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 Silver, Nate, 1978
 New York : Penguin Press, 2012.
 Description
 Book — 534 p. : ill. ; 25 cm.
 Summary

 A catastrophic failure of prediction
 Are you smarter than a television pundit?
 All I care about is W's and L's
 For years you've been telling us that rain is green
 Desperately seeking signal
 How to drown in three feet of water
 Role models
 Less and less and less wrong
 Rage against the machines
 The poker bubble
 If you can't beat 'em
 A climate of healthy skepticism
 What you don't know can hurt you.
 Online
Law Library (Crown)
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CB158 .S54 2012  Unknown 
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LAW24301
 Course
 LAW24301  Bayesian Statistics and Econometrics
 Instructor(s)
 Strnad, James Frank
2. Basic econometrics [2009]
 Gujarati, Damodar N.
 5th ed.  Boston : McGrawHill Irwin, c2009.
 Description
 Book — xx, 922 p. : ill. ; 26 cm.
 Summary

 Part I: SingleEquation Regression Model Chapter 1: The Nature of Regression Analysis Chapter 2: TwoVariable Regression Analysis: Some Basic Ideas Chapter 3: Two Variable Regression Model: The Problem of Estimation Chapter 4: Classical Normal Linear Regression Model (CNLRM) Chapter 5: TwoVariable Regression: Interval Estimation and Hypothesis Testing Chapter 6: Extensions of the TwoVariable Linear Regression Model Chapter 7: Multiple Regression Analysis: The Problem of Estimation Chapter 8: Multiple Regression Analysis: The Problem of Inference Chapter 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model Chapter 10: Multicollinearity: What happens if the Regressor are Correlated Chapter 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? Chapter 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics Chapter 14: Nonlinear Regression Models Chapter 15: Qualitative Response Regression Models Chapter 16: Panel Data Regression Models Chapter 17: Dynamic Econometric Model: Autoregressive and DistributedLag Models. Part IV: SimultaneousEquation Models Chapter 18: SimultaneousEquation Models. Chapter 19: The Identification Problem. Chapter 20: SimultaneousEquation Methods. Chapter 21: Time Series Econometrics: Some Basic Concepts Chapter 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
 (source: Nielsen Book Data)9780071276252 20160528
 Part I: SingleEquation Regression Model 1: The Nature of Regression Analysis 2: TwoVariable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: TwoVariable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the TwoVariable Linear Regression Model 7: Multiple Regression Analysis: The Problem of Estimation 8: Multiple Regression Analysis: The Problem of Inference 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model 10: Multicollinearity: What happens if the Regressor are Correlated 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics 14: Nonlinear Regression Models 15: Qualitative Response Regression Models 16: Panel Data Regression Models 17: Dynamic Econometric Model: Autoregressive and DistributedLag Models. Part IV: SimultaneousEquation Models 18: SimultaneousEquation Models. 19: The Identification Problem. 20: SimultaneousEquation Methods. 21: Time Series Econometrics: Some Basic Concepts 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
 (source: Nielsen Book Data)9780073375779 20160528
 Online
Law Library (Crown)
Law Library (Crown)  Status 

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HB139 .G84 2009  Unknown 
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LAW24301
 Course
 LAW24301  Bayesian Statistics and Econometrics
 Instructor(s)
 Strnad, James Frank
3. A guide to econometrics [2008]
 Kennedy, Peter, 19432010
 6th ed.  Malden, MA : Blackwell Pub., 2008.
 Description
 Book — xii, 585 p. : ill. ; 26 cm.
 Summary

 Preface.Dedication.1. Introduction.1.1 What is Econometrics?.1.2 The Disturbance Term.1.3 Estimates and Estimators.1.4 Good and Preferred Estimators.General Notes.Technical Notes.2. Criteria for Estimators.2.1 Introduction.2.2 Computational Cost.2.3 Least Squares.2.4 Highest R2.2.5 Unbiasedness.2.6 Efficiency.2.7 Mean Square Error (MSE).2.8 Asymptotic Properties.2.9 Maximum Likelihood.2.10 Monte Carlo Studies.2.11 Adding Up.General Notes.Technical Notes.3. The Classical Linear Regression Model.3.1 Textbooks as Catalogs.3.2 The Five Assumptions.3.3 The OLS Estimator in the CLR Model.General Notes.Technical Notes.4. Interval Estimation and Hypothesis Testing.4.1 Introduction.4.2 Testing a Single Hypothesis: the t Test.4.3 Testing a Joint Hypothesis: the F Test.4.4 Interval Estimation for a Parameter Vector.4.5 LR, W, and LM Statistics.4.6 Bootstrapping.General Notes.Technical Notes.5. Specification.5.1 Introduction.5.2 Three Methodologies.5.3 General Principles for Specification.5.4 Misspecification Tests/Diagnostics.5.5 R2 Again.General Notes.Technical Notes.6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy.6.1 Introduction.6.2 Incorrect Set of Independent Variables.6.3 Nonlinearity.6.4 Changing Parameter Values.General Notes.Technical Notes.7. Violating Assumption Two: Nonzero Expected Disturbance.General Notes.8. Violating Assumption Three: Nonspherical Disturbances.8.1 Introduction.8.2 Consequences of Violation.8.3 Heteroskedasticity.8.4 Autocorrelated Disturbances.8.5 Generalized Method of Moments.General Notes.Technical Notes.9. Violating Assumption Four: Instrumental Variable Estimation.9.1 Introduction.9.2 The IV Estimator.9.3 IV Issues.General Notes.Technical Notes.10. Violating Assumption Four: Measurement Errors and Autoregression.10.1 Errors in Variables.10.2 Autoregression.General Notes.Technical Notes.11. Violating Assumption Four: Simultaneous Equations.11.1 Introduction.11.2 Identification.11.3 Singleequation Methods.11.4 Systems Methods.General Notes.Technical Notes.12. Violating Assumption Five: Multicollinearity.12.1 Introduction.12.2 Consequences.12.3 Detecting Multicollinearity.12.4 What to Do.General Notes.Technical Notes.13. Incorporating Extraneous Information.13.1 Introduction.13.2 Exact Restrictions.13.3 Stochastic Restrictions.13.4 Pretest Estimators.13.5 Extraneous Information and MSE.General Notes.Technical Notes.14. The Bayesian Approach.14.1 Introduction.14.2 What Is a Bayesian Analysis?.14.3 Advantages of the Bayesian Approach.14.4 Overcoming Practitioners' Complaints.General Notes.Technical Notes.15. Dummy Variables.15.1 Introduction.15.2 Interpretation.15.3 Adding Another Qualitative Variable.15.4 Interacting with Quantitative Variables.15.5 Observationspecific Dummies.General Notes.Technical Notes.16. Qualitative Dependent Variables.16.1 Dichotomous Dependent Variables.16.2 Polychotomous Dependent Variables.16.3 Ordered Logit/Probit.16.4 Count Data.General Notes.Technical Notes.17. Limited Dependent Variables.17.1 Introduction.17.2 The Tobit Model.17.3 Sample Selection.17.4 Duration Models.General Notes.Technical Notes.18. Panel Data.18.1 Introduction.18.2 Allowing for Different Intercepts.18.3 Fixed versus Random Effects.18.4 Short Run versus Long Run.18.5 Long, Narrow Panels.General Notes.Technical Notes.19. Time Series Econometrics.19.1 Introduction.19.2 ARIMA Models.19.3 VARs.19.4 Errorcorrection Models.19.5 Testing for Unit Roots.19.6 Cointegration.General Notes.Technical Notes.20. Forecasting.20.1 Introduction.20.2 Causal Forecasting/Econometric Models.20.3 Time Series Analysis.20.4 Forecasting Accuracy.General Notes.Technical Notes.21. Robust Estimation.21.1 Introduction.21.2 Outliers and Influential Observations.21.3 Guarding Against Influential Observations.21.4 Artificial Neural Networks.21.5 Nonparametric Estimation.General Notes.Technical Notes.22. Applied Econometrics.22.1 Introduction.22.2 The Ten Commandments of Applied.Econometrics.22.3 Getting the Wrong Sign.22.4 Common Mistakes.22.5 What Do Practitioners Need to Know?.General Notes.Technical Notes.23. Computational Considerations.23.1 Introduction.23.2 Optimizing via a Computer Search.23.3 Estimating Integrals via Simulation.23.4 Drawing Observations from Awkward Distributions.General Notes.Technical Notes.Appendix A: Sampling Distributions, the.Foundation of Statistics.Appendix B: All about Variance.Appendix C: A Primer on Asymptotics.Appendix D: Exercises.Appendix E: Answers to Evennumbered Questions.Glossary.Bibliography.Name Index.Subject Index.
 (source: Nielsen Book Data)9781405182584 20160528
(source: Nielsen Book Data)9781405182584 20160528
 Online
Law Library (Crown)
Law Library (Crown)  Status 

Find it On reserve: Ask at circulation desk  
HB139 .K45 2008  Unknown 2hour loan 
LAW24301
 Course
 LAW24301  Bayesian Statistics and Econometrics
 Instructor(s)
 Strnad, James Frank