Econometrics / K. Nirmal Ravi Kumar.

Format:

Book

Author/Creator:

Ravi Kumar, K. N. (Kotamraju Nirmal), 1969- author.

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (xxvii, 895 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Boca Raton, FL : CRC Press, [2020]

Biography/History:

Dr. K. Nirmal Ravi Kumar is presently working as Professor and Head (Agricultural Economics) in Agricultural College, Mahanandi in Acharya N.G. Ranga Agricultural University. He has a brilliant academic career and specialized in 'Agricultural Marketing' both in his post-graduate and doctoral programmes. He is actively involved both in agricultural research and teaching activities during the past fifteen years in the University. He published 55 articles in popular agricultural journals of both national and international repute. He also contributed two technical bulletins on economic aspects of irrigation water management highlighting the research priorities in major irrigation commands of Andhra Pradesh and need based technological interventions to address the same during his active stint in "Andhra Pradesh Water Management Project", an international project funded by The Royal Netherlands Embassy. His interested areas include International trade of Indian agriculture, Farming systems approach, Irrigation water management etc.

Summary:

This book harbors an updated and standard material on the various aspects of Econometrics. It covers both fundamental and applied aspects and is intended to serve as a basis for a course in Econometrics and attempts at satisfying a need of postgraduate and doctoral students of Economics. It is hoped that, this book will also be worthwhile to teachers, researchers, professionals etc. Note: T& F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

Contents:

Cover

Title Page

Copyright Page

Dedication

Table of Contents

Foreword

Preface

Author's Note

Notations Used

Abbreviations

1: Definitions and Scope of Econometrics

I. Why Do We Study Econometrics?

II. Types of Econometrics

III. Data Employed in Econometric Analysis

Primary Data and Secondary Data

Cross-Sectional Data and Time Series Data

Univariate Data, Bivariate Data and Multivariate Data

Micro Data and Macro Data

IV. Terminology Used in Econometric Analysis

V. Methodology of Econometrics

Appendix

2: Correlation

I. Pearson's Correlation Coefficient 'r'

II. Scattergram

III. Types of Correlation

Positive Correlation, Negative Correlation and Zero Correlation

Linear Correlation and Non - Linear Correlation

IV. Methods or Formulae to Compute Correlation Coefficient

V. Test of Significance of 'r'

VI. Methods of Studying the Significance of 'r' Value

VII. Properties of Correlation Coefficient 'r'

VIII. Numerical Examples for Computation of Correlation Coefficient

IX. Coefficient of Determination (r2)

Relationship Between r and r2

Limitation of r2

X. Spearman's Rank Correlation Coefficient 'rs'

Properties of rs

Procedure to Work Out rs

Test of Significance of 'rs'

XI. Partial Correlation Coefficient

3: Regression

I. Methods of Estimating Regression Equations or Derivation of Regression Line

Deriving Regression Equation Through Normal Equations

Deriving Regression Equation Through Regression Coefficients

II. Properties of Regression Coefficient and Relationship Between Correlation and Regression

Differences Between Correlation and Regression

III. Tests of Significance in Regression

Classification of Regression Models

4: Basic Concepts in Simple (Two-Variable) Regression Analysis (SLRM).

I. Concept of PRF

PRF in Stochastic Form

II. Concept of SRF

III. OLS Estimation of SLRM

IV. OLS Estimator

Assumptions of OLS Estimator

Features of OLS Method or Estimator

Characteristics of the OLS Coefficient Estimates, â and b̂

V. Interpretation of OLS Sample Estimates â and b̂

VI. Measures of Variation

Total Variation

Explained Variation

Unexplained Variation

VII. SE Around the Estimated Regression Line (SEyx)

VIII. Coefficient of Determination - Test of Goodness of Fit of Regression Line in SLRM

Derivation of r2

Interpretation of 'r2'

Properties of 'r2'

IX. Mean and Variances of the Sample Estimates in SRF â and b̂ in SRF

X. Test of Significance of SLRM

XI. Numerical Examples in Simple Linear Regression

XII. How the Slope of Regression Equation Changes Due to Changes in the Units of Measurement of Variables

XIII. Regression Through Origin (RTO) or Regression Model Without Intercept i.e., Estimation of a Regression Function, Whose Intercept Is Zero

XIV. Elasticity vs Slope in an Estimated Regression Equation

5: Assumptions of the Classical Linear Regression Model (CLRM)

I. Assumptions About Independent Variable (x)

II. Assumptions Related to Error Term, 'µ'

III. Other Assumptions Related to Dependent Variable, Y

6: Establishing the Criteria for Judging the Goodness of the Parameter Estimates

I. Specification of the Model:

Variables that are to be Included in the Model

Size (Magnitude) and Signs of the Estimates

Formulation of the Econometric Model

II. Estimation of the Model by Employing an Appropriate Econometric Method

III. Evaluation of the Estimates

Economic 'a Priori' Criteria or Theoretical Criteria

Statistical Criteria or First Order Tests

Econometric Criteria or Second Order Tests.

IV. Forecasting the Findings of Econometric Model

7: Tests of Significance of the Parameter Estimates and Gauss-Markov Theorem

I. Means and Variances of OLS Estimates

II. Tests of Significance

III. Steps in Testing of Hypothesis

General Procedure for Statistical Testing of Hypothesis

III. Errors in Drawing Conclusions in Research

Type I Error, Type II Error

IV. Size of Test vs Power of a Test

Benefits of Hypothesis Testing

V. Gauss-Markov Theorem

Small or Finite Sample Properties

Unbiasedness

Minimum Variance

Efficiency

Linearity

Minimum Mean-Square-Error (MSE)

Sufficiency

Large Sample or Asymptotic Properties: Consistency

Importance of Blue Properties of OLS Estimates

8: Functional Form Specifications of (Linear) Regression Model

I. Linear Regression Model

II. Different Functional Forms of Linear Regression Model

Semi Log Functional Form

Double Log Functional Form or Log-Log (Double-Log) Model

Polynomial Functional Form

Inverse Functional Form

Regression Through Origin (RTO) Model

Choice of Functional Form

Box-Cox Test for Comparing Different Forms of Linear Regression Models

Other Tests for Functional Form

Adjusted R2 Test

Ramsey's Regression Specification Error Test (RESET) Test

9: Multiple Linear Regression Model (MLRM)

I. Differences Between SLRM and MLRM

II. Formulation of MLRM

The MLRM Building - Input to a Regression Problem

MLRM with Two Independent Variables

MLRM with 'k' Independent Variables

III. Assumptions of MLRM

IV. Deriving Normal Equations for MLRM

Considering Actual Values of Observations

Considering Deviations of Observations of Variables Taken from their Respective Means

V. General Procedure to Derive Normal Equations of MLRM for 'k' Variables

VI. Normal Equations in SLRM and MLRM.

VII. Interpretation of MLRM Equation

Interpretation of the Intercept

Interpretation of Partial Regression Coefficients

Error Term

VIII. Properties of OLS Estimates in MLRM

IX. Expressions for the OLS Coefficient Estimates of (Three Variable) MLRM

X. Goodness of Fit of MLRM (R2)

Derivation of Formula of R2

Generalization of Formula of R2

Properties of R2

XI. Adjusted Coefficient of Multiple Determination ( R̄2 )

Differences Between R2 and R̄2

XII. Tests of Significance of MLRM

Test of Significance of Individual Sample Estimate or Individual Partial Regression Coefficient

Test for the Overall Significance of MLRM

Regression Statistics Table

ANOVA Table

Regression Coefficients Table

Test Hypothesis of Estimated Slope Coefficients (Test of Statistical Significance of Slope Coefficient Estimates)

Confidence Intervals for Partial Slope Coefficients

Predicted Value of Y from Sample Estimates

XIII. The Regression Equation: Standardized Coefficients

XIV. Incremental or Marginal Contribution of an Independent Variable

XV. Testing the Equality of Two Regression Coefficients

XVI. Regression Analysis Under Linear Restrictions and Preliminary Test Estimation

XVII. Relationship Between SLRM and MLRM

XVIII.Different Methods of Entering Independent Variables in the MLRM

Forced Entry Method

Hierarchical Method

Step-Wise Method

Forward Selection

Backward Elimination or Deletion

XIX. Extension of MLRM to Non-Linear Relationships

XX. Regression and Analysis of Variance (ANOVA)

ANOVA as a Statistical Method to Study Variation

Regression Analysis

Comparison of ANOVA and Regression Analysis

XXI. Multiple Regression - Specification Bias

Omission of Right Independent Variable from the Model

Inclusion of Irrelevant Independent Variable into the Model.

XXII. MLRM with Interaction Among Independent Variables

10: Relaxing the Assumptions of CLRM

11: Multicollinearity

I. Why Is Multicollinearity a Problem?

II. Types of Multicollinearity:

Exact or Perfect Multicollinearity

Near or Less Than Perfect or Imperfect Multicollinearity

III. Sources of Multicollinearity

IV. Examples for Multicollinearity

V. Consequences of Multicollinearity

Theoretical Consequences

Practical Consequences

VI. Detecting Multicollinearity

Tests for Detecting Multicollinearity Problem in MLRM

Frisch's Confluence Analysis or Bunch Map Analysis

The Farrar - Glauber Test for Multicollinearity

Solutions for the Incidence of Multicollinearity

12: Hetroscedasticity

I. Forms of Heteroscedasticity

Pure Heteroscedasticity

Impure Heteroscedasticity

II. Reasons for the Presence of Heteroscedasticity

III. Interpretation and Graphical Representation of Homoscedasticity and Heteroscedasticity

IV. Consequences of the Violation of the Assumption of Homoscedasticity

V. Differences Between OLS and GLS Methods

Case 1 -Transforming the Variables and Applying OLS

Case 2 - Application of GLS Method

Deriving the GLS Estimates for a General Linear Regression Model with Heteroscedasticity

WLS Estimator

Problems with Using the GLS Estimator

Feasible Generalized Least Squares (FGLS) Estimator

VI. Tests for Detection of Heteroscedasticity Problem

Informal Methods

Nature of the Problem

Graphical Method (Residual Plot Method)

Formal Methods

Park Test

Glejser Test

Spearman Rank Correlation Test

Goldfeld and Quandt Test

Koenker-Bassett (KB) Test

Breusch-pagan-godfrey (BPG) Test

White Test

VII. Solutions for Heteroscedasticity Problem

Transforming the Heteroscedastic Model

When σ2iμ is Specified or Known.

Use of Robust SEs - Robust Inference After OLS.

Notes:

Includes bibliographical references.

Description based on print version record.

ISBN:

1-00-307965-2

1-000-09661-0

1-003-07965-2

1-000-09665-3

9781003079651

OCLC:

1155487536