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Analysis of Economic Data

ISBN-10: 0470713895

ISBN-13: 9780470713891

Edition: 3rd 2009

Authors: Gary Koop

List price: $35.99
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Description:

Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles. Analysis of Economic Data teaches methods of data analysis to readers whose primary interest is not in econometrics, statistics or mathematics. It shows how to apply econometric techniques in the context of real-world empirical problems, and adopts a largely non-mathematical approach relying on verbal and graphical intuition. The book covers most of the tools used in modern econometrics research e.g. correlation, regression and extensions for time-series methods and contains extensive use of real data examples and involves readers in hands-on computer work.
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Book details

List price: $35.99
Edition: 3rd
Copyright year: 2009
Publisher: John Wiley & Sons, Limited
Publication date: 2/6/2009
Binding: Paperback
Pages: 264
Size: 6.75" wide x 9.50" long x 0.75" tall
Weight: 1.034
Language: English

Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Introduction
Organization of the Book
Useful Background
Mathematical Concepts Used in this Book
Endnote
References
Basic Data Handling
Types of Economic Data
Obtaining Data
Working with Data: Graphical Methods
Working with Data: Descriptive Statistics
Index Numbers
Advanced Descriptive Statistics
Expected Values and Variances
Endnotes
Correlation
Understanding Correlation
Understanding Why Variables Are Correlated
Understanding Correlation through XY-plots
Correlation between Several Variables
Mathematical Details
Endnotes
An Introduction to Simple Regression
Regression as a Best Fitting Line
Interpreting OLS Estimates
Fitted Values and R<sup>2</sup>: Measuring the Fit of a Regression Model
Nonlinearity in Regression
Mathematical Details
Endnotes
Statistical Aspects of Regression
Which Factors Affect the Accuracy of the Estimate <$$$>?
Calculating a Confidence Interval for �
Testing whether �=0
Hypothesis Testing Involving R<sup>2</sup>: The F Statistic
Using Statistical Tables for Testing whether �=0
Endnotes
References
Multiple Regression
Regression as a Best Fitting Line
Ordinary Least Squares Estimation of the Multiple Regression Model
Statistical Aspects of Multiple Regression
Interpreting OLS Estimates
Pitfalls of Using Simple Regression in a Multiple Regression Context
Omitted Variables Bias
Multicollinearity
Mathematical Interpretation of Regression Coefficients
Endnotes
Regression with Dummy Variables
Simple Regression with a Dummy Variable
Multiple Regression with Dummy Variables
Multiple Regression with Dummy and Nondummy Explanatory Variables
Interacting Dummy and Nondummy Variables
What if the Dependent Variable is a Dummy?
Endnotes
Regression with Time Lags: Distributed Lag Models
Aside on Lagged Variables
Aside on Notation
Selection of Lag Order
Other Distributed Lag Models
Endnotes
Univariate Time Series Analysis
The Autocorrelation Function
The Autoregressive Model for Univariate Time Series
Nonstationary versus Stationary Time Series
Extensions of the AR(1) Model
Testing in the AR(p) with Deterministic Trend Model
Mathematical Intuition for the AR(1) Model
Endnotes
References
Regression with Time Series Variables
Time Series Regression when X and Y Are Stationary
Time Series Regression when Y and X Have Unit Roots: Spurious Regression
Time Series Regression when Y and X Have Unit Roots: Cointegration
Time Series Regression when Y and X Are Cointegrated: The Error Correction Model
Time Series Regression when Y and X Have Unit Roots but Are NOT Cointegrated
Endnotes
Applications of Time Series Methods in Macroeconomics and Finance
Volatility in Asset Prices
Autoregressive Conditional Heteroskedasticity (ARCH)
Granger Causality
Vector Autoregressions
Hypothesis Tests Involving More than One Coefficient
Endnotes
Limitations and Extensions
Problems that Occur when the Dependent Variable Has Particular Forms
Problems that Occur when the Errors Have Particular Forms
Problems that Call for the Use of Multiple Equation Models
Endnotes
Writing an Empirical Project
Description of a Typical Empirical Project
General Considerations
Project Topics
References
Data Directory
Index