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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.
Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger. Drawing on his own groundbreaking work, Silver examines the world of prediction.
Law Library (Crown)
LAW-243-01
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.
For courses in introductory econometrics. An approach to modern econometrics theory and practice through engaging applications. Ensure students grasp the relevance of econometrics with Introduction to Econometrics--the text that connects modern theory and practice with engaging applications. The third edition builds on the philosophy that applications should drive the theory, not the other way around, while maintaining a focus on currency.
(source: Nielsen Book Data)9781408264331 20160605
Law Library (Crown)
LAW-243-01, LAW-7512-01

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
Law Library (Crown)
LAW-243-01
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
This is the perfect (and essential) supplement for all econometrics classes - from a rigorous first undergraduate course, to a first master's, to a PhD course. This book explains what is going on in textbooks full of proofs and formulas. It offers intuition, skepticism, insights, humor, and practical advice (dos and don'ts). It contains new chapters that cover instrumental variables and computational considerations. It includes additional information on GMM, nonparametrics, and an introduction to wavelets.
(source: Nielsen Book Data)9781405182584 20160528
Law Library (Crown)
LAW-243-01
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
Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.
(source: Nielsen Book Data)9780470845677 20160528
Law Library (Crown)
LAW-243-01