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Econometrics by Example 2nd Edition Gujarati Solutions Manual

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Econometrics by Example 2nd Edition Gujarati Solutions Manual.

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This is completed downloadable of Econometrics by Example 2nd Edition Gujarati Solutions Manual

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ISBN-10 ‏ : ‎ 1137375019
ISBN-13 ‏ : ‎ 978-1137375018
Author: Damodar Gujarati

The second edition of this bestselling textbook retains its unique learning-by-doing approach to econometrics. Rather than relying on complex theoretical discussions and complicated mathematics, this book explains econometrics from a practical point of view by walking the student through real-life examples, step by step. Damodar Gujarati’s clear, concise, writing style guides students from model formulation, to estimation and hypothesis-testing, through to post-estimation diagnostics. The basic statistics needed to follow the book are covered in an appendix, making the book a flexible and self-contained learning resource.

Table of Content:

Part I: Basics of linear regression
1. The linear regression model: an overview
1.1 The linear regression model
1.2 The nature and sources of data
1.3 Estimation of the linear regression model
1.4 The classical linear regression model (CLRM)
1.5 Variances and standard errors of OLS estimators
1.6 Testing hypotheses about the true or population regression coefficients
1.7 R2: a measure of goodness of fit of the estimated regression
1.8 An illustrative example: the determinants of hourly wages
1.9 Forecasting
1.10 The road ahead
Exercises
Appendix: The method of maximum likelihood (ML)
2. Functional forms of regression models
2.1 Log-linear, double log or constant elasticity models
2.2 Testing validity of linear restrictions
2.3 Log-lin or growth models
2.4 Lin-log models
2.5 Reciprocal models
2.6 Polynomial regression models
2.7 Choice of the functional form
2.8 Comparing linear and log-linear models
2.9 Regression on standardized variables
2.10 Regression through the origin: the zero-intercept model
2.11 Measures of goodness of fit
2.12 Summary and conclusions
Exercises
3. Qualitative explanatory variables regression models
3.1 Wage function revisited
3.2 Refinement of the wage function
3.3 Another refinement of the wage function
3.4 Functional form of the wage regression
3.5 Use of dummy variables in structural change
3.6 Use of dummy variables in seasonal data
3.7 Expanded sales function
3.8 Piecewise linear regression
3.9 Summary and conclusions
Exercises
Part II : Regression diagnostics
4. Regression diagnostic I: multicollinearity
4.1 Consequences of imperfect collinearity
4.2 An example: married women’s hours of work in the labor market
4.3 Detection of multicollinearity
4.4 Remedial measures
4.5 The method of principal components (PC)
4.6 Summary and conclusions
Exercises
5. Regression diagnostic II: heteroscedasticity
5.1 Consequences of heteroscedasticity
5.2 Abortion rates in the USA
5.3 Detection of heteroscedasticity
5.4 Remedial measures
5.5 Summary and conclusions
Exercises
6. Regression diagnostic III: autocorrelation
6.1 US consumption function, 1947–2000
6.2 Tests of autocorrelation
6.3 Remedial measures
6.4 Model evaluation
6.5 Summary and conclusions
Exercises
7. Regression diagnostic IV: model specification errors
7.1 Omission of relevant variables
7.2 Tests of omitted variables
7.3 Inclusion of irrelevant or unnecessary variables
7.4 Misspecification of the functional form of a regression model
7.5 Errors of measurement
7.6 Outliers, leverage and influence data
7.7 Probability distribution of the error term
7.8 Random or stochastic regressors
7.9 The simultaneity problem
7.10 Dynamic regression models
7.11 Summary and conclusions
Exercises
Appendix: Inconsistency of the OLS estimators of the consumption function
Part III : Topics in cross-section data
8. The logit and probit models
8.1 An illustrative example: to smoke or not to smoke
8.2 The linear probability model (LPM)
8.3 The logit model
8.4 The language of the odds ratio (OR)
8.5 The probit model
8.6 Summary and conclusions
Exercises
9. Multinomial regression models
9.1 The nature of multinomial regression models
9.2 Multinomial logit model (MLM): school choice
9.3 Conditional logit model (CLM)
9.4 Mixed logit (MXL)
9.5 Summary and conclusions
Exercises
10. Ordinal regression models
10.1 Ordered multinomial models (OMM)
10.2 Estimation of ordered logit model (OLM)
10.3 An illustrative example: attitudes toward working mothers
10.4 Limitation of the proportional odds model
10.5 Summary and conclusions
Exercises
Appendix: Derivation of Eq. (10.4)
11. Limited dependent variable regression models
11.1 Censored regression models
11.2 Maximum likelihood (ML) estimation of the censored regression model: the Tobit model
11.3 Truncated sample regression models
11.4 A concluding example
11.5 Summary and conclusions
Exercises
Appendix: Heckman’s (Heckit) selection-bias model
12. Modeling count data: the Poisson and negative binomial regression models
12.1 An illustrative example
12.2 The Poisson regression model (PRM)
12.3 Limitation of the Poisson regression model
12.4 The Negative Binomial Regression Model (NBRM)
12.5 Summary and conclusions
Exercises
Part IV : Time series econometrics
13. Stationary and nonstationary time series
13.1 Are exchange rates stationary?
13.2 The importance of stationary time series
13.3 Tests of stationarity
13.4 The unit root test of stationarity
13.5 Trend stationary vs. difference stationary time series
13.6 The random walk model (RWM)
13.7 Summary and conclusions
Exercises
14. Cointegration and error correction models
14.1 The phenomenon of spurious regression
14.2 Simulation of spurious regression
14.3 Is the regression of consumption expenditure on disposable income spurious?
14.4 When a spurious regression may not be spurious
14.5 Tests of cointegration
14.6 Cointegration and error correction mechanism (ECM)
14.7 Are 3-month and 6-month Treasury Bill rates cointegrated?
14.8 Summary and conclusions
Exercises
15. Asset price volatility: the ARCH and GARCH models
15.1 The ARCH model
15.2 The GARCH model
15.3 Further extensions of the ARCH model
15.4 Summary and conclusions
Exercises
16. Economic forecasting
16.1 Forecasting with regression models
16.2 The Box–Jenkins methodology: ARIMA modeling
16.3 An ARMA model of IBM daily closing prices, 3 January 2000 to 31 October 2002
16.4 Vector autoregression (VAR)
16.5 Testing causality using VAR: the Granger causality test
16.6 Summary and conclusions
Exercises
Appendix: Measures of forecast accuracy
Part V: Selected topics in econometrics
17. Panel data regression models
17.1 The importance of panel data
17.2 An illustrative example: charitable giving
17.3 Pooled OLS regression of charity function
17.4 The fixed effects least squares dummy variable (LSDV) model
17.5 Limitations of the fixed effects LSDV model
17.6 The fixed effect within group (WG) estimator
17.7 The random effects model (REM) or error components model (ECM)
17.8 Fixed effects model vs. random effects model
17.9 Properties of various estimators
17.10 Panel data regressions: some concluding comments
17.11 Summary and conclusions
Exercises
18. Survival analysis
18.1 An illustrative example: modeling recidivism duration
18.2 Terminology of survival analysis
18.3 Modeling recidivism duration
18.4 Exponential probability distribution
18.5 Weibull probability distribution
18.6 The proportional hazard model
18.7 Summary and conclusions
Exercises
19. Stochastic regressors and the method of instrumental variables
19.1 The problem of endogeneity
19.2 The problem with stochastic regressors
19.3 Reasons for correlation between regressors and the error term
19.4 The method of instrumental variables
19.5 Monte Carlo simulation of IV
19.6 Some illustrative examples
19.7 A numerical example: earnings and educational attainment of youth in the USA
19.8 Hypothesis testing under IV estimation
19.9 Test of endogeneity of a regressor
19.10 How to find whether an instrument is weak or strong
19.11 The case of multiple instruments
19.12 Regression involving more than one endogenous regressor
19.13 Summary and conclusions
Exercises
20. Beyond OLS: quantile regression
20.1 Quantiles
20.2 The quantile regression model (QRM)
20.3 The quantile wage regression model
20.4 Median wage regression
20.5 Wage regressions for 25%, 50% and 75% quantiles
20.6 Test of coefficient equality of different quantiles
20.7 Summary of OLS and 25th, 50th (median) and 75th quantile regressions
20.8 Quantile regressions in Eviews 8
20.9 Summary and conclusions
Exercises
Appendix: The mechanics of quantile regression
21. Multivariate regression models
21.1 Some examples of MRMs
21.2 Advantages of joint estimation
21.3 An illustrative example of MRM estimation with the same explanatory variables
21.4 Estimation of MRM
21.5 Other advantages of MRM
21.6 Some technical aspects of MRM
21.7 Seemingly Unrelated Regression Equations (SURE)
21.8 Summary and conclusions
Exercises
Appendix
Appendices
1. Data sets used in the text
2. Statistical appendix
A.1 Summation notation
A.2 Experiments
A.3 Empirical definition of probability
A.4 Probabilities: properties, rules, and definitions
A.5 Probability distributions of random variables
A.6 Expected value and variance
A.7 Covariance and correlation coefficient
A.8 Normal distribution
A.9 Student’s t distribution
A.10 Chi-square (??2) distribution
A.11 F distribution
A.12 Statistical inference
Exercises
Exponential and logarithmic functions
Index

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