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- W1606341369 abstract "This chapter shows the behavior of quantile regressions in datasets with different characteristics. Using simulated data, the chapter also shows the empirical distribution of the quantile regression estimator in the case of independent and identically distributed (i.i.d.) errors, non-identically distributed (i.ni.d.) errors and dependent (ni.i.d.) errors. The chapter analyzes only the case of i.i.d. errors. It considers a small size real dataset and a very simple linear regression model where wages depend on education, to compare ordinary least squares and quantile regression estimates when the errors are i.i.d. Then the simple linear regression model is extended to comprise more than one explanatory variable, and elements such as age, gender and type of work, dependent or independent, full time or part time, are included. The tests considered here allow to verify hypotheses on more than one coefficient at a time, in order to evaluate the validity of the selected explanatory variables." @default.
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- W1606341369 date "2014-08-04" @default.
- W1606341369 modified "2023-10-17" @default.
- W1606341369 title "Estimated coefficients and inference" @default.
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- W1606341369 doi "https://doi.org/10.1002/9781118752685.ch3" @default.
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