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_aHB139 _b.A46 2009 |
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_a330.0285555 _222 |
100 | 1 |
_aAjmani, Vivek B., _eauthor. _91074556 |
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245 | 1 | 0 |
_aApplied econometrics using the SAS system / _cVivek B. Ajmani. |
264 | 1 |
_aHoboken, N.J. : _bJohn Wiley, _c[2009] |
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264 | 4 | _c©2009 | |
300 |
_axv, 311 pages : _billustrations ; _c28 cm |
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336 |
_atext _btxt _2rdacontent |
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337 |
_aunmediated _bn _2rdamedia |
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338 |
_avolume _bnc _2rdacarrier |
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504 | _aIncludes bibliographical references (pages 299-301) and index. | ||
505 | 0 | 0 |
_g1. _tIntroduction to Regression Analysis -- _g1.1. _tIntroduction -- _g1.2. _tMatrix Form of the Multiple Regression Model -- _g1.3. _tBasic Theory of Least Squares -- _g1.4. _tAnalysis of Variance -- _g1.5. _tThe Frisch - Waugh Theorem -- _g1.6. _tGoodness of Fit -- _g1.7. _tHypothesis Testing and Confidence Intervals -- _g1.8. _tSome Further Notes -- _g2. _tRegression Analysis Using Proc IML and Proc Reg -- _g2.1. _tIntroduction -- _g2.2. _tRegression Analysis Using Proc IML -- _g2.3. _tAnalyzing the Data Using Proc Reg -- _g2.4. _tExtending the Investment Equation Model to the Complete Data Set -- _g2.5. _tPlotting the Data -- _g2.6. _tCorrelation Between Variables -- _g2.7. _tPredictions of the Dependent Variable -- _g2.8. _tResidual Analysis -- _g2.9. _tMulticollinearity -- _g3. _tHypothesis Testing -- _g3.1. _tIntroduction -- _g3.2. _tUsing SAS to Conduct the General Linear Hypothesis -- _g3.3. _tThe Restricted Least Squares Estimator -- _g3.4. _tAlternative Methods of Testing the General Linear Hypothesis -- _g3.5. _tTesting for Structural Breaks in Data -- _g3.6. _tThe CUSUM Test -- _g3.7. _tModels with Dummy Variables -- _g4. _tInstrumental Variables -- _g4.1. _tIntroduction -- _g4.2. _tOmitted Variable Bias -- _g4.3. _tMeasurement Errors -- _g4.4. _tInstrumental Variable Estimation -- _g4.5. _tSpecification Tests -- _g5. _tNonspherical Disturbances and Heteroscedasticity -- _g5.1. _tIntroduction -- _g5.2. _tNonspherical Disturbances -- _g5.3. _tDetecting Heteroscedasticity -- _g5.4. _tFormal Hypothesis Tests to Detect Heteroscedasticity -- _g5.5. _tEstimation of b Revisited -- _g5.6. _tWeighted Least Squares and FGLS Estimation -- _g5.7. _tAutoregressive Conditional Heteroscedasticity -- _g6. _tAutocorrelation -- _g6.1. _tIntroduction -- _g6.2. _tProblems Associated with OLS Estimation Under Autocorrelation -- _g6.3. _tEstimation Under the Assumption of Serial Correlation -- _g6.4. _tDetecting Autocorrelation -- _g6.5. _tUsing SAS to Fit the AR Models -- _g7. _tPanel Data Analysis -- _g7.1. _tWhat is Panel Data? -- _g7.2. _tPanel Data Models -- _g7.3. _tThe Pooled Regression Model -- _g7.4. _tThe Fixed Effects Model -- _g7.5. _tRandom Effects Models -- _g8. _tSystems of Regression Equations -- _g8.1. _tIntroduction -- _g8.2. _tEstimation Using Generalized Least Squares -- _g8.3. _tSpecial Cases of the Seemingly Unrelated Regression Model -- _g8.4. _tFeasible Generalized Least Squares -- _g9. _tSimultaneous Equations -- _g9.1. _tIntroduction -- _g9.2. _tProblems with OLS Estimation -- _g9.3. _tStructural and Reduced Form Equations -- _g9.4. _tThe Problem of Identification -- _g9.5. _tEstimation of Simultaneous Equation Models -- _g9.6. _tHausman's Specification Test -- _g10. _tDiscrete Choice Models -- _g10.1. _tIntroduction -- _g10.2. _tBinary Response Models -- _g10.3. _tPoisson Regression -- _g11. _tDuration Analysis -- _g11.1. _tIntroduction -- _g11.2. _tFailure Times and Censoring -- _g11.3. _tThe Survival and Hazard Functions -- _g11.4. _tCommonly Used Distribution Functions in Duration Analysis -- _g11.5. _tRegression Analysis with Duration Data -- _g12. _tSpecial Topics -- _g12.1. _tIterative FGLS Estimation Under Heteroscedasticity -- _g12.2. _tMaximum Likelihood Estimation Under Heteroscedasticity -- _g12.3. _tHarvey's Multiplicative Heteroscedasticity -- _g12.4. _tGroupwise Heteroscedasticity -- _g12.5. _tHausman - Taylor Estimator for the Random Effects Model -- _g12.6. _tRobust Estimation of Covariance Matrices in Panel Data -- _g12.7. _tDynamic Panel Data Models -- _g12.8. _tHeterogeneity and Autocorrelation in Panel Data Models -- _g12.9. _tAutocorrelation in Panel Data -- _gAppendix A. _tBasic Matrix Algebra for Econometrics -- _tB.1 Assigning Scalars -- _gAppendix C. _tSimulating the Large Sample Properties of the OLS Estimators -- _gAppendix D. _tIntroduction to Bootstrap Estimation -- _gAppendix E. _tComplete Programs and Proc IML Routines. |
520 | _a"The first cutting-edge guide to using the SAS system for the analysis of econometric data Applied Econometrics Using the SAS System is the first book of its kind to treat the analysis of basic econometric data using SAS, one of the most commonly used software tools among today's statisticians in business and industry. This book thoroughly examines econometric methods and discusses how data collected in economic studies can easily be analyzed using the SAS system. In addition to addressing the computational aspects of econometric data analysis, the author provides a statistical foundation by introducing the underlying theory behind each method before delving into the related SAS routines ."--Publisher's website. | ||
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_aEconometrics _xComputer programs _9769759 |
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