Introductory econometrics: a modern approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Mason, Ohio
Thomson, South-Western
2006
|
| Ausgabe: | 3. ed., internat. student ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXII, 890 S. graph. Darst. |
| ISBN: | 0324323484 9780324323481 |
Internformat
MARC
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| 100 | 1 | |a Wooldridge, Jeffrey M. |d 1960- |e Verfasser |0 (DE-588)131680463 |4 aut | |
| 245 | 1 | 0 | |a Introductory econometrics |b a modern approach |c Jeffrey M. Wooldridge |
| 250 | |a 3. ed., internat. student ed. | ||
| 264 | 1 | |a Mason, Ohio |b Thomson, South-Western |c 2006 | |
| 300 | |a XXII, 890 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 7 | |a Econometria |2 sbt | |
| 650 | 7 | |a Regressione (statistica) |2 sbt | |
| 650 | 7 | |a Statistica - metodi matematici |2 sbt | |
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| _version_ | 1805079370330537984 |
|---|---|
| adam_text |
Contents
CHAPTER
The Nature of Econometrics
and Economic Data
1.1
1.2
1.3
Cross-Sectional Data
Time Series Data
Pooled Cross Sections
Panel or Longitudinal Data
A Comment on Data Structures
1.4
in Econometric Analysis
Summary
Key Terms
Problems
Computer Exercises
Regression Analysis with
Cross-Sectional Data
CHAPTER
The Simple Regression Model
24
2.1
2.2
Estimates
A Note on Terminology
2.3
Fitted Values and Residuals
Algebraic Properties of OLS Statistics
Goodness-of-Fit
2.4
The Effects of Changing Units of Measurement
on OLS Statistics
Incorporating Nonlinearities in Simple
Regression
The Meaning of "Linear" Regression
2.5
Estimators
Unbiasedness of OLS
Variances of the OLS Estimators
Estimating the Error Variance
2.6
Summary
Key Terms
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis: Estimation
3.1
The Model with Two Independent Variables
The Model with
3.2
Squares
Obtaining the OLS Estimates
Interpreting the OLS Regression Equation
On the Meaning of "Holding Other Factors
Fixed" in Multiple Regression
Changing More than One Independent Variable
Simultaneously
OLS Fitted Values and Residuals
A "Partialling Out" Interpretation of Multiple
Regression
Comparison of Simple and Multiple Regression
Estimates
Goodness-of-Fit
Regression through the Origin
3.3
Including Irrelevant Variables in a Regression
Model
Omitted Variable Bias: The Simple Case
iv
Contents
Omitted
Cases
3.4
The Components of the OLS Variances:
Multicollinearity
Variances in Misspecified Models
Estimating a2: Standard Errors of the OLS
Estimators
3.5
Theorem
Summary
Key Terms 111
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis: Inference
4.1
of the OLS Estimators
4.2
Population Parameter: The
Testing against One-Sided Alternatives
Two-Sided Alternatives
Testing Other Hypotheses about
Computing p-Valuesfor
A Reminder on the Language of Classical
Hypothesis Testing
Economic, or Practical, versus Statistical
Significance
4.3
4.4
Combination of the Parameters
4.5
The
Testing Exclusion Restrictions
Relationship between
The R-Squared Form of the
Computing p-Valuesfor
The
of a Regression
Testing General Linear Restrictions
4.6
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Multiple Regression Analysis:
OLS Asymptotics
5.1
Deriving the Inconsistency in OLS
5.2
Sample Inference
Other Large Sample Tests: The
Multiplier Statistic
5.3
Summary
Key Terms
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis:
Further Issues
6.1
Beta Coefficients
6.2
More on Using Logarithmic Functional
Forms
Models with Quadratics
Models with Interaction Terms
6.3
and Selection of Regressors
AdjustedR-Squared
Using Adjusted R-Squared to Choose
between Nonnested Models
Controlling for Too Many Factors
in Regression Analysis
Adding Regressors to Reduce the Error
Variance
6.4
Confidence Intervals for Predictions
Residual Analysis
Predicting
Variable
Summary
Key Terms
Problems
Computer Exercises
VI
Contents
CHAPTER
Multiple Regression Analysis
with Qualitative Information:
Binary (or Dummy) Variables
7.1
7.2
Interpreting Coefficients on Dummy Explanatory
Variables When the Dependent Variable
Islog(y)
7.3
for Multiple Categories
Incorporating Ordinal Information
by Using Dummy Variables
7.4
Interactions among Dummy Variables
Allowing for Different Slopes
Testing for Differences in Regression Functions
across Groups
7.5
The Linear Probability Model
7.6
and Program Evaluation
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
More on Specification
and Data Problems
CHAPTER
Heteroskedasticity
271
8.1
forOLS
8.2
after OLS Estimation
Computing Heteroskedasticity-Robust
LM Tests
8.3
The White Test for Heteroskedasticity
8.4
The Heteroskedasticity Is Known
up to a Multiplicative Constant
The Hetervskedasticity Function Must
Be Estimated: Feasible GLS
8.5
Summary
Key Terms
Problems
Computer Exercises
9.1
RESET as a General Test for Functional
Form Misspecification
Tests against Nonnested Alternatives
9.2
Explanatory Variables
Using Lagged Dependent Variables
as Proxy Variables
A Different Slant on Multiple Regression
9.3
Error
Measurement Error in the Dependent
Variable
Measurement Error in an Explanatory
Variable
9.4
and Outlying Observations
Missing Data
Nonrandom Samples
Outliers and Influential Observations
Summary
Key Terms
Problems
Computer Exercises
Regression Analysis with
Time Series Data
CHAPTER
Basic Regression Analysis
with Time Series Data
10.1
10.2
Models
Static Models
Finite Distributed Lag Models
A Convention about the
10.3
under Classical Assumptions
Unbiasedness of OLS
Contents
VII
The Variances of the OLS Estimators
and the Gauss-Markov Theorem
Inference under the Classical Linear Model
Assumptions
10.4
and Index Numbers
10.5
Characterizing Trending Time Series
Using Trending Variables in Regression
Analysis
A Detrending Interpretation of Regressions
with a Time Trend
Computing R-Squared When the Dependent
Variable Is Trending
Seasonality
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Further Issues in Using OLS
with Time Series Data
11.1
Series
Stationary and Nonstationary Time Series
Weakly Dependent Time Series
11.2
11.3
in Regression Analysis
Highly Persistent Time Series
Transformations on Highly Persistent Time
Series
Deciding Whether a Time Series Is
11.4
and the Absence of Serial Correlation
11.5
for Time Series Models
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Serial Correlation and Heteroskedasticity
in Time Series Regressions
12.1
Errors
Unbiasedness and Consistency
Efficiency and Inference
Goodness-of-Fit
Serial Correlation in the Presence
of Lagged Dependent Variables
12.2
A t Test for AR(l) Serial Correlation
with Strictly Exogenous Regressors
The Durbin-Watson Test under Classical
Assumptions
Testing for AR(
without Strictly Exogenous Regressors
Testing for Higher Order Serial
Correlation
12.3
with Strictly Exogenous Regressors
Obtaining the Best Linear Unbiased Estimator
in the AR(1) Model
Feasible GLS Estimation withAR(l)
Errors
Comparing OLS and FGLS
Correcting
Correlation
12.4
12.5
Inference after OLS
12.6
Regressions
Heteroskedasticity-Robust Statistics
Testing for Heteroskedasticity
Autoregressive
Heteroskedasticity
Heteroskedasticity and Serial Correlation
in Regression Models
Summary
Key Terms
Problems
Computer Exercises
Advanced Topics
CHAPTER
Pooling Cross Sections across Time:
Simple Panel Data Methods
13.1
across Time
VIII
Contents
The Chow Test for Structural Change
across
13.2
Sections
13.3
Organizing Panel Data
13.4
Data
13.5
Two Time Periods
Potential Pitfalls in First-Differencing Panel
Data
Summary
Key Terms
Problems
Computer Exercises
Appendix 13A
CHAPTER
Advanced Panel Data Methods
14.1
The Dummy Variable Regression
Fixed Effects or First Differencing
Fixed Effects with Unbalanced Panels
14.2
Random Effects or Fixed Effects?
14.3
to Other Data Structures
Summary
Key Terms
Problems
Computer Exercises
Appendix 14A
CHAPTER
Instrumental Variables Estimation
and Two Stage Least Squares
15.1
Regression Model
Statistical Inference with the
Properties of IV with a Poor Instrumental
Variable
Computing ^-Squared after IV Estimation
15.2
Model
15.3
A Single Endogenous Explanatory
Variable
Multicollinearity and 2SLS
Multiple Endogenous Explanatory
Variables
Testing Multiple Hypotheses after 2SLS
Estimation
15.4
Problems
15.5
Overidentifying Restrictions
Testing for Endogeneity
Testing Overidentification Restrictions
15.6
15.7
15.8
and Panel Data
Summary
Key Terms
Problems
Computer Exercises
Appendix 15A
CHAPTER
Simultaneous Equations Models
552
16.1
Equations Models
16.2
16.3
a Structural Equation
Identification in a Two-Equation System
Estimation by 2SLS
16.4
Identification in Systems with Three
or More Equations
Estimation
16.5
with Time Series
16.6
with Panel Data
Summary
Key Terms
Problems
Computer Exercises
Contents
IX
CHAPTER
Limited
and Sample
17.1 Logit and Probit Models
Response
Specifying Logit and
Maximum Likelihood Estimation of Logit and
Probit
Testing Multiple Hypotheses
Interpreting the Logit and
Estimates
17.2
for Corner Solution Responses
Interpreting the Tobit Estimates
Specification Issues in Tobit Models
17.3
17.4
Models
Censored Regression Models
Truncated Regression Models
17.5
When Is OLS on the Selected Sample
Consistent?
Incidental Truncation
Summary
Key Terms
Problems
Computer Exercises
Appendix 17A
CHAPTER
Advanced Time Series Topics
18.1
The Geometric (or Koyck) Distributed Lag
635
Rational Distributed Lag Models
18.2
18.3
18.4
Cointegration
Error Correction Models
18.5
Types of Regression Models Used
for Forecasting
One-Step-Ahead Forecasting
Comparing One-Step-Ahead Forecasts
Multiple-Step-Ahead Forecasts
Forecasting Trending, Seasonal, and Integrated
Processes
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Carrying Out an Empirical Project
19.1
19.2
19.3
Deciding on the Appropriate Data Set
Entering and Storing Your Data
Inspecting, Cleaning, and Summarizing Your
Data
19.4
19.5
Introduction
Conceptual (or Theoretical) Framework
Econometric Models and Estimation
Methods
The Data
Results
Conclusions
Style Hints
Summary
Key Terms
Sample Empirical Projects
List of Journals
Data Sources
APPENDIX A
Basic Mathematical Tools
A.I The Summation Operator
and Descriptive Statistics
A.2 Properties of Linear Functions
A.3 Proportions and Percentages
A.4 Some Special Functions and Their
Properties
Quadratic Functions
The Natural Logarithm
The Exponential Function
Contents
A.5 Differential
Summary
Key
Problems 725
APPENDIX
Fundamentals of Probability
В.
Distributions
Discrete Random Variables
Continuous Random Variables
B.2 Joint Distributions, Conditional Distributions,
and Independence
Joint
Conditional Distributions
B.3 Features of Probability Distributions
A Measure of Central Tendency:
The Expected Value
Properties of Expected Values
Another Measure of Central Tendency:
The Median
Measures of Variability: Variance
and Standard Deviation
Variance
Standard Deviation
Standardizing a Random Variable
B.4 Features of Joint and Conditional
Distributions
Measures of Association:
Covariance and Correlation
Covariance
Correlation Coefficient
Variance of Sums of Random Variables
Conditional Expectation
Properties of Conditional Expectation
Conditional Variance
B.5 The Normal and Related Distributions
The Normal Distribution
The Standard Normal Distribution
Additional Properties of the Normal
Distribution
The Chi-Square Distribution
The
The
Summary
Key Terms
Problems
APPENDIX
Fundamentals of Mathematical Statistics
С
Sampling
Sampling
C.2 Finite Sample Properties of Estimators
Estimators and Estimates
Unbiasedness
The Sampling Variance of Estimators
Efficiency
C.3 Asymptotic or Larger Sample Properties
of Estimators
Consistency
Asymptotic Normality
C.4 General Approaches to Parameter
Estimation
Method of Moments
Maximum Likelihood
Least Squares
C.5 Interval Estimation and Confidence
Intervals
The Nature of Interval Estimation
Confidence Intervals for the Mean from a
Normally Distributed Population
A Simple Rule of Thumb for a
Interval
Asymptotic Confidence Intervals for
Populations
C.6 Hypothesis Testing
Fundamentals of Hypothesis Testing
Testing Hypotheses about the Mean in a Normal
Population
Asymptotic Tests for
Populations
Computing and Using p-Values
The Relationship between Confidence Intervals
and Hypothesis Testing
Practical versus Statistical Significance
C.I Remarks on Notation
Summary
Key Terms
Problems
Contents
APPENDIX
Summary of
D.l Basic
D.2 Matrix
Matrix Addition 809
Scalar Multiplication
Matrix
Transpose
Partitioned Matrix Multiplication
Trace
Inverse
D,3 Linear Independence and Rank
of a Matrix
D.4 Quadratic Forms and Positive Definite
Matrices
D.5 Idempotent Matrices
D.6 Differentiation of Linear and Quadratic
Forms
D.7 Moments and Distributions of Random
Vectors
Expected Value
Variance-Covariance Matrix
Multivariate Normal Distribution
Chi-Square Distribution
t
F
Summary
Key Terms
Problems
APPENDIX
The Linear Regression Model
in Matrix Form
E.
Estimation
E.2 Finite Sample Properties of OLS
E.3 Statistical Inference
E.4. Some Asymptotic Analysis
Wald
Hypotheses
Summary
Key Terms
Problems
APPENDIX
Answers to Chapter Questions
APPENDIX
Statistical Tables
References
Glossary
Index |
| adam_txt |
Contents
CHAPTER
The Nature of Econometrics
and Economic Data
1.1
1.2
1.3
Cross-Sectional Data
Time Series Data
Pooled Cross Sections
Panel or Longitudinal Data
A Comment on Data Structures
1.4
in Econometric Analysis
Summary
Key Terms
Problems
Computer Exercises
Regression Analysis with
Cross-Sectional Data
CHAPTER
The Simple Regression Model
24
2.1
2.2
Estimates
A Note on Terminology
2.3
Fitted Values and Residuals
Algebraic Properties of OLS Statistics
Goodness-of-Fit
2.4
The Effects of Changing Units of Measurement
on OLS Statistics
Incorporating Nonlinearities in Simple
Regression
The Meaning of "Linear" Regression
2.5
Estimators
Unbiasedness of OLS
Variances of the OLS Estimators
Estimating the Error Variance
2.6
Summary
Key Terms
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis: Estimation
3.1
The Model with Two Independent Variables
The Model with
3.2
Squares
Obtaining the OLS Estimates
Interpreting the OLS Regression Equation
On the Meaning of "Holding Other Factors
Fixed" in Multiple Regression
Changing More than One Independent Variable
Simultaneously
OLS Fitted Values and Residuals
A "Partialling Out" Interpretation of Multiple
Regression
Comparison of Simple and Multiple Regression
Estimates
Goodness-of-Fit
Regression through the Origin
3.3
Including Irrelevant Variables in a Regression
Model
Omitted Variable Bias: The Simple Case
iv
Contents
Omitted
Cases
3.4
The Components of the OLS Variances:
Multicollinearity
Variances in Misspecified Models
Estimating a2: Standard Errors of the OLS
Estimators
3.5
Theorem
Summary
Key Terms 111
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis: Inference
4.1
of the OLS Estimators
4.2
Population Parameter: The
Testing against One-Sided Alternatives
Two-Sided Alternatives
Testing Other Hypotheses about
Computing p-Valuesfor
A Reminder on the Language of Classical
Hypothesis Testing
Economic, or Practical, versus Statistical
Significance
4.3
4.4
Combination of the Parameters
4.5
The
Testing Exclusion Restrictions
Relationship between
The R-Squared Form of the
Computing p-Valuesfor
The
of a Regression
Testing General Linear Restrictions
4.6
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Multiple Regression Analysis:
OLS Asymptotics
5.1
Deriving the Inconsistency in OLS
5.2
Sample Inference
Other Large Sample Tests: The
Multiplier Statistic
5.3
Summary
Key Terms
Problems
Computer Exercises
Appendix
CHAPTER
Multiple Regression Analysis:
Further Issues
6.1
Beta Coefficients
6.2
More on Using Logarithmic Functional
Forms
Models with Quadratics
Models with Interaction Terms
6.3
and Selection of Regressors
AdjustedR-Squared
Using Adjusted R-Squared to Choose
between Nonnested Models
Controlling for Too Many Factors
in Regression Analysis
Adding Regressors to Reduce the Error
Variance
6.4
Confidence Intervals for Predictions
Residual Analysis
Predicting
Variable
Summary
Key Terms
Problems
Computer Exercises
VI
Contents
CHAPTER
Multiple Regression Analysis
with Qualitative Information:
Binary (or Dummy) Variables
7.1
7.2
Interpreting Coefficients on Dummy Explanatory
Variables When the Dependent Variable
Islog(y)
7.3
for Multiple Categories
Incorporating Ordinal Information
by Using Dummy Variables
7.4
Interactions among Dummy Variables
Allowing for Different Slopes
Testing for Differences in Regression Functions
across Groups
7.5
The Linear Probability Model
7.6
and Program Evaluation
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
More on Specification
and Data Problems
CHAPTER
Heteroskedasticity
271
8.1
forOLS
8.2
after OLS Estimation
Computing Heteroskedasticity-Robust
LM Tests
8.3
The White Test for Heteroskedasticity
8.4
The Heteroskedasticity Is Known
up to a Multiplicative Constant
The Hetervskedasticity Function Must
Be Estimated: Feasible GLS
8.5
Summary
Key Terms
Problems
Computer Exercises
9.1
RESET as a General Test for Functional
Form Misspecification
Tests against Nonnested Alternatives
9.2
Explanatory Variables
Using Lagged Dependent Variables
as Proxy Variables
A Different Slant on Multiple Regression
9.3
Error
Measurement Error in the Dependent
Variable
Measurement Error in an Explanatory
Variable
9.4
and Outlying Observations
Missing Data
Nonrandom Samples
Outliers and Influential Observations
Summary
Key Terms
Problems
Computer Exercises
Regression Analysis with
Time Series Data
CHAPTER
Basic Regression Analysis
with Time Series Data
10.1
10.2
Models
Static Models
Finite Distributed Lag Models
A Convention about the
10.3
under Classical Assumptions
Unbiasedness of OLS
Contents
VII
The Variances of the OLS Estimators
and the Gauss-Markov Theorem
Inference under the Classical Linear Model
Assumptions
10.4
and Index Numbers
10.5
Characterizing Trending Time Series
Using Trending Variables in Regression
Analysis
A Detrending Interpretation of Regressions
with a Time Trend
Computing R-Squared When the Dependent
Variable Is Trending
Seasonality
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Further Issues in Using OLS
with Time Series Data
11.1
Series
Stationary and Nonstationary Time Series
Weakly Dependent Time Series
11.2
11.3
in Regression Analysis
Highly Persistent Time Series
Transformations on Highly Persistent Time
Series
Deciding Whether a Time Series Is
11.4
and the Absence of Serial Correlation
11.5
for Time Series Models
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Serial Correlation and Heteroskedasticity
in Time Series Regressions
12.1
Errors
Unbiasedness and Consistency
Efficiency and Inference
Goodness-of-Fit
Serial Correlation in the Presence
of Lagged Dependent Variables
12.2
A t Test for AR(l) Serial Correlation
with Strictly Exogenous Regressors
The Durbin-Watson Test under Classical
Assumptions
Testing for AR(
without Strictly Exogenous Regressors
Testing for Higher Order Serial
Correlation
12.3
with Strictly Exogenous Regressors
Obtaining the Best Linear Unbiased Estimator
in the AR(1) Model
Feasible GLS Estimation withAR(l)
Errors
Comparing OLS and FGLS
Correcting
Correlation
12.4
12.5
Inference after OLS
12.6
Regressions
Heteroskedasticity-Robust Statistics
Testing for Heteroskedasticity
Autoregressive
Heteroskedasticity
Heteroskedasticity and Serial Correlation
in Regression Models
Summary
Key Terms
Problems
Computer Exercises
Advanced Topics
CHAPTER
Pooling Cross Sections across Time:
Simple Panel Data Methods
13.1
across Time
VIII
Contents
The Chow Test for Structural Change
across
13.2
Sections
13.3
Organizing Panel Data
13.4
Data
13.5
Two Time Periods
Potential Pitfalls in First-Differencing Panel
Data
Summary
Key Terms
Problems
Computer Exercises
Appendix 13A
CHAPTER
Advanced Panel Data Methods
14.1
The Dummy Variable Regression
Fixed Effects or First Differencing
Fixed Effects with Unbalanced Panels
14.2
Random Effects or Fixed Effects?
14.3
to Other Data Structures
Summary
Key Terms
Problems
Computer Exercises
Appendix 14A
CHAPTER
Instrumental Variables Estimation
and Two Stage Least Squares
15.1
Regression Model
Statistical Inference with the
Properties of IV with a Poor Instrumental
Variable
Computing ^-Squared after IV Estimation
15.2
Model
15.3
A Single Endogenous Explanatory
Variable
Multicollinearity and 2SLS
Multiple Endogenous Explanatory
Variables
Testing Multiple Hypotheses after 2SLS
Estimation
15.4
Problems
15.5
Overidentifying Restrictions
Testing for Endogeneity
Testing Overidentification Restrictions
15.6
15.7
15.8
and Panel Data
Summary
Key Terms
Problems
Computer Exercises
Appendix 15A
CHAPTER
Simultaneous Equations Models
552
16.1
Equations Models
16.2
16.3
a Structural Equation
Identification in a Two-Equation System
Estimation by 2SLS
16.4
Identification in Systems with Three
or More Equations
Estimation
16.5
with Time Series
16.6
with Panel Data
Summary
Key Terms
Problems
Computer Exercises
Contents
IX
CHAPTER
Limited
and Sample
17.1 Logit and Probit Models
Response
Specifying Logit and
Maximum Likelihood Estimation of Logit and
Probit
Testing Multiple Hypotheses
Interpreting the Logit and
Estimates
17.2
for Corner Solution Responses
Interpreting the Tobit Estimates
Specification Issues in Tobit Models
17.3
17.4
Models
Censored Regression Models
Truncated Regression Models
17.5
When Is OLS on the Selected Sample
Consistent?
Incidental Truncation
Summary
Key Terms
Problems
Computer Exercises
Appendix 17A
CHAPTER
Advanced Time Series Topics
18.1
The Geometric (or Koyck) Distributed Lag
635
Rational Distributed Lag Models
18.2
18.3
18.4
Cointegration
Error Correction Models
18.5
Types of Regression Models Used
for Forecasting
One-Step-Ahead Forecasting
Comparing One-Step-Ahead Forecasts
Multiple-Step-Ahead Forecasts
Forecasting Trending, Seasonal, and Integrated
Processes
Summary
Key Terms
Problems
Computer Exercises
CHAPTER
Carrying Out an Empirical Project
19.1
19.2
19.3
Deciding on the Appropriate Data Set
Entering and Storing Your Data
Inspecting, Cleaning, and Summarizing Your
Data
19.4
19.5
Introduction
Conceptual (or Theoretical) Framework
Econometric Models and Estimation
Methods
The Data
Results
Conclusions
Style Hints
Summary
Key Terms
Sample Empirical Projects
List of Journals
Data Sources
APPENDIX A
Basic Mathematical Tools
A.I The Summation Operator
and Descriptive Statistics
A.2 Properties of Linear Functions
A.3 Proportions and Percentages
A.4 Some Special Functions and Their
Properties
Quadratic Functions
The Natural Logarithm
The Exponential Function
Contents
A.5 Differential
Summary
Key
Problems 725
APPENDIX
Fundamentals of Probability
В.
Distributions
Discrete Random Variables
Continuous Random Variables
B.2 Joint Distributions, Conditional Distributions,
and Independence
Joint
Conditional Distributions
B.3 Features of Probability Distributions
A Measure of Central Tendency:
The Expected Value
Properties of Expected Values
Another Measure of Central Tendency:
The Median
Measures of Variability: Variance
and Standard Deviation
Variance
Standard Deviation
Standardizing a Random Variable
B.4 Features of Joint and Conditional
Distributions
Measures of Association:
Covariance and Correlation
Covariance
Correlation Coefficient
Variance of Sums of Random Variables
Conditional Expectation
Properties of Conditional Expectation
Conditional Variance
B.5 The Normal and Related Distributions
The Normal Distribution
The Standard Normal Distribution
Additional Properties of the Normal
Distribution
The Chi-Square Distribution
The
The
Summary
Key Terms
Problems
APPENDIX
Fundamentals of Mathematical Statistics
С
Sampling
Sampling
C.2 Finite Sample Properties of Estimators
Estimators and Estimates
Unbiasedness
The Sampling Variance of Estimators
Efficiency
C.3 Asymptotic or Larger Sample Properties
of Estimators
Consistency
Asymptotic Normality
C.4 General Approaches to Parameter
Estimation
Method of Moments
Maximum Likelihood
Least Squares
C.5 Interval Estimation and Confidence
Intervals
The Nature of Interval Estimation
Confidence Intervals for the Mean from a
Normally Distributed Population
A Simple Rule of Thumb for a
Interval
Asymptotic Confidence Intervals for
Populations
C.6 Hypothesis Testing
Fundamentals of Hypothesis Testing
Testing Hypotheses about the Mean in a Normal
Population
Asymptotic Tests for
Populations
Computing and Using p-Values
The Relationship between Confidence Intervals
and Hypothesis Testing
Practical versus Statistical Significance
C.I Remarks on Notation
Summary
Key Terms
Problems
Contents
APPENDIX
Summary of
D.l Basic
D.2 Matrix
Matrix Addition 809
Scalar Multiplication
Matrix
Transpose
Partitioned Matrix Multiplication
Trace
Inverse
D,3 Linear Independence and Rank
of a Matrix
D.4 Quadratic Forms and Positive Definite
Matrices
D.5 Idempotent Matrices
D.6 Differentiation of Linear and Quadratic
Forms
D.7 Moments and Distributions of Random
Vectors
Expected Value
Variance-Covariance Matrix
Multivariate Normal Distribution
Chi-Square Distribution
t
F
Summary
Key Terms
Problems
APPENDIX
The Linear Regression Model
in Matrix Form
E.
Estimation
E.2 Finite Sample Properties of OLS
E.3 Statistical Inference
E.4. Some Asymptotic Analysis
Wald
Hypotheses
Summary
Key Terms
Problems
APPENDIX
Answers to Chapter Questions
APPENDIX
Statistical Tables
References
Glossary
Index |
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| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 3. ed., internat. student ed. |
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| genre_facet | Lehrbuch Lehrbuch - Ökonometrie |
| id | DE-604.BV020855307 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:21:05Z |
| indexdate | 2024-07-20T06:43:54Z |
| institution | BVB |
| isbn | 0324323484 9780324323481 |
| language | English |
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| physical | XXII, 890 S. graph. Darst. |
| publishDate | 2006 |
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| spelling | Wooldridge, Jeffrey M. 1960- Verfasser (DE-588)131680463 aut Introductory econometrics a modern approach Jeffrey M. Wooldridge 3. ed., internat. student ed. Mason, Ohio Thomson, South-Western 2006 XXII, 890 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Econometria sbt Regressione (statistica) sbt Statistica - metodi matematici sbt Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Lehrbuch - Ökonometrie Ökonometrie (DE-588)4132280-0 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014176951&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wooldridge, Jeffrey M. 1960- Introductory econometrics a modern approach Econometria sbt Regressione (statistica) sbt Statistica - metodi matematici sbt Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Introductory econometrics a modern approach |
| title_auth | Introductory econometrics a modern approach |
| title_exact_search | Introductory econometrics a modern approach |
| title_exact_search_txtP | Introductory econometrics a modern approach |
| title_full | Introductory econometrics a modern approach Jeffrey M. Wooldridge |
| title_fullStr | Introductory econometrics a modern approach Jeffrey M. Wooldridge |
| title_full_unstemmed | Introductory econometrics a modern approach Jeffrey M. Wooldridge |
| title_short | Introductory econometrics |
| title_sort | introductory econometrics a modern approach |
| title_sub | a modern approach |
| topic | Econometria sbt Regressione (statistica) sbt Statistica - metodi matematici sbt Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometria Regressione (statistica) Statistica - metodi matematici Ökonometrie Lehrbuch Lehrbuch - Ökonometrie |
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