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Introduction to Econometrics 4 als Buch
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Introduction to Econometrics 4

3:B&W 7. 5 x 9. 25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam. Sprache: Englisch.
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Now in its fourth edition, this landmark text provides a fresh, accessible and well written introduction to the subject. With a rigorous pedagogical framework, which sets it apart from comparable texts, the latest edition features an expanded website … weiterlesen
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Introduction to Econometrics 4 als Buch

Produktdetails

Titel: Introduction to Econometrics 4
Autor/en: Maddala, Lahiri

ISBN: 0470015128
EAN: 9780470015124
3:B&W 7. 5 x 9. 25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam.
Sprache: Englisch.
John Wiley & Sons

8. Dezember 2009 - kartoniert - 656 Seiten

Beschreibung

The word "econometrics" literally means "measurement in economics." A revision of the landmark text, Introduction to Econometrics offers, in its new fourth edition, a fresh accessible, and well-written introduction to the subject. With a rigorous pedagogical framework, which sets it apart from comparable texts, the book also includes such Web-based supplements as an instructor's manual and data sets.

Inhaltsverzeichnis

Part I: Introduction and the Linear Regression Model Chapter 1 What is Econometrics?

Summary and an Outline of the Book

References

Chapter 2 Statistical Background and Matrix Algebra

Addition Rules of Probability

Conditional Probability and the Multiplication Rule

Bayes' Theorem

Summation and Product Operations

Joint, Marginal, and Conditional Distributions

Illustrative Example

The Normal Distribution

Related Distributions

Point Estimation

Unbiasedness

Efficiency

Consistency

Other Asymptotic Properties

Summary

Exercises

Appendix: Matrix Algebra

Exercises on Matrix Algebra

References

Chapter 3 Simple Regression

Example 1: Simple Regression

Example 2: Multiple Regression

Illustrative Example

Reverse Regression

Illustrative Example

Illustrative Example

Confidence Intervals for a, b, and s²

Testing of Hypotheses

Example of Comparing Test Scores from the GRE and GMAT Tests

Regression with No Constant Term

Prediction of Expected Values

Illustrative Example

Some Illustrative Examples

Illustrative Example

The Bivariate Normal Distribution

Galton's Result and the Regression Fallacy

A Note on the Term "Regression"

Summary

Exercises

Appendix: Proofs

References

Chapter 4 Multiple Regression

The Least Squares Method

Illustrative Example

Illustrative Example

Formulas for the General Case of k Explanatory Variables

Some Illustrative Examples

Illustrative Example

Two Illustrative Examples

Illustrative Example

Nested and Nonnested Hypotheses

Tests for Linear Functions of Parameters

Illustrative Example

Omission of Relevant Variables

Example 1: Demand for Food in the United States

Example 2: Production Functions and Management Bias

Inclusion of Irrelevant Variables

The Analysis of Variance Test

Example 1: Stability of the Demand for Food Function

Example 2: Stability of Production Functions

Predictive Tests for Stability

Illustrative Example

Illustrative Example

Summary

Exercises

Appendix 4.1: The Multiple Regression Model in Matrix Notation

Appendix 4.2: Nonlinear Regressions

Appendix 4.3: Large-Sample Theory

Data Sets

References

Part II: Violation of the Assumptions of the Basic Model Chapter 5 Heteroskedasticity

Illustrative Example

Illustrative Example

Some Other Tests

Illustrative Example

An Intuitive Justification for the Breusch-Pagan Test

Estimation of the Variance of the OLS Estimator under Heteroskedasticity

Illustrative Example

Illustrative Example: The Density Gradient Model

The Box-Cox Test

The BM Test

The PE Test

Summary

Exercises

Appendix: Generalized Least Squares

References

Chapter 6 Autocorrelation

Illustrative Example

Some Illustrative Examples

Iterative Procedures

Grid-Search Procedures

The von Neumann Ratio

The Berenblut-Webb Test

Durbin's h-Test

Durbin's Alternative Test

Illustrative Example

Errors Not AR(1)

Autocorrelation Caused by Omitted Variables

Serial Correlation Due to Misspecified Dynamics

The Wald Test

Illustrative Example

Spurious Trends

Differencing and Long-Run Effects: The Concept of Cointegration

Summary

Exercises

References

Chapter 7 Multicollinearity

Using Ratios or First Differences

Using Extraneous Estimates

Getting More Data

Summary

Exercises

Appendix: Linearly Dependent Explanatory Variables

References

Chapter 8 Dummy Variables and Truncated Variables

Illustrative Example

Two More Illustrative Examples

The Linear Probability Model

The Linear Discriminant Function

Illustrative Example

The Problem of Disproportionate Sampling

Prediction of Effects of Changes in the Explanatory Variables

Measuring Goodness of Fit

Some Examples

Method of Estimation

Limitations of the Tobit Model

The Truncated Regression Model

Summary

Exercises

References

Chapter 9 Simultaneous Equations Models

Illustrative Example

Illustrative Example

Measuring R²

Illustrative Example

Computing Standard Errors

Illustrative Example

Illustrative Example

Working's Concept of Identification

Recursive Systems

Estimation of Cobb-Douglas Production Functions

Weak Exogeneity

Superexogeneity

Strong Exogeneity

Granger Causality

Granger Causality and Exogeneity

Tests for Exogeneity

Summary

Exercises

Appendix

References

Chapter 10 Diagnostic Checking, Model Selection, and Specification Testing

Tests for Omitted Variables

Tests for ARCH Effects

Predicted Residuals and Studentized Residuals

Dummy Variable Method for Studentized Residuals

BLUS Residuals

Recursive Residuals

Illustrative Example

Illustrative Example

Hypothesis-Testing Search

Interpretive Search

Simplification Search

Proxy Variable Search

Data Selection Search

Post-Data Model Construction

Hendry's Approach to Model Selection

Theil's Criterion

Criteria Based on Minimizing the Mean-Squared Error of Prediction

Akaike's Information Criterion

Bayes' Theorem and Posterior Odds for Model Selection

An Application: Testing for Errors in Variables or Exogeneity

Some Illustrative Examples

An Omitted Variable Interpretation of the Hausman Test

The Davidson and MacKinnon Test

The Encompassing Test

A Basic Problem in Testing Nonnested Hypotheses

Hypothesis Testing versus Model Selection as a Research Strategy

Tests for Normality

Summary

Exercises

Appendix

References

Chapter 11 Errors in Variables

Two Explanatory Variables: One Measured with Error

Illustrative Example

Two Explanatory Variables: Both Measured with Error

Coefficient of the Proxy Variable

The Case of Multiple Equations

Correlated Errors

Summary

Exercises

References

Part III: Special Topics Chapter 12 Introduction to Time-Series Analysis

Strict Stationarity

Weak Stationarity

Properties of Autocorrelation Function

Nonstationarity

Purely Random Process

Random Walk

Moving Average Process

Autoregressive Process

Autoregressive Moving Average Process

Autoregressive Integrated Moving Average Process

Estimation of MA Models

Estimation of ARMA Models

Residuals from the ARMA Models

Testing Goodness of Fit

Forecasting from Box-Jenkins Models

Illustrative Example

Trend Elimination: The Traditional Method

A Summary Assessment

Seasonality in the Box-Jenkins Modeling

Summary

Exercises

Data Sets

References

Chapter 13 Models of Expectations and Distributed Lags

Estimation in the Autoregressive Form

Estimation in the Distributed Lag Form

Finite Lags: The Polynomial Lag

Illustrative Example

Choosing the Degree of the Polynomial

Case 1

Case 2

Illustrative Example

Summary

Exercises

References

Chapter 14 Vector Autoregressions, Unit Roots, and Cointegration

The Dickey-Fuller Tests

The Serial Correlation Problem

The Low Power of Unit Root Tests

The DF-GLS Test

What are the Null and Alternative Hypotheses in Unit Root Tests?

Tests with Stationarity as Null

Confirmatory Analysis

Panel Data Unit Root Tests

Structural Change and Unit Roots

Summary

Exercises

References

Chapter 15 Panel Data Analysis

Illustrative Example: Fixed Effect Estimation

Hausman Test

Breusch and Pagan Test

Tests for Serial Correlation

Summary

References

Chapter 16 Small-Sample Inference: Resampling Methods

More Efficient Monte Carlo Methods

Response Surfaces

Some Illustrative Examples

Other Issues Relating to the Bootstrap

Heteroskedasticity and Autocorrelation

Unit Root Tests Based on the Bootstrap

Cointegration Tests

Summary

References

Appendix

Index

Portrait

G.S.Maddala was one of the leading figures in field of econometrics for more than 30 years until he passed away in 1999. At the time of his death, he held the University Eminent Scholar Professorship in the Department of Economics at Ohio State University. His previous affiliations include Stanford University, University of Rochester and University of Florida. Kajal Lahiri is Distinguished Professor of Economics, and Health Policy, and Management and Behaviour at the State University of New York, Albany where he is also Director of the Econometric Research Institute. Professor Lahiri is an Honorary Fellow of the International Institute of Forecasters.

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