Testing the Intercept of a Balanced Predictive Regression Model

Abstract

Testing predictability is known to be an important issue for the balanced predictive regression model. Some unified testing statistics of desirable properties have been proposed, though their validity depends on a predefined assumption regarding whether or not an intercept term nevertheless exists. In fact, most financial data have endogenous or heteroscedasticity structure, and the existing intercept term test does not perform well in these cases. In this paper, we consider the testing for the intercept of the balanced predictive regression model. An empirical likelihood based testing statistic is developed, and its limit distribution is also derived under some mild conditions. We also provide some simulations and a real application to illustrate its merits in terms of both size and power properties.

Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital

“…Therefore, the authors concluded that Genre, Age, Disease, and Service explain about 45.6% of the variability in LOS. Performing the estimation procedure as shown above, the author concluded that LOS and Gender, Age, Disease, and Service are related such that: where 4.3952 represents the Intercept, which gives the response variable when other variables are equal to zero or constants [44]. Table 3 below shows the significance of all the features, which were obtanied after performing the pvalues() function from the fitted model as follows fitted_model.pvalues and creating a function which returns yes if the p-value of a feature is less than 0.05 or no if not.…”