Advances in Contemporary Statistics and Econometrics : Festschrift in Honor of Christine Thomas-Agnan / edited by Abdelaati Daouia, Anne Ruiz-Gazen.

Format:

Book

Contributor:

Daouia, Abdelaati, editor.

Ruiz-Gazen, Anne, editor.

Language:

English

Subjects (All):

Statistics.

Econometrics.

Social sciences--Statistical methods.

Social sciences.

Statistical Theory and Methods.

Statistics in Business, Management, Economics, Finance, Insurance.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Local Subjects:

Statistical Theory and Methods.

Econometrics.

Statistics in Business, Management, Economics, Finance, Insurance.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Physical Description:

1 online resource (713 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

Mode of access: World Wide Web.

Summary:

This book presents a unique collection of contributions on modern topics in statistics and econometrics, written by leading experts in the respective disciplines and their intersections. It addresses nonparametric statistics and econometrics, quantiles and expectiles, and advanced methods for complex data, including spatial and compositional data, as well as tools for empirical studies in economics and the social sciences. The book was written in honor of Christine Thomas-Agnan on the occasion of her 65th birthday. Given its scope, it will appeal to researchers and PhD students in statistics and econometrics alike who are interested in the latest developments in their field.

Contents:

Intro

Foreword

Preface

Acknowledgements

Contents

Contributors

Nonparametric Statistics and Econometrics

Profile Least Squares Estimators in the Monotone Single Index Model

1 Introduction

2 General Conditions and the Functions n, and ψ

3 The Limit Theory for the SSE

4 The Limit Theory for ESE and Cubic Spline Estimator

5 Simulation and Comparisons with Other Estimators

6 Concluding Remarks

References

Optimization by Gradient Boosting

2 Gradient Boosting

2.1 Mathematical Context

2.2 Some Examples

2.3 Two Algorithms

3 Convergence of the Algorithms

3.1 Algorithm 1

3.2 Algorithm 2

4 Large Sample Properties

Nonparametric Model-Based Estimators for the Cumulative Distribution Function of a Right Censored Variable in a Small Area

2 Estimation of the Cdf of a Censored Variable in a Small Area

2.1 Framework

2.2 Direct Estimators

2.3 The New Small Area Estimator

3 Model-Based Simulations

3.1 Description

3.2 Results

4 Example

5 Concluding Remarks

Relaxing Monotonicity in Endogenous Selection Models and Application to Surveys

2 Preliminaries

2.1 Notations

2.2 Baseline Setup

2.3 NMAR Missing Data

3 Models with One Unobservable in the Endogenous Selection

4 Monotonicity

5 A Random Coefficients Model for the Selection Equation

5.1 Scaling to Handle Genuine Non Instrument Monotonicity

5.2 Alternative Scaling Under a Weak Version of Monotonicity

6 Application to Missing Data in Surveys

B-Spline Estimation in a Survey Sampling Framework

2 B-Spline Model-Assisted Estimator for Finite Population Totals

2.1 B-Spline Model-Assisted Estimation

2.2 B-Spline Calibration Estimator.

3 B-Spline Model-Assisted Estimator for Complex Parameters

4 B-Spline Imputation for Handling Item Nonresponse

Computational Outlier Detection Methods in Sliced Inverse Regression

2 A Brief Review on Usual SIR

3 Outlier Detection Methods in SIR

3.1 A Naive Method

3.2 TTR Method

3.3 BOOT Method

4 A Numerical Example

4.1 Description of the Simulated Dataset

4.2 Numerical Results

5 Simulation Results

6 A Real Data Application

7 Concluding Remarks and Extensions

Uncoupled Isotonic Regression with Discrete Errors

2 Estimation in Uncoupled Regression with Discrete Errors

3 Comparison with Coupled Isotonic Regression

4 Additional Proofs

Quantiles and Expectiles

Partially Linear Expectile Regression Using Local Polynomial Fitting

2 Partially Linear Expectile Regression

3 Statistical Estimation Methodology

3.1 Estimation of the Vector of Regression Coefficients

3.2 Estimation of the Nonparametric Part

4 Asymptotic Properties and Bandwidth Selection

4.1 Optimal Theoretical Bandwidth (Matrix)

4.2 Rule-of-Thumb (ROT) Bandwidth Selector

5 Simulation Study

5.1 Simulation Results for Model 1

5.2 Simulation Results for Model 2

6 Real Data Application

7 Further Reading

Piecewise Linear Continuous Estimators of the Quantile Function

2 The Piecewise Quantile Estimators

2.1 Definition

2.2 First Properties

2.3 Mean Integrated Squared Error

3 Discussion

Appendix

Single-Index Quantile Regression Models for Censored Data

2 Model and Estimation

3 Asymptotic Results

4 Bandwidth Selection

5 Numerical Results

6 Case Study

Extreme Lp-quantile Kernel Regression.

1 Introduction

2 Lp-quantile Kernel Regression

3 Main Results

3.1 Intermediate Lp-quantile Regression

3.2 Extreme Lp-quantile Regression

3.3 Lp-quantile-Based Estimation of the Conditional Tail Index

4 Simulation Study

5 Real Data Example

6 Appendix

6.1 Preliminary Results

6.2 Proofs of Main Results

Robust Efficiency Analysis of Public Hospitals in Queensland, Australia

2 Methodology

2.1 Theoretical Concepts

2.2 Nonparametric Estimators

3 Variables and Data

4 Results and Discussions

4.1 Univariate Input-Output Illustration

4.2 Main Analysis: Multiple Inputs Case

On the Behavior of Extreme d-dimensional Spatial Quantiles Under Minimal Assumptions

2 Results

3 Proofs

Modelling Flow in Gas Transmission Networks Using Shape-Constrained Expectile Regression

2 Description of Data and Motivation

2.1 Data

2.2 Previous Models and Advantages of the New Approach

3 Methods

3.1 Geoadditive Regression Models

3.2 Shape-Constrained P-splines

3.3 Semiparametric Expectile Regression

4 Estimating and Forecasting Gas Flow

4.1 Results

4.2 Risk Analysis

5 Conclusion

Spatial Statistics and Econometrics

Asymptotic Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs

2 Framework and Notations

2.1 Gaussian Processes and Covariance Functions

2.2 Classical Families of Covariance Functions

2.3 Maximum Likelihood

3 Increasing-Domain Asymptotics

3.1 Consistency

3.2 Asymptotic Normality

4 Fixed-Domain Asymptotics

4.1 What Changes

4.2 Microergodic and Non-microergodic Parameters.

4.3 Consistent Estimation of the Microergodic Parameter of the Isotropic Matérn Model

Global Scan Methods for Comparing Two Spatial Point Processes

2.1 Spatial Scan Statistics for Bivariate Data

2.2 Significance Issues

3 Applications

3.1 Simulation Study

3.2 Forest Fire Occurrences

4 Discussion

Assessing Spillover Effects of Spatial Policies with Semiparametric Zero-Inflated Models and Random Forests

2 Conditional Average Treatment Effect, Identification and Model Specification

2.1 Identification Issues and Conditional Independence Assumption

2.2 Zero Inflation and Conditional Mixtures

3 Econometric Modeling and Estimation Procedures

3.1 A Flexible Semi-parametric Modeling Approach Based on Additive Models and Conditional Mixtures

3.2 Estimation of the Conditional Treatment Effect with Random Forests

4 An Illustration on the Estimation of the Effect of Local Development Policies in France

4.1 Description of the Policy and Data

4.2 Estimation Results and Counterfactual Analysis at the Municipality Level

Spatial Autocorrelation in Econometric Land Use Models: An Overview

2 Econometric Land Use Models

3 Linear Land Use Models

3.1 Land Use Share Models

3.2 Spatial Autocorrelation in Linear Models

3.3 Example of Spatial Land Studies with Linear Models

4 Discrete Choice Land Use Models

4.1 Individual Choice Land Use Model

4.2 Spatial Autocorrelation in Discrete Choice Models

4.3 Examples of Spatial Land Use Studies with Discrete Choice Models

5 Land Use and Its Impacts on the Environment

5.1 Land Use and ES

5.2 Land Use and Water Quality

5.3 Land Use and Climate Change

6 Conclusion

References.

Modeling Dependence in Spatio-Temporal Econometrics

2 Spatio-Temporal Statistics

2.1 Uncertainty and Data

2.2 Uncertainty and Models

2.3 Conditional Probabilities in a Hierarchical Statistical Model (HM)

2.4 ``Classical'' Statistical Modeling

3 Spatio-Temporal-Econometric Modeling

3.1 Spatial Description and Temporal Dynamics: A Simple Example

3.2 Time Series of Spatial Processes

3.3 Space-Time Autoregressive Moving Average (STARMA) Models

4 Spatial-Econometric Modeling

5 Modern Spatio-Temporal-Econometric Hierarchical Models

Guidelines on Areal Interpolation Methods

1.1 Motivation

1.2 Context

2 Notations

3 Data

3.1 Target Zones

3.2 First Source Scale: The Cells

3.3 Second Source Scale: The Iris

3.4 Variables to Estimate

4 Point-in-Polygon Method

4.1 Extensive Variables

4.2 Intensive Variables

4.3 Limitation of the Point-in Polygon Method

5 Areal Weighting Interpolation Method

5.1 Extensive Variable

5.2 Intensive Variable

6 Dasymetric Method with Auxiliary Variable X

6.1 Extensive Variables

6.2 Intensive Variables

7 Dasymetric Method with Control Zones

7.1 Presentation of the Method

7.2 Comparison Between DAC and DAX

8 Regression Modelling

8.1 Covariates and Exploratory Analysis

8.2 Linear Modelling

8.3 Regression Tree

Predictions in Spatial Econometric Models: Application to Unemployment Data

1.1 Related Literature

2 Notation, Models, and Prediction Formula

2.1 Notation and the Spatial Autoregressive Durbin Model

2.2 In-Sample and Out-of-Sample Units

2.3 In-Sample Prediction Formulas

2.4 Out-of-Sample Prediction Formulas

3 Application

3.1 Theoretical Explanations for Regional Unemployment Differentials.

3.2 Data and Definition of Neighborhood Structure.

Notes:

Includes bibliographical references and index.

ISBN:

3-030-73249-5

OCLC:

1257076016