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E-grāmata: Econometrics

  • Formāts: 926 pages
  • Izdošanas datums: 19-May-2020
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781000096651
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EconometricsPalielināt
  • Formāts: 926 pages
  • Izdošanas datums: 19-May-2020
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781000096651
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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.

Foreword xvii
Preface xix
Author's Note xxi
Notations Used xxiii
Abbreviations xxvii
1 Definitions And Scope Of Econometrics
1(36)
I Why do we study econometrics?
3(2)
II Types of Econometrics
5(1)
III Data employed in econometric analysis
6(6)
Primary data and Secondary data
6(2)
Cross-sectional data and Time series data
8(3)
Univariate data, Bivariate data and Multivariate data
11(1)
Micro data and Macro data
12(1)
IV Terminology used in econometric analysis
12(4)
V Methodology of econometrics
16(21)
Appendix
35(2)
2 Correlation
37(52)
I Pearson's correlation coefficient `r'
38(1)
II Scattergram
39(3)
III Types of Correlation
42(2)
Positive correlation, Negative correlation and Zero correlation
42(1)
Linear correlation and Non - linear correlation
43(1)
IV Methods or Formulae to Compute Correlation Coefficient
44(7)
V Test of significance of `r'
51(1)
VI Methods of studying the significance of `r' value
52(6)
VII Properties of correlation coefficient `r'
58(6)
VIII Numerical Examples for computation of correlation coefficient
64(10)
IX Coefficient of determination (r2)
74(2)
Relationship between r and r2
75(1)
Limitation of r2
76(1)
X Spearman's Rank correlation coefficient `rs'
76(8)
Properties of r
77(1)
Procedure to work out rs
78(5)
Test of significance of `rs'
83(1)
XI Partial correlation coefficient
84(5)
Appendix
86(3)
3 Regression
89(26)
I Methods of estimating regression equations or derivation of regression line
92(13)
Deriving regression equation through normal equations
93(1)
Deriving regression equation through regression coefficients
94(11)
II Properties of regression coefficient and relationship between correlation and regression
105(9)
Differences between Correlation and Regression
109(5)
III Tests of Significance in Regression
114(1)
Classification of Regression Models
114(1)
4 Basic Concepts In Simple (Two-Variable) Regression Analysis (SLRM)
115(84)
I Concept of PRF
119(6)
PRF in Stochastic Form
121(4)
II Concept of SRF
125(4)
III OLS estimation of SLRM
129(5)
IV OLS estimator
134(11)
Assumptions of OLS estimator
134(1)
Features of OLS method or estimator
135(2)
Characteristics of the OLS Coefficient Estimates, a and b
137(8)
V Interpretation of OLS sample estimates a and b
145(1)
VI Measures of variation
145(10)
Total variation
147(2)
Explained variation
149(1)
Unexplained variation
149(6)
VII SE around the estimated regression line (SEYX)
155(3)
VIII Coefficient of determination - Test of Goodness of fit of regression line in SLRM
158(5)
Derivation of r2
158(3)
Interpretation of `r2'
161(2)
Properties of `r2'
IX Mean and variances of the sample estimates in SRF aandb in SRF
163(1)
X Test of significance of SLRM
164(7)
XI Numerical examples in simple linear regression
171(10)
XII How the slope of regression equation changes due to changes in the units of measurement of variables
181(9)
XIII Regression Through Origin (RTO) or Regression model without intercept i.e., estimation of a regression function, whose intercept is zero
190(1)
XIV Elasticity vs Slope in an estimated regression equation
191(8)
Appendix
194(5)
5 Assumptions Of The Classical Linear Regression MODEL (CLRM)
199(24)
I Assumptions about independent variable (X)
200(12)
II Assumptions related to error term, `u'
212(8)
III Other assumptions related to dependent variable, Y
220(3)
6 Establishing The Criteria For Judging The Goodness Of The Parameter Estimates
223(6)
I Specification of the model
223(3)
Variables that are to be included in the model
224(1)
Size (Magnitude) and signs of the estimates
224(1)
Formulation of the econometric model
225(1)
II Estimation of the model by employing an appropriate econometric method
226(1)
III Evaluation of the estimates
227(1)
Economic `a priori' criteria or Theoretical criteria
227(1)
Statistical criteria or First order tests
227(1)
Econometric criteria or Second order tests
228(1)
IV Forecasting the findings of econometric model
228(1)
7 Tests Of Significance Of The Parameter Estimates And Gauss-Markov Theorem
229(1)
I Means and Variances of OLS estimates
230(15)
II Tests of significance
245(1)
III Steps in testing of hypothesis
245(39)
General procedure for statistical testing of hypothesis
283(1)
III Errors in drawing conclusions in research
284(5)
Type I error, Type II error
285(4)
IV Size of test vs Power of a test
289(1)
Benefits of Hypothesis testing
290(1)
V Gauss-Markov Theorem
290(27)
Small or Finite Sample Properties
294(1)
Unbiasedness
295(3)
Minimum Variance
298(7)
Efficiency
305(1)
Linearity
305(2)
Minimum Mean-Square-Error (MSE)
307(1)
Sufficiency
308(1)
Large Sample or Asymptotic Properties: Consistency
309(2)
Importance of BLUE properties of OLS estimates
311(1)
Appendix
312(5)
8 Functional Form Specifications Of (LINEAR) Regression Model
317(73)
I Linear regression model
320(8)
II Different functional forms of Linear regression model
328(62)
Semi log functional form
328(18)
Double log functional form or Log-Log (Double-log) model
346(10)
Polynomial functional form
356(15)
Inverse functional form
371(5)
Regression Through Origin (RTO) Model
376(4)
Choice of Functional Form
380(1)
Box-Cox Test for comparing different forms of linear regression models
381(3)
Other Tests for Functional Form
384(1)
Adjusted R2 Test
384(1)
Ramsey's Regression Specification Error Test (RESET) Test
385(5)
9 Multiple Linear Regression Model (MLRM)
390(119)
I Differences between SLRM and MLRM
391(3)
II Formulation of MLRM
394(7)
The MLRM Building - Input to a regression problem
394(4)
MLRM with Two Independent variables
398(3)
MLRM with `k' Independent variables
401(1)
III Assumptions of MLRM
401(4)
IV Deriving normal equations for MLRM
405(5)
Considering actual values of observations
405(2)
Considering deviations of observations of variables taken from their respective means
407(3)
V General procedure to derive normal equations of MLRM for `k' variables
410(1)
VI Normal equations in SLRM and MLRM
411(1)
VII Interpretation of MLRM Equation
412(5)
Interpretation of the intercept
413(1)
Interpretation of partial regression coefficients
413(3)
Error term
416(1)
VIII Properties of OLS estimates in MLRM
417(4)
IX Expressions for the OLS coefficient estimates of (three variable) MLRM
421(4)
X Goodness of fit of MLRM (R2)
425(4)
Derivation of formula of R2 All Generalization of formula of R2
428(1)
Properties of R2
429(1)
XI Adjusted Coefficient of Multiple Determination (R2)
429(3)
Differences between R2 and R2
430(2)
XII Tests of significance of MLRM
432(18)
Test of significance of individual sample estimate or individual partial regression coefficient
433(3)
Test for the overall significance of MLRM
436(8)
Regression statistics table
444(1)
ANOVA table
445(1)
Regression Coefficients Table
446(1)
Test Hypothesis of estimated slope coefficients (Test of statistical significance of slope coefficient estimates)
447(2)
Confidence Intervals for Partial Slope Coefficients
449(1)
Predicted Value of Y from sample estimates
450(1)
XIII The Regression Equation: Standardized Coefficients
450(5)
XIV Incremental or Marginal contribution of an independent variable
455(6)
XV Testing the equality of two regression coefficients
461(1)
XVI Regression analysis under linear restrictions and preliminary test estimation
462(3)
XVII Relationship between SLRM and MLRM
465(1)
XVIII Different methods of entering independent variables in the MLRM
465(11)
Forced Entry method
467(1)
Hierarchical method
467(3)
Step-wise method
470(1)
Forward selection
471(2)
Backward elimination or deletion
473(3)
XIX Extension of MLRM to non-linear relationships
476(1)
XX Regression and Analysis of Variance (ANOVA)
477(12)
ANOVA as a statistical method to study variation
478(6)
Regression analysis
484(3)
Comparison of ANOVA and regression analysis
487(2)
XXI Multiple Regression - Specification Bias
489(11)
Omission of right independent variable from the model
489(7)
Inclusion of irrelevant independent variable into the model
496(4)
XXII MLRM with interaction among independent variables
500(9)
Appendix
506(3)
10 Relaxing The Assumptions Of Clrm
509(5)
11 Multicollinearity
514(72)
I Why is multicollinearity a problem?
515(1)
II Types of multicollinearity
516(3)
Exact or Perfect Multicollinearity
516(1)
Near or less than perfect or Imperfect Multicollinearity
517(2)
III Sources of multicollinearity
519(2)
IV Examples for multicollinearity
521(3)
V Consequences of Multicollinearity
524(10)
Theoretical consequences
524(2)
Practical consequences
526(8)
VI Detecting multicollinearity
534(52)
Tests for detecting multicollinearity problem in MLRM
554(1)
Frisch's Confluence Analysis or Bunch Map Analysis
554(14)
The Farrar - Glauber test for multicollinearity
568(6)
Solutions for the incidence of multicollinearity
574(12)
12 Hetroscedasticity
586(97)
I Forms of heteroscedasticity
586(6)
Pure heteroscedasticity
586(1)
Impure heteroscedasticity
587(5)
II Reasons for the presence of heteroscedasticity
592(5)
III Interpretation and graphical representation of homoscedasticity and heteroscedasticity
597(1)
IV Consequences of the violation of the assumption of homoscedasticity
598(8)
V Differences between OLS and GLS Methods
606(1)
Case 1 Transforming the variables and applying OLS
608(1)
Case 2 Application of GLS method
608(2)
Deriving the GLS Estimates for a General Linear Regression Model with Heteroscedasticity
610(1)
WLS Estimator
611(1)
Problems with Using the GLS Estimator
612(1)
Feasible Generalized Least Squares (FGLS) Estimator
612(1)
VI Tests for Detection of heteroscedasticity problem
613(1)
Informal methods
614(1)
Nature of the problem
614(1)
Graphical method (Residual plot method)
614(2)
Formal methods
616(1)
Park test
616(6)
Glejser test
622(6)
Spearman rank correlation test
628(3)
Goldfeld and Quandt test
631(5)
Koenker-Bassett (KB) test
636(4)
Breusch-Pagan-Godfrey (BPG) test
640(6)
White test
646(13)
VII Solutions for heteroscedasticity problem
659(20)
Transforming the Heteroscedastic model
659(1)
When σ2iμ is specified or known
659(8)
Use of Robust SEs - Robust inference after OLS
667(8)
Change the functional form of regression model
675(1)
Drop Outliers
676(3)
VII Testing for Heteroscedasticity in Time Series Data
679(4)
13 Autocorrelation
683(123)
I FOARS or First order Markov Process
684(1)
II Second Order Autoregressive Scheme (SOARS)
684(3)
III Calculation of `p' in case of FOARS for population data
687(1)
IV Calculation of `p' in case of FOARS for sample data
687(2)
V Autocorrelation vis-a-vis Serial Correlation
689(2)
Spatial autocorrelation
690(1)
True or Pure autocorrelation
691(1)
False or Impure autocorrelation
691(1)
VI Sources of autocorrelation
691(6)
VII Estimation of Error term (μt) in the presence of autocorrelation (FOARS)
697(1)
VIII Mean, Variance and covariance of autocorrelated error terms
698(3)
IX Consequences of Autocorrelation or Consequences of using OLS in the presence of Autocorrelation
701(15)
X Detection of autocorrelation or Tests for autocorrelation
716(34)
Graphical method
716(1)
Qualitative Approach
716(2)
Plot residuals and lagged values in a 4-Quadrant Diagram 1\1 Plot residuals across time
718(1)
Plot Standardized Residuals across time
719(2)
Quantitative approach or Formal tests
721(1)
The Runs Test
721(3)
Durbin-Watson `d' test
724(16)
The Durbin's `h' Test
740(2)
Berenblut-Webb's `g' test
742(1)
Theil Nagar's Modified `d' statistic
743(1)
An alternative test for autocorrelation
744(1)
An Asymptotic or Large Sample test
744(1)
Breusch-Godfrey (BG) test of High-order autocorrelation
745(3)
Ljung-Box `Q' Test
748(2)
XI Model mis-specification versus Pure Autocorrelation
750(2)
XII Remedial Measures of Autocorrelation
752(28)
Generalized Least Squares (GLS) Procedure
753(3)
Rationalization of the transformation procedure
756(9)
A priori information about `p'
765(1)
Estimation of `p' from Durbin-Watson's `d' statistic
766(1)
Iterative Procedures
767(1)
The Cochrane-Orcutt Iterative Procedure
767(5)
Durbin's Two-Step Method of `p' Estimation
772(4)
Hildreth-Lu (HILU) Search Procedure
776(2)
Newey-West SEs
778(2)
XIII Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models
780(26)
Appendix
793(13)
14 Regression On Dummy Variables
806(81)
I ANOVA Model
807(1)
Regression by employing a single dummy variable
807(1)
Regression by employing two dummy variable?
807(1)
II ANCOVA Model
807(1)
Regression by employing one quantitative independent variable and one dummy variable (with two classes)
807(1)
Regression by employing one quantitative independent variable and two dummy variables (with two classes each)
807(1)
III Interaction effects using Dummy variables
808(41)
Interaction between Quantitative independent variable and Qualitative independent (dummy) variable
808(1)
Interaction between two Qualitative independent (dummy) variables
808(41)
IV Caution in the Use of Dummy Variables
849(15)
V Testing for Structural Stability of Regression Models - Chow Test
864(8)
VI Testing for Structural Stability of Regression Models by employing Dummy Variables - Use of Dummy Variable Technique Alternative to Chow test
872(8)
VII Use of dummy variables in seasonal analysis
880(7)
References 887
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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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