Understanding Econometrics (Registro n. 3511)

006 - Campo Fixo - Material Adicional
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007 - Campo Fixo - Descrição Física
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008 - Campo de Tamanho Fixo
Campo fixo de controle local 220519b2005 us d|||gr|||| 001 0 eng u
020 ## - ISBN
ISBN 0030348064
040 ## - Fonte da Catalogação
Fonte de catalogação BR-BrCADE
090 ## - Número de Chamada
Localização na estante 330.015195 H157u
Cutter H157u
100 1# - Autor
Autor HALCOUSSIS, Dennis
245 10 - Titulo Principal
Título principal Understanding Econometrics
260 ## - Local, Editora e Data
Cidade Mason, Ohio:
Editora Thompson South-Western,
Data 2005.
300 ## - Descrição Física
Número de páginas 332 p.
Ilustração il.
505 ## - Conteúdo
Conteúdo Preface<br/><br/>Chapter 1 An Introduction to Ordinary Least Squares <br/>1-1 You AIready Use Econometrics Every Day <br/>1-2 A Simple Regression Model: Collecting DVDs <br/>A Theoretical Regression Line <br/>The Error Terrn <br/>The Theoretical Regression Line Cannot Be Observed: It Must Be Esti,nared <br/>1-3 Ordinary Least Squares: The Best Way to Draw the Line <br/>Finding the OLS Slope and Intercept Estimates <br/>8 Total, Explained and Residual Sum of Squares <br/>Summary <br/>Exercises <br/>1-4 Appendix: Deriving OLS Estimates for a Simple Regression Model <br/><br/>Chapter 2 Ordinary Least Squares, Part 2 <br/>2-1 Multiple Regression Modeis: What Do the B's Mean? <br/>Estimating and Interpreting a Multiple Regression Model<br/>Degrees of Freedom<br/>2-2 Assumptions of the Classical Linear Regression Model <br/>2-3 Characteristics of Ordinary Least Squares <br/>Sampling Distribution of OLS Slope Estimates <br/>Properties of Estimators <br/>Gauss-Markov Theorem <br/>Estimating Variances lar me Error Term and Slope Estimates <br/>Summary <br/>Exercises<br/> <br/>Chapter 3 Commonly Used Statistics for Regression Analysis <br/>3-1 Hypothesis Testing: Do My Estimates Matter?<br/>Randon Samples <br/>Hypothesis Testing <br/>The Null and Alternative Hypotheses<br/>One-and Two-Sided Tests <br/>Levels of Significance <br/>3-2 Conducting a t-Test<br/>Critical Values and Decision Rules <br/>p-Value<br/>Confidence Intervals<br/>Statistical Significance Can Be Trivial<br/>3-3 F-Test of All Independent Variables<br/>3-4 Goodness of Fit: How Well Does It Work?<br/>R2<br/>Adjusted R2 <br/>Summary <br/>Exercises <br/><br/>Chapter 4 Basics in Conducting Econometric Research <br/>4-1 Choosing a Topic <br/>4-2 The Literature Review: What's Been Done Already<br/>4-3 Determining the Dependent and Independent Variables<br/>Change Is Good: Variables Should Vary<br/>Tautologv: A Perfect but Useless Regression <br/>Adjusting Time-Series Variables for Inflation <br/>Adjusting Cross-Section Variables for Population Size <br/>Variable Definitions and Slope Estimates<br/>When Independent Variables Are Omitted<br/>When Extra Independent Variables Are Added <br/>4-4 Objectivity in Econometrics<br/>4-5 Finding and Using Data<br/> Outliers <br/>4-6 Writing About Your Research<br/> Summary <br/> Exercises <br/><br/>Chapter 5 Additional Modeling Techniques <br/>5-1 Dummy Variables Aren't Stupid: When a Variable Is Not a Number <br/>Intercept Dummies <br/>Professional Wrestling Needs Dummies <br/>The Dummy Variable Trap: Alwavs Leave an Escape Route <br/>Seasonal Retail Sales Model <br/>Slope Dummies <br/>5-2 Interaction Variables Can't Leave Each Other Alone<br/>5-3 Designing Your Own F-Test <br/>F-Test Your Way to Riches <br/>Chow test: Testing for Identical Twin Regressions <br/>Gasoline Revenue and OPEC <br/>5-4 Polynomial Models: Curves Can Be Linear Regressions <br/>Sports Car Production Costs <br/>5-5 Log Models: Estimating Elasticities<br/>The Double-Log Model <br/>Estimating the Price Elasticity of Demand for Compact Discs<br/>The Semi log Model <br/>Summary <br/>Exercises <br/><br/>Chapter 6 Multicollinearity: When Independent Variables Have Relationships <br/>6-1 The Illness <br/>6-2 The Symptoms <br/>6-3 Measunng Multicollinearity <br/>Correlation Coefficients <br/>Regress One Independent Variable on Another <br/>Variance Inflation Factor <br/>6-4 Treating Multicollinearity <br/>Leave the Model Alone <br/>Eliminate an Independent Variable <br/>Redesign the Model <br/>Increase the Sample Size <br/>Summary <br/>Exercises <br/><br/>Chapter 7 Autocorrelation: A Problem with Time-Series Regressions<br/>7-1 The Illness <br/>7-2 The Symptoms <br/>7-3 Testing for the Illness: The Durbin-Watson Statistic <br/>7-4 Treating the Disease <br/>7-5 Treating the Symptoms <br/>The Cochrane-Orcutt Method<br/>The AR(1) Method <br/>Summary <br/>Exercises <br/><br/>Chapter 8 Heteroskedasticity: A Problem with Cross-Section Regressions<br/>8-1 The lllness <br/>8-2 The Symptoms <br/>8-3 Testing for the Illness: The Park Test and the White Test <br/>The Park Test <br/>The White Test <br/>8-4 Treating the Disease <br/>8-5 Treating the Symptoms <br/>Weighted Least Squares <br/>Correcting Standard Errors and t-Statistics for Heteroskedaricity <br/>Summary <br/>Exercises <br/><br/>Chapter 9 Pooling Data Across Time and Space <br/>9-1 Mixing the Data: Differences between Time and Space Disappear <br/>9-2 Seemingly Unrelated Regressions Are Actually Related <br/>9-3 The Fixed Effects Model: Everyone Deserve a Different Intercept <br/>9-4 The Random Effects Model: They All Make Their Own Errors <br/>9-5 A Comparison of SUR. Fixed Effects and Random Effects<br/>Summary <br/>Exercises <br/><br/>Chapter 10 Simultaneous-Equation Systems: When One Equation Is<br/>Not Enough <br/>10-1 A Two-Equation Model for Pizza <br/>10-2 The Identification Problem: How to Tell Supply from Demand <br/>The Order Condition<br/>10-3 Ordinary Least Squares Has Issues with Simultaneity <br/>10-4 Checking for Simultaneity with the Hausman Test <br/>10-5 Instrumental Variables: An Alternative for a Problem Variable <br/>Measurement Error <br/>10-6 Two-Stage Least Squares: An Orderly Approach to Instrumental<br/>Variables <br/>Summary <br/>Exercises <br/><br/>Chapter 11 Time-Series Models: Using the Past to Consider the Future <br/>11-1 Estimating Distributed Lag Models <br/>Distributed Lag Models <br/>The Koyck Lag Model <br/>Koyck Lag Models with Autocorrelation <br/>11-2 Autoregressive and Moving Average Models: Errors That Last Over Time <br/>Autoregressive Models <br/>Moving Average Models <br/>Auroregressive Moving Average Models <br/>11-3 Stationary Versus Nonstationary Series: Unit Roots Can Be Hard to Kill <br/>Keeping a Nonstationary Variable in Its Place <br/>Cointegration <br/>11-4 Forecasting: There Is No Crystal Ball <br/>Confidence Intervals for Forecasts <br/>Evaluating Forecasts <br/>Forecasting with Autocorrelation <br/>Forecasting with Simultaneous-Equation Models <br/>11-5 Testing for Causality: What Came First: the Chicken or the Egg? <br/>Summary <br/>Exercises <br/>11-6 Appendix: The Math Behind the Koyck Lag Model <br/><br/>Chapter 12 Qualitative Choice Models: The Dependent Variable Is a Dummy <br/>12-1 Binary Choice: The Dependent Variable Is O or 1 <br/>Linear Probability Model <br/>Probit<br/>Logit<br/>12-2 Multiple Choice: More than Two Possible Answers <br/>A Linear Probability Model for More than Two Choices <br/>Multinomial Logit <br/>12-3 An Overview of Censored and Truncated Data: Observations<br/>You Can't See Can Hurt You <br/>Censored Data <br/>Truncated Data <br/>Summary <br/>Exercises<br/>Chapter 13 Econome-"tricks": Misleading Uses of Econometrics <br/>13-1 Statistical Significance Does Not Prove Causality: Hendry's Theory of Inflation <br/>13-2 Different Combinations of Independent Variables Can Give Contradictory Results <br/>13-3 Extrapolation Can Stretch Things Too Far <br/>13-4 Connecting the Dots: Forcing the Regression Line to Fit the Data <br/>13-5 Small Sample Sizes Don't Give You Much Information <br/>13-6 Self-Selection: Individual Choice Determines Who Is in the Sample <br/>13-7 "Truth-in-Advertising" Is the Key to Honest Econometrics <br/>13-8 A Table of Econometric Situations and Problems <br/>Sumrnary <br/>Exercises <br/>Glossary <br/>Appendix of Statistical Tables<br/>Index <br/><br/><br/>
650 #0 - ASSUNTO
9 (RLIN) 2195
Assunto Econometria
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