- Book
- 534 p. : ill. ; 25 cm.
- 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.
- 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.
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Find it Basement | |
CB158 .S54 2012 | Unknown |
CB158 .S54 2012 | Unknown |
CB158 .S54 2012 | Unknown |
CB158 .S54 2012 | Unknown |
Find it On reserve: Ask at circulation desk | |
CB158 .S54 2012 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
2. Introduction to econometrics [2011]
- Book
- xlii, 785 p. : ill. ; 24 cm.
- Economic questions and data
- Review of probability
- Review of statistics
- Linear regression with one regressor
- Regression with a single regressor : hypothesis tests and confidence intervals
- Linear regression with multiple regressors
- Hypothesis tests and confidence intervals in multiple regression
- Nonlinear regression functions
- Assessing studies based on multiple regression
- Regression with panel data
- Regression with a binary dependent variable
- Instrumental variables regression
- Experiments and quasi-experiments
- Introduction to time series regression and forecasting
- Estimation of dynamic causal effects
- Additional topics in time series regression
- The theory of linear regression with one regressor
- The theory of multiple regression.
(source: Nielsen Book Data)9781408264331 20160605
- Economic questions and data
- Review of probability
- Review of statistics
- Linear regression with one regressor
- Regression with a single regressor : hypothesis tests and confidence intervals
- Linear regression with multiple regressors
- Hypothesis tests and confidence intervals in multiple regression
- Nonlinear regression functions
- Assessing studies based on multiple regression
- Regression with panel data
- Regression with a binary dependent variable
- Instrumental variables regression
- Experiments and quasi-experiments
- Introduction to time series regression and forecasting
- Estimation of dynamic causal effects
- Additional topics in time series regression
- The theory of linear regression with one regressor
- The theory of multiple regression.
(source: Nielsen Book Data)9781408264331 20160605
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Find it On reserve: Ask at circulation desk | |
HB139 .S765 2011 | Unknown 2-hour loan |
Find it Permanent reserve: Ask at circulation desk | |
HB139 .S765 2011 | Unknown |
HB139 .S765 2011 | Unknown |
LAW-243-01, LAW-7512-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Course
- LAW-7512-01 -- Statistical Inference in Law
- Instructor(s)
- Ho, Daniel E.
3. Basic econometrics [2009]
- Book
- xx, 922 p. : ill. ; 26 cm.
- Part I: Single-Equation Regression Model Chapter 1: The Nature of Regression Analysis Chapter 2: Two-Variable 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: Two-Variable Regression: Interval Estimation and Hypothesis Testing Chapter 6: Extensions of the Two-Variable 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 Distributed-Lag Models. Part IV: Simultaneous-Equation Models Chapter 18: Simultaneous-Equation Models. Chapter 19: The Identification Problem. Chapter 20: Simultaneous-Equation 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: Single-Equation Regression Model 1: The Nature of Regression Analysis 2: Two-Variable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the Two-Variable 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 Distributed-Lag Models. Part IV: Simultaneous-Equation Models 18: Simultaneous-Equation Models. 19: The Identification Problem. 20: Simultaneous-Equation 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
- Part I: Single-Equation Regression Model Chapter 1: The Nature of Regression Analysis Chapter 2: Two-Variable 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: Two-Variable Regression: Interval Estimation and Hypothesis Testing Chapter 6: Extensions of the Two-Variable 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 Distributed-Lag Models. Part IV: Simultaneous-Equation Models Chapter 18: Simultaneous-Equation Models. Chapter 19: The Identification Problem. Chapter 20: Simultaneous-Equation 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: Single-Equation Regression Model 1: The Nature of Regression Analysis 2: Two-Variable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the Two-Variable 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 Distributed-Lag Models. Part IV: Simultaneous-Equation Models 18: Simultaneous-Equation Models. 19: The Identification Problem. 20: Simultaneous-Equation 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
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Find it Basement | |
HB139 .G84 2009 | Unknown |
Find it On reserve: Ask at circulation desk | |
HB139 .G84 2009 | Unknown 2-hour loan |
Find it Permanent reserve: Ask at circulation desk | |
HB139 .G84 2009 | Unknown |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
4. A guide to econometrics [2008]
- Book
- xii, 585 p. : ill. ; 26 cm.
- 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 Single-equation 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 Pre-test 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 Observation-specific 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 Error-correction 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 Non-parametric 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 Even-numbered Questions.Glossary.Bibliography.Name Index.Subject Index.
- (source: Nielsen Book Data)9781405182584 20160528
(source: Nielsen Book Data)9781405182584 20160528
- 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 Single-equation 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 Pre-test 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 Observation-specific 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 Error-correction 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 Non-parametric 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 Even-numbered Questions.Glossary.Bibliography.Name Index.Subject Index.
- (source: Nielsen Book Data)9781405182584 20160528
(source: Nielsen Book Data)9781405182584 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Find it On reserve: Ask at circulation desk | |
HB139 .K45 2008 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
5. Bayesian econometrics [2003]
- Book
- xiv, 359 p. : ill. ; 25 cm.
- Preface.1. An Overview of Bayesian Econometrics.2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables.4. The Normal Linear Regression Model with Other Priors.5. The Nonlinear Regression Model.6. The Linear Regression Model with General Error Covariance Matrix.7. The Linear Regression Model with Panel Data.8. Introduction to Time Series: State Space Models.9. Qualitative and Limited Dependent Variable Models.10. Flexible Models: Nonparametric and Semi-Parametric Methods.11. Bayesian Model Averaging.12. Other Models, Methods and Issues.Appendix A: Introduction to Matrix Algebra.Appendix B: Introduction to Probability and Statistics.Bibliography.Index.
- (source: Nielsen Book Data)9780470845677 20160528
(source: Nielsen Book Data)9780470845677 20160528
- Preface.1. An Overview of Bayesian Econometrics.2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables.4. The Normal Linear Regression Model with Other Priors.5. The Nonlinear Regression Model.6. The Linear Regression Model with General Error Covariance Matrix.7. The Linear Regression Model with Panel Data.8. Introduction to Time Series: State Space Models.9. Qualitative and Limited Dependent Variable Models.10. Flexible Models: Nonparametric and Semi-Parametric Methods.11. Bayesian Model Averaging.12. Other Models, Methods and Issues.Appendix A: Introduction to Matrix Algebra.Appendix B: Introduction to Probability and Statistics.Bibliography.Index.
- (source: Nielsen Book Data)9780470845677 20160528
(source: Nielsen Book Data)9780470845677 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Find it Basement | |
HB141 .K6443 2003 | Unknown |
HB141 .K6443 2003 | Unknown |
Find it On reserve: Ask at circulation desk | |
HB141 .K6443 2003 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank