In many cases of statistical analysis, we are not sure whether our statistical model is correctly specified. The second approach is to test whether our sample is consistent with these assumptions. The following briefly summarizes specification and diagnostics tests for linear regression. The tests differ in which kind of heteroscedasticity is detecting heteroscedasticity in stata forex as alternative hypothesis.
They also vary in the power of the test for different types of heteroscedasticity. This group of test whether the regression residuals are not autocorrelated. They assume that observations are ordered by time. Multiplier test for Null hypothesis that linear specification is correct. Lagrange Multiplier test for Null hypothesis that linear specification is correct.
This tests against specific functional alternatives. Test whether all or some regression coefficient are constant over the entire data sample. Calculate recursive ols with residuals and cusum test statistic. This is currently mainly helper function for recursive residual based tests. However, since it uses recursive updating and doesn’t estimate separate problems it should be also quite efficient as expanding OLS function. Lilliefors test for normality, this is a Kolmogorov-Smirnov tes with for normality with estimated mean and variance. These measures try to identify observations that are outliers, with large residual, or observations that have a large influence on the regression estimates.
Robust Regression, RLM, can be used to both estimate in an outlier robust way as well as identify outlier. And the weights give an idea of how much a particular observation is down-weighted according to the scaling asked for. This is mainly written for OLS, some but not all measures are also valid for other models. Some of these statistics can be calculated from an OLS results instance, others require that an OLS is estimated for each left out variable. Copyright 2009-2017, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers. Serial correlation is a frequent problem in the analysis of time series data.
Various factors can produce residuals that are correlated with each other, such as an omitted variable or the wrong functional form. If the problem cannot be resolved by improved model specification, then we need to correct for the influence of the autocorrelation through statistical means. For this example we will use the presidentail approval data set: presapp. Stata needs to know that the data set is a time series data set. Sinvce we have quartely data, and only ear and quarter as variables, we need to create a variable coded 1,2,,t for the quarters in the data set. Examine this data with the list command to ensure that it is the series 1 to 148 indicateing that there are 148 quarters in the period 1949-1985.
Stata starts all data sets on at january 1960. This can be alterd, but not here! After running the regression, type the Durbin-Watson Statistic command. Correcting for autocorrelation is easy with STATA. Run the analysis with the Prais-Winston command, specifying the Cochran-Orcutt option. This gives us results that are substantially different from the original results. The Stata information on Prais-Winston estimation options.
Whenever that assumption is violated, then one can assume that heteroscedasticity has occurred in the data. Statistics Solutions is the country’s leader in examining heteroscedasticity and dissertation statistics help. Contact Statistics Solutions today for a free 30-minute consultation. An example can help better explain Heteroscedasticity. Consider an income saving model in which the income of a person is regarded as the independent variable, and the savings made by that individual is regarded as the dependent variable for heteroscedasticity. So, as the value of the income of that individual increases, simultaneously the savings also increase.