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Econometrics 1st edition by K Nirmal Ravi Kumar ISBN 1000096653 9781000096651

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ISBN 10: 1000096653
ISBN 13: 9781000096651
Author: K Nirmal Ravi Kumar

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.

Econometrics 1st Table of contents:

1. Definitions and Scope of Econometrics

  • Why study econometrics?: Econometrics helps quantify and test economic theories using statistical methods.

  • Types of Econometrics: Different approaches to econometric analysis, including time-series and cross-sectional econometrics.

  • Data Employed:

    • Primary vs Secondary Data: Primary data is collected firsthand, while secondary data is from pre-existing sources.

    • Cross-sectional vs Time series Data: Cross-sectional data is collected at one point in time, while time series data is collected over multiple periods.

    • Univariate, Bivariate, and Multivariate Data: Data with one, two, or more variables respectively.

    • Micro vs Macro Data: Micro data pertains to individual units, while macro data refers to aggregate national or global data.

  • Econometric Terminology: Key terms and definitions used in econometric analysis.

  • Methodology of Econometrics: The approach to using statistical techniques to analyze economic data.

2. Correlation

  • Pearson’s Correlation Coefficient: A measure of linear relationship between two variables.

  • Scattergram: A graphical representation to show the relationship between two variables.

  • Types of Correlation:

    • Positive, Negative, Zero Correlation: Directions of the relationship between variables.

    • Linear vs Non-Linear Correlation: Differentiates between straight-line relationships and others.

  • Methods for Computing Correlation: Mathematical formulae for calculating correlation coefficients.

  • Test of Significance: Assessing whether the correlation is statistically significant.

  • Coefficient of Determination (r²): Measures the proportion of variance in the dependent variable explained by the independent variable(s).

  • Spearman’s Rank Correlation: A non-parametric method to assess correlation based on ranks.

  • Partial Correlation: The relationship between two variables while controlling for a third.

3. Regression

  • Estimating Regression Equations: Derivation of regression equations through normal equations and regression coefficients.

  • Difference Between Correlation and Regression: Regression predicts values of a dependent variable, while correlation measures the strength of the relationship.

  • Tests of Significance in Regression: Evaluating the statistical significance of regression coefficients.

4. Simple Linear Regression Analysis (SLRM)

  • PRF (Population Regression Function): Represents the relationship between independent and dependent variables.

  • OLS Estimation: Ordinary Least Squares method used to estimate regression coefficients.

  • Interpretation of OLS Estimates: Understanding the meaning of regression coefficients.

  • Measures of Variation: Total, explained, and unexplained variations.

  • Goodness of Fit (r²): Assessing how well the regression line fits the data.

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

  • Assumptions about Independent Variable and Error Term: Fundamental assumptions required for OLS to produce unbiased and efficient estimates.

6. Judging the Goodness of Parameter Estimates

  • Model Specification: Proper inclusion of variables and correct formulation of econometric models.

  • Evaluation Criteria: Statistical, economic, and econometric criteria for evaluating model estimates.

  • Forecasting: Using econometric models for future predictions.

7. Tests of Significance and Gauss-Markov Theorem

  • OLS Estimates: Understanding the properties and significance of OLS estimates.

  • Gauss-Markov Theorem: Conditions under which OLS estimators are BLUE (Best Linear Unbiased Estimators).

8. Functional Form Specifications

  • Linear and Non-Linear Models: Various functional forms such as semi-log, log-log, and polynomial models.

  • Box-Cox Test: A test used to compare different functional forms of a regression model.

9. Multiple Linear Regression Model (MLRM)

  • Differences Between SLRM and MLRM: Extension from simple linear regression (one independent variable) to multiple independent variables.

  • Assumptions in MLRM: Specific assumptions needed for valid multiple regression analysis.

  • Tests of Significance: Hypothesis testing for multiple regression coefficients.

  • Multicollinearity: Problems arising from high correlations between independent variables in MLRM.

10. Relaxing the Assumptions of CLRM

  • Dealing with Violations: Adjustments or methods to handle situations where CLRM assumptions do not hold.

11. Multicollinearity

  • Types and Causes: Identifying and addressing issues arising from high correlations between independent variables.

  • Detection and Solutions: Methods such as the Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) to detect and resolve multicollinearity.

12. Heteroscedasticity

  • Detection: Methods to detect non-constant variance of the error terms, such as residual plots and formal tests.

  • Solutions: Approaches like weighted least squares (WLS) and generalized least squares (GLS) to handle heteroscedasticity.

13. Autocorrelation

  • First-Order Autoregressive Model (FOARS): Explores the relationship between error terms over time.

  • Tests for Autocorrelation: Methods such as the Durbin-Watson test and Breusch-Godfrey test for detecting autocorrelation.

  • Remedial Measures: Generalized Least Squares (GLS) and iterative methods for correcting autocorrelation.

14. Regression on Dummy Variables

  • ANOVA and ANCOVA Models: Techniques for handling categorical independent variables using dummy variables.

  • Interaction Effects: Studying how dummy variables interact with continuous variables in regression models.

  • Testing for Structural Stability: Techniques like the Chow test and use of dummy variables to test if the regression model’s parameters change over time or across groups.

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