Econometrics by Example 2nd Edition Gujarati Solutions Manual

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Product Details:

  • 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:

  1. Part I: Basics of linear regression
  2. 1. The linear regression model: an overview
  3. 1.1 The linear regression model
  4. 1.2 The nature and sources of data
  5. 1.3 Estimation of the linear regression model
  6. 1.4 The classical linear regression model (CLRM)
  7. 1.5 Variances and standard errors of OLS estimators
  8. 1.6 Testing hypotheses about the true or population regression coefficients
  9. 1.7 R2: a measure of goodness of fit of the estimated regression
  10. 1.8 An illustrative example: the determinants of hourly wages
  11. 1.9 Forecasting
  12. 1.10 The road ahead
  13. Exercises
  14. Appendix: The method of maximum likelihood (ML)
  15. 2. Functional forms of regression models
  16. 2.1 Log-linear, double log or constant elasticity models
  17. 2.2 Testing validity of linear restrictions
  18. 2.3 Log-lin or growth models
  19. 2.4 Lin-log models
  20. 2.5 Reciprocal models
  21. 2.6 Polynomial regression models
  22. 2.7 Choice of the functional form
  23. 2.8 Comparing linear and log-linear models
  24. 2.9 Regression on standardized variables
  25. 2.10 Regression through the origin: the zero-intercept model
  26. 2.11 Measures of goodness of fit
  27. 2.12 Summary and conclusions
  28. Exercises
  29. 3. Qualitative explanatory variables regression models
  30. 3.1 Wage function revisited
  31. 3.2 Refinement of the wage function
  32. 3.3 Another refinement of the wage function
  33. 3.4 Functional form of the wage regression
  34. 3.5 Use of dummy variables in structural change
  35. 3.6 Use of dummy variables in seasonal data
  36. 3.7 Expanded sales function
  37. 3.8 Piecewise linear regression
  38. 3.9 Summary and conclusions
  39. Exercises
  40. Part II : Regression diagnostics
  41. 4. Regression diagnostic I: multicollinearity
  42. 4.1 Consequences of imperfect collinearity
  43. 4.2 An example: married women’s hours of work in the labor market
  44. 4.3 Detection of multicollinearity
  45. 4.4 Remedial measures
  46. 4.5 The method of principal components (PC)
  47. 4.6 Summary and conclusions
  48. Exercises
  49. 5. Regression diagnostic II: heteroscedasticity
  50. 5.1 Consequences of heteroscedasticity
  51. 5.2 Abortion rates in the USA
  52. 5.3 Detection of heteroscedasticity
  53. 5.4 Remedial measures
  54. 5.5 Summary and conclusions
  55. Exercises
  56. 6. Regression diagnostic III: autocorrelation
  57. 6.1 US consumption function, 1947–2000
  58. 6.2 Tests of autocorrelation
  59. 6.3 Remedial measures
  60. 6.4 Model evaluation
  61. 6.5 Summary and conclusions
  62. Exercises
  63. 7. Regression diagnostic IV: model specification errors
  64. 7.1 Omission of relevant variables
  65. 7.2 Tests of omitted variables
  66. 7.3 Inclusion of irrelevant or unnecessary variables
  67. 7.4 Misspecification of the functional form of a regression model
  68. 7.5 Errors of measurement
  69. 7.6 Outliers, leverage and influence data
  70. 7.7 Probability distribution of the error term
  71. 7.8 Random or stochastic regressors
  72. 7.9 The simultaneity problem
  73. 7.10 Dynamic regression models
  74. 7.11 Summary and conclusions
  75. Exercises
  76. Appendix: Inconsistency of the OLS estimators of the consumption function
  77. Part III : Topics in cross-section data
  78. 8. The logit and probit models
  79. 8.1 An illustrative example: to smoke or not to smoke
  80. 8.2 The linear probability model (LPM)
  81. 8.3 The logit model
  82. 8.4 The language of the odds ratio (OR)
  83. 8.5 The probit model
  84. 8.6 Summary and conclusions
  85. Exercises
  86. 9. Multinomial regression models
  87. 9.1 The nature of multinomial regression models
  88. 9.2 Multinomial logit model (MLM): school choice
  89. 9.3 Conditional logit model (CLM)
  90. 9.4 Mixed logit (MXL)
  91. 9.5 Summary and conclusions
  92. Exercises
  93. 10. Ordinal regression models
  94. 10.1 Ordered multinomial models (OMM)
  95. 10.2 Estimation of ordered logit model (OLM)
  96. 10.3 An illustrative example: attitudes toward working mothers
  97. 10.4 Limitation of the proportional odds model
  98. 10.5 Summary and conclusions
  99. Exercises
  100. Appendix: Derivation of Eq. (10.4)
  101. 11. Limited dependent variable regression models
  102. 11.1 Censored regression models
  103. 11.2 Maximum likelihood (ML) estimation of the censored regression model: the Tobit model
  104. 11.3 Truncated sample regression models
  105. 11.4 A concluding example
  106. 11.5 Summary and conclusions
  107. Exercises
  108. Appendix: Heckman’s (Heckit) selection-bias model
  109. 12. Modeling count data: the Poisson and negative binomial regression models
  110. 12.1 An illustrative example
  111. 12.2 The Poisson regression model (PRM)
  112. 12.3 Limitation of the Poisson regression model
  113. 12.4 The Negative Binomial Regression Model (NBRM)
  114. 12.5 Summary and conclusions
  115. Exercises
  116. Part IV : Time series econometrics
  117. 13. Stationary and nonstationary time series
  118. 13.1 Are exchange rates stationary?
  119. 13.2 The importance of stationary time series
  120. 13.3 Tests of stationarity
  121. 13.4 The unit root test of stationarity
  122. 13.5 Trend stationary vs. difference stationary time series
  123. 13.6 The random walk model (RWM)
  124. 13.7 Summary and conclusions
  125. Exercises
  126. 14. Cointegration and error correction models
  127. 14.1 The phenomenon of spurious regression
  128. 14.2 Simulation of spurious regression
  129. 14.3 Is the regression of consumption expenditure on disposable income spurious?
  130. 14.4 When a spurious regression may not be spurious
  131. 14.5 Tests of cointegration
  132. 14.6 Cointegration and error correction mechanism (ECM)
  133. 14.7 Are 3-month and 6-month Treasury Bill rates cointegrated?
  134. 14.8 Summary and conclusions
  135. Exercises
  136. 15. Asset price volatility: the ARCH and GARCH models
  137. 15.1 The ARCH model
  138. 15.2 The GARCH model
  139. 15.3 Further extensions of the ARCH model
  140. 15.4 Summary and conclusions
  141. Exercises
  142. 16. Economic forecasting
  143. 16.1 Forecasting with regression models
  144. 16.2 The Box–Jenkins methodology: ARIMA modeling
  145. 16.3 An ARMA model of IBM daily closing prices, 3 January 2000 to 31 October 2002
  146. 16.4 Vector autoregression (VAR)
  147. 16.5 Testing causality using VAR: the Granger causality test
  148. 16.6 Summary and conclusions
  149. Exercises
  150. Appendix: Measures of forecast accuracy
  151. Part V: Selected topics in econometrics
  152. 17. Panel data regression models
  153. 17.1 The importance of panel data
  154. 17.2 An illustrative example: charitable giving
  155. 17.3 Pooled OLS regression of charity function
  156. 17.4 The fixed effects least squares dummy variable (LSDV) model
  157. 17.5 Limitations of the fixed effects LSDV model
  158. 17.6 The fixed effect within group (WG) estimator
  159. 17.7 The random effects model (REM) or error components model (ECM)
  160. 17.8 Fixed effects model vs. random effects model
  161. 17.9 Properties of various estimators
  162. 17.10 Panel data regressions: some concluding comments
  163. 17.11 Summary and conclusions
  164. Exercises
  165. 18. Survival analysis
  166. 18.1 An illustrative example: modeling recidivism duration
  167. 18.2 Terminology of survival analysis
  168. 18.3 Modeling recidivism duration
  169. 18.4 Exponential probability distribution
  170. 18.5 Weibull probability distribution
  171. 18.6 The proportional hazard model
  172. 18.7 Summary and conclusions
  173. Exercises
  174. 19. Stochastic regressors and the method of instrumental variables
  175. 19.1 The problem of endogeneity
  176. 19.2 The problem with stochastic regressors
  177. 19.3 Reasons for correlation between regressors and the error term
  178. 19.4 The method of instrumental variables
  179. 19.5 Monte Carlo simulation of IV
  180. 19.6 Some illustrative examples
  181. 19.7 A numerical example: earnings and educational attainment of youth in the USA
  182. 19.8 Hypothesis testing under IV estimation
  183. 19.9 Test of endogeneity of a regressor
  184. 19.10 How to find whether an instrument is weak or strong
  185. 19.11 The case of multiple instruments
  186. 19.12 Regression involving more than one endogenous regressor
  187. 19.13 Summary and conclusions
  188. Exercises
  189. 20. Beyond OLS: quantile regression
  190. 20.1 Quantiles
  191. 20.2 The quantile regression model (QRM)
  192. 20.3 The quantile wage regression model
  193. 20.4 Median wage regression
  194. 20.5 Wage regressions for 25%, 50% and 75% quantiles
  195. 20.6 Test of coefficient equality of different quantiles
  196. 20.7 Summary of OLS and 25th, 50th (median) and 75th quantile regressions
  197. 20.8 Quantile regressions in Eviews 8
  198. 20.9 Summary and conclusions
  199. Exercises
  200. Appendix: The mechanics of quantile regression
  201. 21. Multivariate regression models
  202. 21.1 Some examples of MRMs
  203. 21.2 Advantages of joint estimation
  204. 21.3 An illustrative example of MRM estimation with the same explanatory variables
  205. 21.4 Estimation of MRM
  206. 21.5 Other advantages of MRM
  207. 21.6 Some technical aspects of MRM
  208. 21.7 Seemingly Unrelated Regression Equations (SURE)
  209. 21.8 Summary and conclusions
  210. Exercises
  211. Appendix
  212. Appendices
  213. 1. Data sets used in the text
  214. 2. Statistical appendix
  215. A.1 Summation notation
  216. A.2 Experiments
  217. A.3 Empirical definition of probability
  218. A.4 Probabilities: properties, rules, and definitions
  219. A.5 Probability distributions of random variables
  220. A.6 Expected value and variance
  221. A.7 Covariance and correlation coefficient
  222. A.8 Normal distribution
  223. A.9 Student’s t distribution
  224. A.10 Chi-square (??2) distribution
  225. A.11 F distribution
  226. A.12 Statistical inference
  227. Exercises
  228. Exponential and logarithmic functions
  229. Index

 

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