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Introduction to Applied Econometerics

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ISBN-10: 0534369162

ISBN-13: 9780534369163

Edition: 2005

Authors: Kenneth Stewart, Kenneth G. Stewart

List price: $319.95
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You'll find the "econ" back in econometrics with INTRODUCTION TO APPLIED ECONOMETRICS and its accompanying CD.. You'll have the opportunity to replicate classic empirical findings using original data sets and will develop an understanding of the relevance of economic theory to empirical analysis. The author integrates classic empirical examples and applications and builds toward a self-contained four-chapter introduction to time series analysis. The CD includes data sets formatted for STATA, Eviews, Excel, Minitab, SAS and ASCII, as well as an appendix presenting multiple regression in matrix form and another on treating portfolio theory and the capital asset pricing model.
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Book details

List price: $319.95
Copyright year: 2005
Publisher: Cengage South-Western
Publication date: 7/30/2004
Binding: Hardcover
Pages: 890
Size: 7.50" wide x 9.00" long x 1.25" tall
Weight: 3.784
Language: English

Kenneth Stewart is Professor Emeritus of Zoology at the University of Manitoba, and a nationally recognized expert on freshwater fish. He is Scientific Advisor to the non-profit foundation Fish Futures Inc., and a recipient of a National Recreation Fishing Award.

Economic Data And Economic Models
Economic Data
Economic Models
Descriptive Statistics VersusStatistical Inference
Statistical Inference
Populations, Samples and Parameters
Statistics and SamplingDistributions
Properties of Estimators
Derivation of Estimators
Hypothesis Testing
Further Topics in Hypothesis Testing
Inferenceis Conditional on the Model
Econometrics and Statistics
StatisticalMethodology and the Philosophy of Science
Relationships between variables
Covariance and Correlation
Regression
Deviation Form Notation
Conclusions
Simple Regression
Model Specification
Least Squares Estimation
Sampling Properties of the Least Squares Estimators
The Sampling Distributions of ^a and ^B
Hypothesis Testing
Decomposition of Sample Variation
Presentation of Regression Results
Scaling and Units of Measure
Sampling, Numerical, and Invariance Properties
Application: Output and Production Costs
Supplementary Topics In Regression
Forecasting
RegressionThroughtheOrigin
WhenRegressionGoesWrong
Matters Of Functional Form
Loglinear Models
Log-Lin Models
Lin-Log Models
Reciprocal Models
Application: Engel Curves
Conclusions
Applications To Production Functions
General Features of ProductionFunctions
The Cobb-DouglasProduction Function
Technical Change
Testing MarginalProductivity Conditions
Conclusions
Multiple Regression
Model Specification
LeastSquares Estimation
Properties of Least SquaresEstimators
Hypothesis Testing
Decomposition of a Sample Variation
Application: Electricity Demand
Multicollinearity
Application:the Quadratic CostFunction
ModelMisspecification
Pre-TestEstimation
Application to Economic Growth
Introduction
The Textbook
Solow-Swan Model
Human Capital in the Solow-Swan Model
Summary: Mankiw, Romer, and Weil in a Nutshell
Conclusions
Dummy Variables And Restricted Coefficients
Dummy Variables
Restricted Coefficients
Identification
Applications To Cost Functions
The Cost Function
Deriving the Cost Function
Using the CostFunction
Returns to Scale in Electricity Generation
The TranslogCost Function
Consumer Demand
Further Reading
Model Discovery
Data Mining
SpecificationTesting
Non-nestedTesting
ModelChoice
Should theEquationBe Part of aSystem?
Conclusions
Nonlinear Regression
Introduction
Nonlinear LeastSquares
ComputerNumerics
Reparameterization
Identification
Sampling Properties of NLS Estimators
EstimatingSigma
Hypothesis Testing
Conclusions
Heteroskedasticity
Consequences for Ordinary Least Squares
Heteroskedasticity-RobustTests
WeightedLeastSquares
Testing forHeteroskedasticity
Time Series: Some Basic Concepts
Introduction
White Noise
Measuring Temporal Dependence
Stationarity and Nonstationarity
Trend Stationary Processes
A Random Walk
A RandomWalkwithDrift
KeyPropertiesofRandomWalks
Conclusions
Fluctuations
Introduction
Moving AverageProcesses
AutoregressiveProcesses
TheStationarity Condition
Key Properties ofMoving Average andAutoregressiveProcesses
Autoregressive-Moving Average Processes
Trends
The Constant Growth Model Revisited
Trend and Difference Stationary Processes
Testing for StochasticTrends
Higher Orders of Integration
Cointegration
Long Run RelationshipsBetween Variables
Relationships BetweenVariables
The Arithmetic ofIntegrated Processes
Cointegration
TheEngle-Granger Test forCointegration
Testing Restrictions onthe Cointegrating Vector
ErrorCorrection Models
The ECMof VAR
CointegratingRank
Conclusions and Further Reading
Lawsof Summation And Deviation Form
Laws ofSummation
Laws ofDeviation Form
Distribution Theory
Random Variables and Probability Distribution
MathematicalExpectation
Expected Value of a Function
Variance
Varianceof a Function
Standardized Random Variables
Bivariate Distributions
Conditional Distributions and Expectation
Statistical Independence
Functions of Two Random Variables
Variance of a Linear Comb