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## Über dieses Buch

This book deals with a number of mathematical topics that are of great importance in the study of classical econometrics. There is a lengthy chapter on matrix algebra, which takes the reader from the most elementary aspects to the partitioned inverses, characteristic roots and vectors, symmetric, and orthogonal and positive (semi) definite matrices. The book also covers pseudo-inverses, solutions to systems of linear equations, solutions of vector difference equations with constant coefficients and random forcing functions, matrix differentiation, and permutation matrices. Its novel features include an introduction to asymptotic expansions, and examples of applications to the general-linear model (regression) and the general linear structural econometric model (simultaneous equations).

## Inhaltsverzeichnis

### Chapter 1. Vectors and Vector Spaces

Abstract
In nearly all of the discussion in this volume, we deal with the set of real numbers. Occasionally, however, we deal with complex numbers as well.
Phoebus J. Dhrymes

### Chapter 2. Matrix Algebra

Abstract
Definition 2.1. Let $$a_{ij} \in \mathcal{F},$$ i = 1,2,,m, j = 1,2,,n, where $$\mathcal{F}$$ is a suitable space, such as the one-dimensional Euclidean or complex space.
Phoebus J. Dhrymes

### Chapter 3. Systems of Linear Equations

Abstract
Consider the system of linear equations
$$\displaystyle{ Ax = b, }$$
(3.1)
where A is m × n and b is an m-element vector.
Phoebus J. Dhrymes

### Chapter 4. Matrix Vectorization

Abstract
It is frequently more convenient to write a matrix in vector form. For lack of a suitable term, we have coined for this operation the phrase “vectorization of a matrix”.
Phoebus J. Dhrymes

### Chapter 5. Vector and Matrix Differentiation

Abstract
Frequently, we need to differentiate quantities like tr(AX) with respect to the elements of X, or quantities like Ax, zAx with respect to the elements of (the vectors) x and/or z.
Phoebus J. Dhrymes

### Chapter 6. DE Lag Operators GLSEM and Time Series

Abstract
In this chapter we deal with econometric applications of (vector) difference equations with constant coefficients, as well as with aspects of the statistical theory of time series and their application in econometrics.
Phoebus J. Dhrymes

### Chapter 7. Mathematical Underpinnings of Probability Theory

Abstract
The purpose of this chapter is to provide a background on the results from probability and inference theory required for the study of several of the topics of contemporary econometrics.
An attempt will be made to give proofs for as many propositions as is consistent with the objectives of this chapter which are to provide the tools deemed necessary for the exposition of several topics in econometric theory; it is clearly not our objective to provide a substitute to a mathematical textbook of modern probability theory.
Phoebus J. Dhrymes

### Chapter 8. Foundations of Probability

Abstract
Consider the problem of constructing a model of the process (experiment) of throwing a die and observing the outcome; in doing so, we need to impose on the experiment a certain probabilistic framework since the same die thrown under ostensibly identical circumstances, generally, yields different outcomes. The framework represents, primarily, the investigator’s view of the nature of the process, but it must also conform to certain logical rules.
Phoebus J. Dhrymes

### Chapter 9. LLN, CLT and Ergodicity

Abstract
We recall from Chap. 8 that discussion of random variables (r.v.) takes place in a probability space (Ω, $$\mathcal{A}$$, $$\mathcal{P}$$), where Ω is the sample space, $$\mathcal{A}$$ is the σ-algebra and $$\mathcal{P}$$ is the probability measure.
Phoebus J. Dhrymes

### Chapter 10. The General Linear Model

Abstract
In this chapter, we examine the General Linear Model (GLM), an important topic for econometrics and statistics, as well as other disciplines. The term general refers to the fact that there are no restrictions in the number of explanatory variables we may consider, the term linear refers to the manner in which the parameters enter the model. It does not refer to the form of the variables. This is often termed in the literature the regression model, and analysis of empirical results obtained from such models as regression analysis.
Phoebus J. Dhrymes

### Chapter 11. Panel Data Models

Abstract
The study of empirical models based on cross-section times series data dates back well into the early 1950s. However, the first modern attempt to consistently model the behavior of agents in such contexts can be traced to Balestra and Nerlove (1966), hereafter referred to as BN, who studied the demand for natural gas by state in the US, over the period 1957–1962.
Phoebus J. Dhrymes

### Chapter 12. GLSEM and TS Models

Abstract
In this chapter, we take up two important applications, involving simultaneous equation, AR, and ARMA models, which are very important in several disciplines, such as economics, other social sciences, engineering and statistics. We partially discussed such topics in Chap. 4, in the context of difference equations, since simultaneous equation and AR models involve the use of difference equations and it is important to establish the nature and the properties of their solutions.
Phoebus J. Dhrymes

### Chapter 13. Asymptotic Expansions

Abstract
This chapter deals with situations in which we wish to approximate the limiting distribution of an estimator. As such it is different from other chapters in that it does not discuss topics in core econometrics and the ancillary mathematics needed to develop and fully understand them. Moreover, its purpose is different from that of the earlier (theoretical) chapters. Its aim is not only to introduce certain (additional) mathematical concepts but also to derive certain results that may prove useful for econometric applications involving hypothesis testing.
Phoebus J. Dhrymes

### Backmatter

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