Some Linear Algebra for Econometrics by Frank Pinter - PDF

About this book :-
"Some Linear Algebra for Econometrics" by Frank Pinter is a concise and practical short guide written for economics and econometrics students who need a clear understanding of "linear algebra" without excessive abstraction. This short book focuses on the most useful mathematical tools required to follow econometric theory, making it ideal for students transitioning into graduate-level coursework or research. The content covers essential topics such as "vector spaces", "matrices", "eigenvalues", "orthogonality", and "projections", all explained with an emphasis on intuition rather than long proofs. These concepts are directly connected to econometric ideas like least squares estimation, regression geometry, and multivariate models. The writing is clear, structured, and purpose-driven, helping readers see why each topic matters in applied economics. The book is well suited for students who want to build strong mathematical foundations for econometrics while staying focused on real analytical applications instead of pure theory.

Book Detail :-
Title: Some Linear Algebra for Econometrics by Frank Pinter - PDF
Publisher: Frank Pinter
Year: 2020
Pages: 17
Type: PDF
Language: English
ISBN-10 #: N/A
ISBN-13 #: N/A
License: CC BY 4.0
Amazon: Amazon

About Author :-
The author Frank Pinter is an economist and educator known for teaching "mathematics for econometrics" in a clear and practical way. He has taught at the university level and contributed to "math camps" that prepare students for graduate economics programs, focusing on usable mathematical foundations rather than abstract theory. As an author, Pinter emphasizes "linear algebra", "econometric intuition", "clear explanations", and "student-friendly notes". His writing helps readers connect mathematical concepts directly to econometric methods, making his work especially valuable for students strengthening their quantitative skills efficiently.

Book Contents :-
1. Vector Space Preliminaries, Including Basis and Dimension 2. Linear Transformations and Matrix Basics 3. Trace, Determinant, and Eigenvalues 4. Inner Products and Orthogonality 5. Projections and the Classical Projection Theorem 6. Basic Matrix Decompositions: Singular Value and Cholesky 7. The Kronecker Tensor Product

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