Quantile Regression: Applications on Experimental and Cross Section Data using EViews
- Length: 496 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2021-06-21
- ISBN-10: 1119715172
- ISBN-13: 9781119715177
- Sales Rank: #0 (See Top 100 Books)
QUANTILE REGRESSION
A thorough presentation of Quantile Regression designed to help readers obtain richer information from data analyses
The conditional least-square or mean-regression (MR) analysis is the quantitative research method used to model and analyze the relationships between a dependent variable and one or more independent variables, where each equation estimation of a regression can give only a single regression function or fitted values variable. As an advanced mean regression analysis, each estimation equation of the mean-regression can be used directly to estimate the conditional quantile regression (QR), which can quickly present the statistical results of a set nine QR(τ)s for τ(tau)s from 0.1 up to 0.9 to predict detail distribution of the response or criterion variable. QR is an important analytical tool in many disciplines such as statistics, econometrics, ecology, healthcare, and engineering.
Quantile Regression: Applications on Experimental and Cross Section Data Using EViews provides examples of statistical results of various QR analyses based on experimental and cross section data of a variety of regression models. The author covers the applications of one-way, two-way, and n-way ANOVA quantile regressions, QRs with multi numerical predictors, heterogeneous QRs, and latent variables QRs, amongst others. Throughout the text, readers learn how to develop the best possible quantile regressions and how to conduct more advanced analysis using methods such as the quantile process, the Wald test, the redundant variables test, residual analysis, the stability test, and the omitted variables test. This rigorous volume:
- Describes how QR can provide a more detailed picture of the relationships between independent variables and the quantiles of the criterion variable, by using the least-square regression
- Presents the applications of the test for any quantile of any numerical response or criterion variable
- Explores relationship of QR with heterogeneity: how an independent variable affects a dependent variable
- Offers expert guidance on forecasting and how to draw the best conclusions from the results obtained
- Provides a step-by-step estimation method and guide to enable readers to conduct QR analysis using their own data sets
- Includes a detailed comparison of conditional QR and conditional mean regression
Quantile Regression: Applications on Experimental and Cross Section Data Using EViews is a highly useful resource for students and lecturers in statistics, data analysis, econometrics, engineering, ecology, and healthcare, particularly those specializing in regression and quantitative data analysis.
Cover Table of Contents Title Page Copyright Dedication Preface About the Author 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 = @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 1.9 Application of the Function @Mediansby(Y,IV) 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 2.4 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 2.8 Special Notes and Comments on Alternative Options 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 3.5 Applications of the N-Way ANOVA-QRs 4 Quantile Regressions Based on (X1,Y1) 4.1 Introduction 4.2 The Simplest Quantile Regression 4.3 Polynomial Quantile Regressions 4.4 Logarithmic Quantile Regressions 4.5 QRs Based on MCYCLE.WF1 4.6 Quantile Regressions Based on SUSENAS-2013.wf1 5 Quantile Regressions with Two Numerical Predictors 5.1 Introduction 5.2 Alternative QRs Based on Data_Faad.wf1 5.3 An Analysis Based on Mlogit.wf1 5.4 Polynomial Two‐Way Interaction QRs 5.5 Double Polynomial QRs 6 Quantile Regressions with Multiple Numerical Predictors 6.1 Introduction 6.2 Alternative Path Diagrams Based on (X1,X2,X3,Y1) 6.3 Applications of QRs Based on Data_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 6.7 Forecasting 6.8 Developing a Complete Data_LW.wf1 6.9 QRs with Four Numerical Predictors 6.10 QRs with Multiple Numerical Predictors 7 Quantile Regressions with the Ranks of Numerical Predictors 7.1 Introduction 7.2 NPQRs Based on a Single Rank Predictor 7.3 NPQRs on Group of R_Milli 7.4 Multiple NPQRs Based on Data‐Faad.wf1 7.5 Multiple NPQRs Based on MLogit.wf1 8 Heterogeneous Quantile Regressions Based on Experimental Data 8.1 Introduction 8.2 HQRs of Y1 on X1 by a Cell‐Factor 8.3 HLQR of Y1 on (X1,X2) by the Cell‐Factor 8.4 The HLQR of Y1 on (X1,X2,X3) by a Cell‐Factor 9 Quantile Regressions Based on CPS88.wf1 9.1 Introduction 9.2 Applications of an ANOVA Quantile Regression 9.3 Quantile Regressions with Numerical Predictors 9.4 Heterogeneous Quantile‐Regressions 10 Quantile Regressions of a Latent Variable 10.1 Introduction 10.2 Spearman‐rank Correlation 10.3 Applications of ANOVA‐QR(τ) 10.4 Three‐way ANOVA‐QR of BLV 10.5 QRs of BLV on Numerical Predictors 10.6 Complete Latent Variables QRs 10.7 An Application of Heterogeneous Quantile‐regressions 10.8 Piecewise QRs Appendix A: Mean and Quantile Regressions A.1 The Single Parameter Mean and Quantile Regressions A.2 The Simplest Conditional Mean and Quantile Regressions A.3 The Estimation Process of the Quantile Regression A.4 An Application of the Forecast Button Appendix B: Applications of the t‐Test Statistic for Testing Alternative Hypotheses B.1 Testing a Two‐Sided Hypothesis B.2 Testing a Right‐Sided Hypothesis B.3 Testing a Left‐Sided Hypothesis Appendix C: Applications of Factor Analysis C.1 Generating the BLV C.2 Generating the MLV References Index End User License Agreement
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.