In this article, we will follow Drukker (2003) procedure to derive the first-order serial correlation test proposed by Jeff Wooldridge (2002) for panel data. It has to be mentioned that this test is considered a robust test, since works with lesser assumptions on the behavior of the heterogeneous individual effects. We start with the linear […]
Wooldridge Serial Correlation Test for Panel Data using Stata. Read More »
When we start to analyze any type of economic relationship, it is often said that we always need to graph the data. The importance of this step is having a visual where we can increase the understanding of our current relationships in the data. Sometimes with this, we can improve the mathematical functional form in
Identifying Patterns with Stata Graphs Read More »
While modelling specific phenomenon’s in economics, sometimes we might encounter a functional form which may not be linear in the explanatory variables. Assuming, that we still have linearity in the estimators, we have the capability to include in the regression, variables with powers. As an example, consider the following model: The last equation presents the
Investigating Non-linear relationships with curvefit using Stata Read More »
The test of Box & Pierce was derived from the article “Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models” in the Journal of the American Statistical Association (Box & Pierce, 1970). The approach is used to test first-order serial correlation, the general form of the test is given the statistic as: Where
Box-Pierce Test of autocorrelation in Panel Data using Stata. Read More »
In regression analysis, we often check the assumptions of the econometrical model regressed, during this, one of the key assumptions is that the model has no omitted variables (and it’s correctly specified). In 1969, Ramsey (1969) developed an omitted variable test, which basically uses the powers of the predicted values of the dependent variable to
Ramsey RESET Test on Panel Data using Stata Read More »
Regarding microeconometrics, we can find applications that go from latent variables to model market decisions (like logit and probit models) and techniques to estimate the basic approaches for consumers and producers. In this article, I want to start with an introduction of a basic concept in microeconomics, which is the Cobb-Douglas utility function and its
A brief example to model the Cobb-Douglas utility function using Stata. Read More »
