetty on October 22, 2018