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Applied Econometrics Using the SAS System [Pehme köide]

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(Ameriprise Financials, Minneapolis, MN)
  • Formaat: Paperback / softback, 328 pages, kõrgus x laius x paksus: 279x216x19 mm, kaal: 767 g, Graphs: 32 B&W, 0 Color
  • Ilmumisaeg: 03-Jul-2009
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470129492
  • ISBN-13: 9780470129494
Teised raamatud teemal:
Applied Econometrics Using the SAS SystemSuurem pilt
  • Formaat: Paperback / softback, 328 pages, kõrgus x laius x paksus: 279x216x19 mm, kaal: 767 g, Graphs: 32 B&W, 0 Color
  • Ilmumisaeg: 03-Jul-2009
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470129492
  • ISBN-13: 9780470129494
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470129494.html
Märksõnad:
The first cutting-edge guide to using the SAS® system for the analysis of econometric data Applied Econometrics Using the SAS® System is the first book of its kind to treat the analysis of basic econometric data using SAS®, one of the most commonly used software tools among today's statisticians in business and industry. This book thoroughly examines econometric methods and discusses how data collected in economic studies can easily be analyzed using the SAS® system.

In addition to addressing the computational aspects of econometric data analysis, the author provides a statistical foundation by introducing the underlying theory behind each method before delving into the related SAS® routines. The book begins with a basic introduction to econometrics and the relationship between classical regression analysis models and econometric models. Subsequent chapters balance essential concepts with SAS® tools and cover key topics such as:





Regression analysis using Proc IML and Proc Reg



Hypothesis testing



Instrumental variables analysis, with a discussion of measurement errors, the assumptions incorporated into the analysis, and specification tests 



Heteroscedasticity, including GLS and FGLS estimation, group-wise heteroscedasticity, and GARCH models



Panel data analysis



Discrete choice models, along with coverage of binary choice models and Poisson regression



Duration analysis models





Assuming only a working knowledge of SAS®, this book is a one-stop reference for using the software to analyze econometric data. Additional features include complete SAS® code, Proc IML routines plus a tutorial on Proc IML, and an appendix with additional programs and data sets. Applied Econometrics Using the SAS® System serves as a relevant and valuable reference for practitioners in the fields of business, economics, and finance. In addition, most students of econometrics are taught using GAUSS and STATA, yet SAS® is the standard in the working world; therefore, this book is an ideal supplement for upper-undergraduate and graduate courses in statistics, economics, and other social sciences since it prepares readers for real-world careers.

Arvustused

The text serves as a relevant and valuable reference for practitioners in the fields of business, economics, and finance. In addition, most students of econometrics are taught using GAUSS and STATA, yet SAS is the standard in the working world; therefore, this book is an ideal supplement for upper-undergraduate and graduate courses in statistics, economics, and other social sciences since it prepares readers for real-world careers. (Zentralblatt MATH, 2012)

 

Preface xi
Acknowledgments xv
Introduction Regression Analysis
1(8)
Introduction
1(2)
Matrix Form of the Multiple Regression Model
3(1)
Basic Theory of Least Squares
3(2)
Analysis of Variance
5(1)
The Frisch-Waugh Theorem
6(1)
Goodness of Fit
6(1)
Hypothesis Testing and Confidence Intervals
7(1)
Some Further Notes
8(1)
Regression Analysis Using Proc IML and Proc Reg
9(18)
Introduction
9(1)
Regression Analysis Using Proc IML
9(3)
Analyzing the Data Using Proc Reg
12(2)
Extending the Investment Equation Model to the Complete Data Set
14(1)
Plotting the Data
15(1)
Correlation Between Variables
16(2)
Predictions of the Dependent Variable
18(3)
Residual Analysis
21(3)
Multicollinearity
24(3)
Hypothesis Testing
27(25)
Introduction
27(2)
Using SAS to Conduct the General Linear Hypothesis
29(2)
The Restricted Least Squares Estimator
31(2)
Alternative Methods of Testing the General Linear Hypothesis
33(5)
Testing for Structural Breaks in Data
38(3)
The CUSUM Test
41(4)
Models with Dummy Variables
45(7)
Instrumental Variables
52(18)
Introduction
52(1)
Omitted Variable Bias
53(1)
Measurement Errors
54(1)
Instrumental Variable Estimation
55(6)
Specification Tests
61(9)
Nonspherical Disturbances and Heteroscedasticity
70(23)
Introduction
70(1)
Nonspherical Disturbances
71(1)
Detecting Heteroscedasticity
72(2)
Formal Hypothesis Tests to Detect Heteroscedasticity
74(6)
Estimation of β Revisited
80(4)
Weighted Least Squares and FGLS Estimation
84(3)
Autoregressive Conditional Heteroscedasticity
87(6)
Autocorrelation
93(17)
Introduction
93(1)
Problems Associated with OLS Estimation Under Autocorrelation
94(1)
Estimation Under the Assumption of Serial Correlation
95(1)
Detecting Autocorrelation
96(5)
Using SAS to Fit the AR Models
101(9)
Panel Data Analysis
110(22)
What is Panel Data?
110(1)
Panel Data Models
111(1)
The Pooled Regression Model
112(1)
The Fixed Effects Model
113(10)
Random Effects Models
123(9)
Systems of Regression Equations
132(10)
Introduction
132(1)
Estimation Using Generalized Least Squares
133(1)
Special Cases of the Seemingly Unrelated Regression Model
133(1)
Feasible Generalized Least Squres
134(8)
Simultaneous Equations
142(11)
Introduction
142(1)
Problems with OLS Estimation
142(2)
Structural and Reduced Form Equations
144(1)
The Problem of Identification
145(2)
Estimation of Simultaneous Equation Models
147(4)
Hausman's Specification Test
151(2)
Discrete Choice Models
153(16)
Introduction
153(1)
Binary Response Models
154(9)
Poisson Regression
163(6)
Duration Analysis
169(33)
Introduction
169(1)
Failure Times and Censoring
169(1)
The Survival and Hazard Functions
170(8)
Commonly Used Distribution Functions in Duration Analysis
178(8)
Regression Analysis with Duration Data
186(16)
Special Topics
202(35)
Iterative FGLS Estimation Under Heteroscedasticity
202(1)
Maximum Likelihood Estimation Under Heteroscedasticity
202(2)
Harvey's Multiplicative Heteroscedasticity
204(1)
Groupwise Heteroscedasticity
205(5)
Hausman-Taylor Estimator for the Random Effects Model
210(9)
Robust Estimation of Covariance Matrices in Panel Data
219(1)
Dynamic Panel Data Models
220(4)
Heterogeneity and Autocorrelation in Panel Data Models
224(3)
Autocorrelation in Panel Data
227(10)
Appendix A Basic Matrix Algebra for Econometrics
237(12)
Matrix Definitions
237(1)
Matrix Operations
238(1)
Basic Laws of Matrix Algebra
239(1)
Identity Matrix
240(1)
Transpose of a Matrix
240(1)
Determinants
241(1)
Trace of a Matrix
241(1)
Matrix Inverses
242(1)
Idempotent Matrices
243(1)
Kronecker Products
244(1)
Some Common Matrix Notations
244(1)
Linear Dependence and Rank
245(1)
Differential Calculus in Matrix Algebra
246(2)
Solving a System of Linear Equations in Proc IML
248(1)
Appendix B Basic Matrix Operations in Proc IML
249(6)
Assigning Scalars
249(1)
Creating Matrices and Vectors
249(1)
Elementary Matrix Operations
250(1)
Comparison Operators
251(1)
Matrix-Generating Functions
251(1)
Subset of Matrices
251(1)
Subscript Reduction Operators
251(1)
The Diag and VecDiag Commands
252(1)
Concatenation of Matrices
252(1)
Control Statements
252(1)
Calculating Summary Statistics in Proc IML
253(2)
Appendix C Simulating the Large Sample Properties of the OLS Estimators
255(7)
Appendix D Introduction to Bootstrap Estimation
262(10)
Introduction
262(2)
Calculating Standard Errors
264(1)
Bootstrapping in SAS
264(1)
Bootstrapping in Regression Analysis
265(7)
Appendix E Complete Programs and Proc IML Routines
272(27)
Program 1
272(1)
Program 2
273(1)
Program 3
274(1)
Program 4
275(1)
Program 5
276(1)
Program 6
277(1)
Program 7
278(1)
Program 8
279(1)
Program 9
280(1)
Program 10
281(2)
Program 11
283(1)
Program 12
284(2)
Program 13
286(1)
Program 14
287(2)
Program 15
289(1)
Program 16
290(3)
Program 17
293(6)
References 299(4)
Index 303
Vivek B. Ajmani, PhD, is Senior Marketing Analyst at U.S. Bank in St. Paul, Minnesota, where he applies econometric modeling, data mining, and predictive modeling techniques to his work with innovative banking products and solutions. Dr. Ajmani has also held positions at Ameriprise Financial, General Mills, Intel Corporation, and the 3M Company, and he has received honors for his use of statistics in the development of quality products.