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

• Autore: Agung I Gusti Ngurah
• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
• Descrizione fisica: 1 online resource (499 pages)
• Disciplina: 519.536
• Soggetto topico: Quantile regression; Mathematical statistics
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Author -- Chapter 1 Test for the Equality of Medians by Series/Group of Variables -- 1.1 Introduction -- 1.2 Test for Equality of Medians of Y1 by Categorical Variables -- 1.3 Test for Equality of Medians of Y1 by Categorical Variables -- 1.4 Testing the Medians of Y1 Categorized by X1 -- 1.5 Testing the Medians of Y1 Categorized by RX1 & -- equals -- @Ranks(X1,a) -- 1.6 Unexpected Statistical Results -- 1.7 Testing the Medians of Y1 by X1 and Categorical Factors -- 1.8 Testing the Medians of Y by Numerical Variables -- 1.8.1 Findings Based on Data& -- uscore -- Faad.wf1 -- 1.8.2 Findings Based on Mlogit.wf1 -- 1.9 Application of the Function @Mediansby(Y,IV) -- Chapter 2 One‐ and Two‐way ANOVA Quantile Regressions -- 2.1 Introduction -- 2.2 One‐way ANOVA Quantile Regression -- 2.3 Alternative Two‐way ANOVA Quantile Regressions -- 2.3.1 Applications of the Simplest Equation Specification -- 2.3.2 Application of the Quantile Process -- 2.3.3 Applications of the Models with Intercepts -- 2.4 Forecasting -- 2.5 Additive Two‐way ANOVA Quantile Regressions -- 2.6 Testing the Quantiles of Y1 Categorized by X1 -- 2.7 Applications of QR on Population Data -- 2.7.1 One‐way‐ANOVA‐QRs -- 2.7.2 Application of the Forecasting -- 2.7.3 Two‐way ANOVA‐QRs -- 2.8 Special Notes and Comments on Alternative Options -- Chapter 3 N‐Way ANOVA Quantile Regressions -- 3.1 Introduction -- 3.2 The Models Without an Intercept -- 3.3 Models with Intercepts -- 3.4 I × J × K Factorial QRs Based on susenas.wf1 -- 3.4.1 Alternative ESs of CWWH on F1, F2, and F3 -- 3.4.1.1 Applications of the Simplest ES in (3.5a) -- 3.4.1.2 Applications of the ES in (3.5b) -- 3.4.1.3 Applications of the ES in (3.5c) -- 3.5 Applications of the N‐Way ANOVA‐QRs -- 3.5.1 Four‐Way ANOVA‐QRs. ; Chapter 4 Quantile Regressions Based on (X1,Y1) -- 4.1 Introduction -- 4.2 The Simplest Quantile Regression -- 4.3 Polynomial Quantile Regressions -- 4.3.1 Quadratic Quantile Regression -- 4.3.2 Third Degree Polynomial Quantile Regression -- 4.3.3 Forth Degree Polynomial Quantile Regression -- 4.3.4 Fifth Degree Polynomial Quantile Regression -- 4.4 Logarithmic Quantile Regressions -- 4.4.1 The Simplest Semi‐Logarithmic QR -- 4.4.2 The Semi‐Logarithmic Polynomial QR -- 4.4.2.1 The Basic Semi‐Logarithmic Third Degree Polynomial QR -- 4.4.2.2 The Bounded Semi‐Logarithmic Third Degree Polynomial QR -- 4.5 QRs Based on MCYCLE.WF1 -- 4.5.1 Scatter Graphs of (MILL,ACCEL) with Fitted Curves -- 4.5.2 Applications of Piecewise Linear QRs -- 4.5.3 Applications of the Quantile Process -- 4.5.4 Alterative Piecewise Linear QRs -- 4.5.5 Applications of Piecewise Quadratic QRs -- 4.5.6 Alternative Piecewise Polynomial QRs -- 4.5.7 Applications of Continuous Polynomial QRs -- 4.5.8 Special Notes and Comments -- 4.6 Quantile Regressions Based on SUSENAS‐2013.wf1 -- 4.6.1 Application of CWWH on AGE -- 4.6.1.1 Quantile Regressions of CWWH on AGE -- 4.6.1.2 Application of Logarithmic QRs -- 4.6.2 An Application of Life‐Birth on AGE for Ever Married Women -- 4.6.2.1 QR(Median) of LBIRTH on AGE as a Numerical Predictor -- Chapter 5 Quantile Regressions with Two Numerical Predictors -- 5.1 Introduction -- 5.2 Alternative QRs Based on Data& -- uscore -- Faad.wf1 -- 5.2.1 Alternative QRs Based on (X1,X2,Y1) -- 5.2.1.1 Additive QR -- 5.2.1.2 Semi‐Logarithmic QR of log(Y1) on X1 and X2 -- 5.2.1.3 Translog QR of log(Y1) on log(X1) and log(X2) -- 5.2.2 Two‐Way Interaction QRs -- 5.2.2.1 Interaction QR of Y1 on X1 and X2 -- 5.2.2.2 Semi‐Logarithmic Interaction QR Based on (X1,X2,Y1) -- 5.2.2.3 Translogarithmic Interaction QR Based on (X1,X2,Y1). ; 5.3 An Analysis Based on Mlogit.wf1 -- 5.3.1 Alternative QRs of LW -- 5.3.2 Alternative QRs of INC -- 5.3.2.1 Using Z‐Scores Variables as Predictors -- 5.3.2.2 Alternative QRs of INC on Other Sets of Numerical Predictors -- 5.3.2.3 Alternative QRs Based on Other Sets of Numerical Variables -- 5.4 Polynomial Two‐Way Interaction QRs -- 5.5 Double Polynomial QRs -- 5.5.1 Additive Double Polynomial QRs -- 5.5.2 Interaction Double Polynomial QRs -- Chapter 6 Quantile Regressions with Multiple Numerical Predictors -- 6.1 Introduction -- 6.2 Alternative Path Diagrams Based on (X1,X2,X3,Y1) -- 6.2.1 A QR Based on the Path Diagram in Figure a -- 6.2.2 A QR Based on the Path Diagram in Figure b -- 6.2.3 QR Based on the Path Diagram in Figure c -- 6.2.3.1 A Full Two‐Way Interaction QR -- 6.2.3.2 A Full Three‐Way Interaction QR -- 6.2.4 QR Based on the Path Diagram in Figure d -- 6.3 Applications of QRs Based on Data& -- uscore -- Faad.wf1 -- 6.4 Applications of QRs Based on Data in Mlogit.wf1 -- 6.5 QRs of PR1 on (DIST1,X1,X2) -- 6.6 Advanced Statistical Analysis -- 6.6.1 Applications of the Quantiles Process -- 6.6.1.1 An Application of the Process Coefficients -- 6.6.1.2 An Application of the Quantile Slope Equality Test -- 6.6.1.3 An Application of the Symmetric Quantiles Test -- 6.6.2 An Application of the Ramsey RESET Test -- 6.6.3 Residual Diagnostics -- 6.7 Forecasting -- 6.7.1 Basic Forecasting -- 6.7.2 Advanced Forecasting -- 6.8 Developing a Complete Data& -- uscore -- LW.wf1 -- 6.9 QRs with Four Numerical Predictors -- 6.9.1 An Additive QR -- 6.9.2 Alternative Two‐Way Interaction QRs -- 6.9.2.1 A Two‐Way Interaction QR Based on Figure a -- 6.9.2.2 A Two‐Way Interaction QR Based on Figure b -- 6.9.2.3 A Two‐Way Interaction QR Based on Figure c -- 6.9.2.4 A Two‐Way Interaction QR Based on Figure d -- 6.9.3 Alternative Three‐Way Interaction QRs. ; 6.9.3.1 Alternative Models Based on Figure a -- 6.9.3.2 Alternative Models Based on Figure b -- 6.9.3.3 Alternative Models Based on Figure c -- 6.9.3.4 Alternative Models Based on Figure d -- 6.10 QRs with Multiple Numerical Predictors -- 6.10.1 Developing an Additive QR -- 6.10.2 Developing a Simple Two‐Way Interaction QR -- 6.10.3 Developing a Simple Three‐Way Interaction QR -- Chapter 7 Quantile Regressions with the Ranks of Numerical Predictors -- 7.1 Introduction -- 7.2 NPQRs Based on a Single Rank Predictor -- 7.2.1 Alternative Piecewise NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.2 Polynomial NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.3 Special Notes and Comments -- 7.3 NPQRs on Group of R& -- uscore -- Milli -- 7.3.1 An Application of the G& -- uscore -- Milli as a Categorical Variable -- 7.3.2 The kth‐Degree Polynomial NPQRs of ACCEL on G& -- uscore -- Milli -- 7.4 Multiple NPQRs Based on Data‐Faad.wf1 -- 7.4.1 An NPQR Based on a Triple Numerical Variable (X1,X2,Y) -- 7.4.2 NPQRs with Multi‐Rank Predictors -- 7.5 Multiple NPQRs Based on MLogit.wf1 -- Chapter 8 Heterogeneous Quantile Regressions Based on Experimental Data -- 8.1 Introduction -- 8.2 HQRs of Y1 on X1 by a Cell‐Factor -- 8.2.1 The Simplest HQR -- 8.2.2 A Piecewise Quadratic QR -- 8.2.3 A Piecewise Polynomial Quantile Regression -- 8.3 HLQR of Y1 on (X1,X2) by the Cell‐Factor -- 8.3.1 Additive HLQR of Y1 on (X1,X2) by CF -- 8.3.2 A Two‐Way Interaction Heterogeneous‐QR of Y1 on (X1,X2) by CF -- 8.3.3 An Application of Translog‐Linear QR of Y1 on (X1,X2) by CF -- 8.4 The HLQR of Y1 on (X1,X2,X3) by a Cell‐Factor -- 8.4.1 An Additive HLQR of Y1 on (X1,X2,X3) by CF -- 8.4.2 A Full Two‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- 8.4.3 A Full Three‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- Chapter 9 Quantile Regressions Based on CPS88.wf1. ; 9.1 Introduction -- 9.2 Applications of an ANOVA Quantile Regression -- 9.2.1 One‐Way ANOVA‐QR -- 9.2.2 Two‐Way ANOVA Quantile Regression -- 9.2.2.1 The Simplest Equation of Two‐Way ANOVA‐QR -- 9.2.2.2 A Special Equation of the Two‐Way ANOVA‐QR -- 9.2.2.3 An Additive Two‐Way ANOVA‐QR -- 9.2.3 Three‐Way ANOVA‐QRs -- 9.3 Quantile Regressions with Numerical Predictors -- 9.3.1 QR of LWAGE on GRADE -- 9.3.1.1 A Polynomial QR of LWAGE on GRADE -- 9.3.1.2 The Simplest Linear QR of Y1 on a Numerical X1 -- 9.3.2 Quantile Regressions of Y1 on (X1,X2) -- 9.3.2.1 Hierarchical and Nonhierarchical Two‐Way Interaction QRs -- 9.3.2.2 A Special Polynomial Interaction QR -- 9.3.2.3 A Double Polynomial Interaction QR of Y1 on (X1,X2) -- 9.3.3 QRs of Y1 on Numerical Variables (X1,X2,X3) -- 9.3.3.1 A Full Two‐Way Interaction QR -- 9.3.3.2 A Full‐Three‐Way‐Interaction QR -- 9.4 Heterogeneous Quantile‐Regressions -- 9.4.1 Heterogeneous Quantile Regressions by a Factor -- 9.4.1.1 A Heterogeneous Linear QR of LWAGE on POTEXP by IND1 -- 9.4.1.2 A Heterogeneous Third‐Degree Polynomial QR of LWAGE on GRADE -- 9.4.1.3 An Application of QR for a Large Number of Groups -- 9.4.1.4 Comparison Between Selected Heterogeneous QR(Median) -- Chapter 10 Quantile Regressions of a Latent Variable -- 10.1 Introduction -- 10.2 Spearman‐rank Correlation -- 10.3 Applications of ANOVA‐QR(τ) -- 10.3.1 One‐way ANOVA‐QR of BLV -- 10.3.2 A Two‐Way ANOVA‐QR of BLV -- 10.3.2.1 The Simplest Equation of a Two‐Way ANOVA‐QR of BLV -- 10.3.2.2 A Two‐way ANOVA‐QR of BLV with an Intercept -- 10.3.2.3 A Special Equation of Two‐Way ANOVA‐QR of BLV -- 10.4 Three‐way ANOVA‐QR of BLV -- 10.5 QRs of BLV on Numerical Predictors -- 10.5.1 QRs of BLV on MW -- 10.5.1.1 The Simplest Linear Regression of BLV on MW -- 10.5.1.2 Polynomial Regression of BLV on MW -- 10.5.2 QRs of BLV on Two Numerical Predictors. ; 10.5.2.1 An Additive QR of BLV.
• Sommario/riassunto: "Quantile regression aims at estimating either the conditional median or other quantiles of the response variable. Essentially, quantile regression is the extension of linear regression and we use it when the conditions of linear regression are not applicable. LS-Regressions, Ordinary-Regressions or Mean-Regressions, the Quantile-Regressions (QRs) can be classified into three groups. The first group consists of the QRs with categorical variables, caller ANOVA QRs, where ordinal variables are treated as nominal variables and the numerical independent variables (IVs) are transformed to ordinal variables. The second group consists of the QRs with numerical variables, where the ordinal variables are treated as the numerical IVs. The third group consists of the various interaction QRs with numerical and categorical IV, where the ordinal variables can be treated as either numerical or nominal categorical IVs. Applications of Quantile Regression of Experimental and Cross Section Data using EViews presents examples of statistical results of various QRs in order to display their richer characteristics, based on the LS-Regression, Ordinary-Regressions, or Mean-Regressions. It offers instructions how to develop the best possible QRs and how to present more advanced analysis by using the Quantile Process, the Wald test, the Redundant Variables test, Omitted Variables Test, and forecasting, as well as to draw the best conclusions from results. A mathematical knowledge of quantile regression is not necessary so this book is applicable to students and lecturers in statistics, data analysis and engineering"--
• Titolo autorizzato: Quantile regression
• ISBN: 1-119-71518-0; 1-119-71516-4; 1-119-71495-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910829888103321
• Lo trovi qui: Univ. Federico II