I. Gusti Ngurah Agung - Applications of Quantile Regression of Experimental and Cross Section Data using EViews: Applications on Experimental and Cross Section Data using EViews

1. Chapter 2
2. Chapter 3
3. Chapter 4
4. Chapter 5
5. Chapter 6
6. Chapter 7
7. Chapter 8
8. Chapter 9
9. Chapter 10
10. Appendix B

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Appendix A
12. Appendix B
13. Appendix C

I Gusti Ngurah Agung

The Ary Suta Center

Jakarta, Indonesia

This edition first published 2021
2021 John Wiley & Sons Ltd

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Library of Congress CataloginginPublication Data

Names: Agung, I Gusti Ngurah, author.

Title: Quantile regression : applications on experimental and cross section data using EViews / I Gusti Ngurah Agung.

Description: First edition. | Hoboken : Wiley, [2021] | Includes bibliographical references.

Identifiers: LCCN 2020025365 (print) | LCCN 2020025366 (ebook) | ISBN 9781119715177 (cloth) | ISBN 9781119715160 (adobe pdf) | ISBN 9781119715184 (epub)

Subjects: LCSH: Quantile regression. | Mathematical statistics. | EViews (Computer file)

Classification: LCC QA278.2 .A32 2021 (print) | LCC QA278.2 (ebook) | DDC 519.5/36dc23

LC record available at https://lccn.loc.gov/2020025365

LC ebook record available at https://lccn.loc.gov/2020025366

Cover Design: Wiley

Cover Images: Kertlis/iStock/Getty Images

Dedicated to my wife Anak Agung Alit Mas, our children Martiningsih, Ratnaningsing, and Darma Putra, as well as all our generation

This book presents various Quantile Regressions ( QR ), based on the crosssection and experimental data. It has been found that the equation specification or the estimation equation of the LSRegression or MeanRegression ( MR ) can be applied directly for the QuantileRegression. Hence, this book can be considered as an extension or a modification of all MeanRegressions presented in all books, and papers, such as Agung ().

In addition, compare to the MeanRegression (MR), the QuantileRegression is a robust regression having critical advantages over the MR, mainly for the robustness to outliers, no normal distribution assumption, and it can present more complete distribution of the objective, criterion or dependent random variable, using the linear programing estimation method (Davino et al..

The models presented in this book in fact are the extension or modification of all meanregression presented in my second book: Cross Section and Experimental Data Analysis Using EViews (Agung 2011a). For this reason, it is recommended the readers to use also the models in the book to conduct the quantileregression analysis, using their own data sets.

This book contains ten chapters.

presents the applications of the test for medians of any response or criterion variable Yi, by series/group of categorical variables, numerical categorical or the ranks of a numerical variable.

presents the applications of OneWay and TwoWay ANOVA QuantileRegressions. In addition, the application of the object Quantile Process having three alternative options are introduced. As the modification of the parametric DID ( DifferenceInDifferences ) of the means of any variable Yi by two factors or categorical variables, this chapter presents the DID of the Quantile() of any variable Yi, by two categorical variables, starting with two dichotomous variables or 22 factorial QR. It has been well known that the value of a DID is representing the twoway interaction effect of the corresponding two factors, indicating the effect of a factor on the criterion variable Yi depends on the other factor. Then a special 23 factorial QR to demonstrate how to compute its DID.

presents NWay ANOVA QuantileRegressions. Specific for N = 3, alternative equation specifications are presented, starting with a 222 factorial ANOVA QR without an intercept, to show how compute conditional twoway interactions factors, and a threeway interaction factor, which should be tested using the Wald Test. As an extension the 222 factorial ANOVA QR without an intercept, a 223 factorial ANOVA QR without an intercepts to show how to compute the 3way interaction factor. Then selected IJK factorial ANOVA QRs with an intercepts are presented using alternative equation specifications, to show the advantages in using each equation specification.

presents quantile regressions based on bivariate numerical variable (Xi,Yi), starting with the simplest linear quantileregression. Then it is extended quadratic QR and alternative polynomial QRs, and alternative logarithmic QRs with lower and upper bounds.

. Three alternative path diagrams based on the triple variables are presented, as the guide to defined equation specifications of alternative QRs. An additive QR is presented as the simplest QR, which is extended to semilogarithmic and translog QRs, with the examples presented based on an experimental data in Data_Faad.wf1. Then they are extended to interaction QRs. As additional illustrative examples, the statistical results of alternative QRs are presented based on MLogit.wf1, which I consider as a special data in EViews work file. In addition special Quantile Slope Equality Test also is presented as an illustration.