Understanding Econometrics

por HALCOUSSIS, Dennis
[ Livros ] Publicado por : Thompson South-Western, (Mason, Ohio:) Detalhes físicos: 332 p. il. ISBN:0030348064.
Assunto(s): Econometria
Ano: 2005 Tipo de Material: Livros
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Chapter 1 An Introduction to Ordinary Least Squares
1-1 You AIready Use Econometrics Every Day
1-2 A Simple Regression Model: Collecting DVDs
A Theoretical Regression Line
The Error Terrn
The Theoretical Regression Line Cannot Be Observed: It Must Be Esti,nared
1-3 Ordinary Least Squares: The Best Way to Draw the Line
Finding the OLS Slope and Intercept Estimates
8 Total, Explained and Residual Sum of Squares
1-4 Appendix: Deriving OLS Estimates for a Simple Regression Model

Chapter 2 Ordinary Least Squares, Part 2
2-1 Multiple Regression Modeis: What Do the B's Mean?
Estimating and Interpreting a Multiple Regression Model
Degrees of Freedom
2-2 Assumptions of the Classical Linear Regression Model
2-3 Characteristics of Ordinary Least Squares
Sampling Distribution of OLS Slope Estimates
Properties of Estimators
Gauss-Markov Theorem
Estimating Variances lar me Error Term and Slope Estimates

Chapter 3 Commonly Used Statistics for Regression Analysis
3-1 Hypothesis Testing: Do My Estimates Matter?
Randon Samples
Hypothesis Testing
The Null and Alternative Hypotheses
One-and Two-Sided Tests
Levels of Significance
3-2 Conducting a t-Test
Critical Values and Decision Rules
Confidence Intervals
Statistical Significance Can Be Trivial
3-3 F-Test of All Independent Variables
3-4 Goodness of Fit: How Well Does It Work?
Adjusted R2

Chapter 4 Basics in Conducting Econometric Research
4-1 Choosing a Topic
4-2 The Literature Review: What's Been Done Already
4-3 Determining the Dependent and Independent Variables
Change Is Good: Variables Should Vary
Tautologv: A Perfect but Useless Regression
Adjusting Time-Series Variables for Inflation
Adjusting Cross-Section Variables for Population Size
Variable Definitions and Slope Estimates
When Independent Variables Are Omitted
When Extra Independent Variables Are Added
4-4 Objectivity in Econometrics
4-5 Finding and Using Data
4-6 Writing About Your Research

Chapter 5 Additional Modeling Techniques
5-1 Dummy Variables Aren't Stupid: When a Variable Is Not a Number
Intercept Dummies
Professional Wrestling Needs Dummies
The Dummy Variable Trap: Alwavs Leave an Escape Route
Seasonal Retail Sales Model
Slope Dummies
5-2 Interaction Variables Can't Leave Each Other Alone
5-3 Designing Your Own F-Test
F-Test Your Way to Riches
Chow test: Testing for Identical Twin Regressions
Gasoline Revenue and OPEC
5-4 Polynomial Models: Curves Can Be Linear Regressions
Sports Car Production Costs
5-5 Log Models: Estimating Elasticities
The Double-Log Model
Estimating the Price Elasticity of Demand for Compact Discs
The Semi log Model

Chapter 6 Multicollinearity: When Independent Variables Have Relationships
6-1 The Illness
6-2 The Symptoms
6-3 Measunng Multicollinearity
Correlation Coefficients
Regress One Independent Variable on Another
Variance Inflation Factor
6-4 Treating Multicollinearity
Leave the Model Alone
Eliminate an Independent Variable
Redesign the Model
Increase the Sample Size

Chapter 7 Autocorrelation: A Problem with Time-Series Regressions
7-1 The Illness
7-2 The Symptoms
7-3 Testing for the Illness: The Durbin-Watson Statistic
7-4 Treating the Disease
7-5 Treating the Symptoms
The Cochrane-Orcutt Method
The AR(1) Method

Chapter 8 Heteroskedasticity: A Problem with Cross-Section Regressions
8-1 The lllness
8-2 The Symptoms
8-3 Testing for the Illness: The Park Test and the White Test
The Park Test
The White Test
8-4 Treating the Disease
8-5 Treating the Symptoms
Weighted Least Squares
Correcting Standard Errors and t-Statistics for Heteroskedaricity

Chapter 9 Pooling Data Across Time and Space
9-1 Mixing the Data: Differences between Time and Space Disappear
9-2 Seemingly Unrelated Regressions Are Actually Related
9-3 The Fixed Effects Model: Everyone Deserve a Different Intercept
9-4 The Random Effects Model: They All Make Their Own Errors
9-5 A Comparison of SUR. Fixed Effects and Random Effects

Chapter 10 Simultaneous-Equation Systems: When One Equation Is
Not Enough
10-1 A Two-Equation Model for Pizza
10-2 The Identification Problem: How to Tell Supply from Demand
The Order Condition
10-3 Ordinary Least Squares Has Issues with Simultaneity
10-4 Checking for Simultaneity with the Hausman Test
10-5 Instrumental Variables: An Alternative for a Problem Variable
Measurement Error
10-6 Two-Stage Least Squares: An Orderly Approach to Instrumental

Chapter 11 Time-Series Models: Using the Past to Consider the Future
11-1 Estimating Distributed Lag Models
Distributed Lag Models
The Koyck Lag Model
Koyck Lag Models with Autocorrelation
11-2 Autoregressive and Moving Average Models: Errors That Last Over Time
Autoregressive Models
Moving Average Models
Auroregressive Moving Average Models
11-3 Stationary Versus Nonstationary Series: Unit Roots Can Be Hard to Kill
Keeping a Nonstationary Variable in Its Place
11-4 Forecasting: There Is No Crystal Ball
Confidence Intervals for Forecasts
Evaluating Forecasts
Forecasting with Autocorrelation
Forecasting with Simultaneous-Equation Models
11-5 Testing for Causality: What Came First: the Chicken or the Egg?
11-6 Appendix: The Math Behind the Koyck Lag Model

Chapter 12 Qualitative Choice Models: The Dependent Variable Is a Dummy
12-1 Binary Choice: The Dependent Variable Is O or 1
Linear Probability Model
12-2 Multiple Choice: More than Two Possible Answers
A Linear Probability Model for More than Two Choices
Multinomial Logit
12-3 An Overview of Censored and Truncated Data: Observations
You Can't See Can Hurt You
Censored Data
Truncated Data
Chapter 13 Econome-"tricks": Misleading Uses of Econometrics
13-1 Statistical Significance Does Not Prove Causality: Hendry's Theory of Inflation
13-2 Different Combinations of Independent Variables Can Give Contradictory Results
13-3 Extrapolation Can Stretch Things Too Far
13-4 Connecting the Dots: Forcing the Regression Line to Fit the Data
13-5 Small Sample Sizes Don't Give You Much Information
13-6 Self-Selection: Individual Choice Determines Who Is in the Sample
13-7 "Truth-in-Advertising" Is the Key to Honest Econometrics
13-8 A Table of Econometric Situations and Problems
Appendix of Statistical Tables

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