Econometric Methods with Applications in Business and Economics
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Product details:
- Publisher OUP Oxford
- Date of Publication 25 March 2004
- ISBN 9780199268016
- Binding Hardback
- No. of pages816 pages
- Size 253x194x46 mm
- Weight 1735 g
- Language English
- Illustrations numerous figures and tables 0
Categories
Short description:
This rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. It covers basic econometric methods and addresses the creative process of model building. Using real-world examples and exercises, it focuses on regression and covers choice data and time series data. Perfect for advanced undergraduate students, new graduate students, and applied researchers.
MoreLong description:
Nowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and hands-on experience of current econometrics.
Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations).
? Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical questions in modern business and economic management.
? Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics.
? Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions.
? Derivations and theory exercises are clearly marked for students in advanced courses.
This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied econometrics.
'. . . students will find the contents of this book to be a very helpful guide . . . Because of its wide coverage and careful presentation the book should be useful for a diverse group of students in many countries and interested in a variety of areas of applications.'
Table of Contents:
Introduction
1 Review of Statistics
Descriptive statistics
Random variables
Parameter estimation
Tests of hypotheses
Summary, further reading, and keywords
Exercises
2 Simple Regression
Least squares
Accuracy of least squares
Significance tests
Prediction
Summary, further reading, and keywords
Exercises
3 Multiple Regression
Least squares in matrix form
Adding or deleting variables
The accuracy of estimates
The F-test
Summary, further reading, and keywords
Exercises
4 Non-Linear Methods
Asymptotic analysis
Non-linear regression
Maximum likelihood
Generalized method of moments
Summary, further reading, and keywords
Exercises
5 Diagnostic Tests and Model Adjustments
Introduction
Functional form and explanatory variables
Varying parameters
Heteroskedasticity
Serial correlation
Disturbance distribution
Endogenous regressors and instrumental variables
Illustration: Salaries of top managers
Summary, further reading, and keywords
Exercises
6 Qualitative and Limited Dependent Variables
Binary response
Multinomial data
Limited dependent variables
Summary, further reading, and keywords
Exercises
7 Time Series and Dynamic Models
Models for stationary time series
Model estimation and selection
Trends and seasonals
Non-linearities and time-varying volatility
Regression models with lags
Vector autoregressive models
Other multiple equation models
Summary, further reading, and keywords
Exercises
Appendix A: Matrix Methods
Summations
Vectors and matrices
Matrix addition and multiplication
Transpose, trace, and inverse
Determinant, rank, and eigenvalues
Positive (semi)definite matrices and projections
Optimization of a function of several variables
Concentration and the Lagrange method
Exercise
Appendix B: Data Sets
Index
