To produce a regression analysis of inference that can be justified or trustworthy in the sense that helpful. The term in the statistical methods that generate a linear the best estimator is not bias (best linear unbiased estimator) abbreviated BLUE. Then there are some other things that are also important to note, in which the data to be processed, must meet certain requirements. In terms of statistical methods some terms or conditions of the so-called classical assumption test. Because they meet the assumptions of classical statistical coefficient will be obtained which actually became estimator of parameters that can be justified or accurate, among others:
All terms or phases of the classical assumptions that must be met, in order to build a regression model that could be accounted for. Thus, the need to test that assumption is intended to meet some of the elements of the accuracy of the parameter estimator is not biased to reflect the efficient level of analysis results are consistent so that the regression equation can be trusted.
That in the classical statistical assumptions are considered to have fulfilled just because what counted was to find a causal relationship between the independent variables affect the dependent variable. Whereas in the economy, the assumption is certainly not applicable, because the economic variables must have each other's behavior that allows one to violate these assumptions.
It can be concluded that the assumptions are considered correct in the statistics need to re-examine, in the sense of doing the reprocessing data that exist, such as the increase or decrease of data, combining data, change data in a particular form (differential and integral) and other. It can be called as well as the manipulation of data with the intention of transforming the regression model for the later expected to meet the classical assumptions. For example, to meet the assumption of single linearity (collinearity) if a regression model has a double colinearity (multicollinearity) it is necessary to find a way to correct these deviations settlement. More on how to tackle the problem of double colinearity, there is some way to addressing the problem of the multicollinearity, among others:
More on ways to tackle the problem of the fulfillment of these classical assumptions. It is known that in fact these problems arise because of the certain things. For example on the assumption that a regression model may not contain autocorrelation, where the occurrence of autocorrelation in fact caused by several things. The cause of autocorrelation, among others:
Conclusion
If concluded in fact still found some problems, related in terms of examining the economy and the use of methods of analysis. Broadly speaking the proficiency level in these issues, among others:
With adjustments being an attempt to fulfill certain requirements (classical assumption) in the regression analysis as a form of simplification in the application of modern economics, which is a form of empirical science. It turned out to be ignoring important and fundamental. Namely, with the change in the price or value of the real, the observation result of these adjustments. So the ability to maintain the condition of all-an empirical regression analysis so dubious.
Based on a description of the problem, it's the next point, which is about questions that arise:
The analyst should be a problem-solvers not be troublemakers.
Comment
another I would add onto this is when doing a regression from a sample of a population- which again takes knowledge and training
When so many predictive models exists, one is obliged to find the best model for the given dataset.
Of course, you can force linear regression to model non-linear models through may be piecewise linear regression, but why? when other more appropriate models exist.
For those interested in understanding the finance and economic models don't scale well, and are not linear, please read this article by a PhD model maker
https://blogs.scientificamerican.com/guest-blog/how-to-lose-3-milli...
To Thomas Lincoln, I do like your comment.
To Jeffry: If you cannot find a transform so that your residuals are normally distributed use Non-linear approach, or a non-parametric approach.
Most social science problems cannot be easily solved using linear model or parametric approaches.
If you have missing variables, and you cannot get a viable model, then rethink the problem. You probably have the wrong assumptions about the potential predictors needed to predict the response variable.
In many cases, a matrix scatterplot can help know if linearity is possible between predictors and response variables. For instance, if you have a scatterplot between a predictor and the response variable that looks like an widening and rounding tunnel, you can just use a log function on the predictor to get a linear relation between the predictor and the response variable.
Economics is the study of the behavior of humans handling money. That makes serial or auto correlation impossible to ignore. Human decisions are flawed, not linear, and more behavioral than scientific. Likewise, economic activity does not scale linearly indefinitely. Herding of local behaviors within a business span of control may cause the global economic growth to decline. Outsourcing as much offshore for a single company makes sense; if all companies outsource offshore, there will not be any jobs to support the economy because employees are the consumers as well and without employees, there are no customers. . . something that economics in business conveniently ignores. The problem is that economic aggregate data series does not capture the information needed to realize that the elites have the power to kill the economy, without realizing that they are doing it together when they thought each was doing proper corporatism. I have to stop now. . .
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