[资料] Heij Econometric Methods with Applications in Business and Economics 高清附书签 [推广有奖]

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Timtw 发表于 2018-5-7 00:55:17 |AI写论文

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(Heij, Boer, Franses, Kloek, & Dijk, 2004)

Heij Econometric Methods with Applications inBusiness and Economics

高清原始电子文件(非Scan版) 附书签

Heij, C., Boer, P.d., Franses, P. H., Kloek, T., & Dijk, H. K. v. (2004). Econometric Methods with Applications inBusiness and Economics: Oxford University Press.

高清原始电子文件(非Scan版) 附书签(论坛其他版本书签有误)

Introduction
1Review of Statistics
1.1. Descriptive statistics
1.2. Random variables
1.3. Parameter estimation
1.4. Tests of hypotheses
Summary, further reading, and keywords
Exercises
2Simple Regression
2.1. Least squares
2.2. Accuracy of least squares
2.3. Significance tests
2.4. Prediction
Summary, further reading, and keywords
Exercises
3Multiple Regression
3.1. Least squares in matrix form
3.2. Adding or deleting variables
3.3. The accuracy of estimates
3.4. The F-test
Summary, further reading, and keywords
Exercises
4Non-Linear Methods
4.1. Asymptotic analysis
4.2. Non-linear regression
4.3. Maximum likelihood
4.4. Generalized method of moments
Summary, further reading, and keywords
Exercises
5Diagnostic Tests and Model Adjustments
5.1. Introduction
5.2. Functional form and explanatory variables
5.3. Varying parameters
5.4. Heteroskedasticity
5.5. Serial correlation
5.6. Disturbance distribution
5.7. Endogenous regressors and instrumental variables
5.8. Illustration: Salaries of top managers
Summary, further reading, and keywords
Exercises
6Qualitative and Limited Dependent Variables
6.1. Binary response
6.2. Multinomial data
6.3. Limited dependent variables
Summary, further reading, and keywords
Exercises
7Time Series and Dynamic Models
7.1. Models for stationary time series
7.2. Model estimation and selection
7.3. Trends and seasonals
7.4. Non-linearities and time-varying volatility
7.5. Regression models with lags
7.6. Vector autoregressive models
7.7. Other multiple equation models
Summary, further reading, and keywords
Exercises
AppendixA: Matrix Methods
A.1. Summations
A.2. Vectors and matrices
A.3. Matrix addition and multiplication
A.4. Transpose, trace, and inverse
A.5. Determinant, rank, and eigenvalues
A.6. Positive (semi)definite matrices and projections
A.7. Optimization of a function of several variables
A.8. Concentration and the Lagrange method
Exercise
AppendixB: Data Sets
Index