Tensor Networks and Multilinear Algebra in Econometrics: With Python (Richman Computational Economics) [Print Replica] Kindle Edition

Unlock the potential of advanced mathematical tools in econometrics with this comprehensive guide on tensor networks and multilinear algebra. Discover how these cutting-edge techniques can transform econometric analysis, enabling you to handle complex, high-dimensional datasets with ease and precision.Key Features:In-Depth Exploration: Dive into the rich landscape of tensor methods and multilinear algebra tailored for econometrics.Practical Python Code: Implement the concepts with practical examples and code snippets provided in Python for each chapter, ensuring hands-on experience.Versatile Application: Learn how these methodologies enhance econometric models, data analysis, machine learning, and more.Book Description:This book serves as a definitive resource for econometricians, data scientists, and researchers eager to harness the power of tensor computations and multilinear algebra. It begins with fundamental concepts and progresses to advanced applications, all supported by Python code examples. Whether you're dealing with time series data, panel data, or high-dimensional datasets, you'll find innovative solutions and tools to streamline your econometric modeling processes.What You Will Learn:Master the Kronecker Product for constructing robust multilinear models.Simplify tensor data using Canonical Polyadic Decomposition for econometric analysis.Apply Tucker Decomposition to effectively reduce tensor dimensionality.Implement Higher-Order Singular Value Decomposition (HOSVD) for tensor applications.Explore PARAFAC for comprehensive multi-dimensional econometric factor analysis.Build and refine tensor regression models for high-dimensional data.Employ Alternating Least Squares (ALS) for efficient tensor decomposition computation.Understand Kruskal's Theorem to ensure identifiability in tensor models.Utilize Multilinear Principal Component Analysis to extract key data features.Apply tensor completion methods to handle missing econometric data sets.Leverage tensor networks to model time series data effectively.Extend eigenvalue decomposition techniques to tensor spaces for advanced applications.Analyze panel data with innovative tensor-based methods.Develop Tensor-based Vector Autoregression (VAR) models for complex time series.Use multilinear rank to streamline econometric model complexity.Design and implement tensor-based estimators for efficient modeling.Investigate conditional independence in econometric tensor models.Explore structural equation modeling with tensor techniques.Employ regularization techniques like Lasso and Ridge in tensor regression.Construct nonlinear econometric models using tensor computations.Develop parameter identification strategies in tensor-based models.Utilize optimization algorithms to boost tensor calculations.Incorporate Bayesian methods in tensor factorization for probabilistic modeling.Solve tensor-related problems with convex optimization principles.Optimize tensor learning with stochastic gradient descent methods.Tackle high-dimensional econometric challenges using tensor networks.Implement randomized algorithms for scalable tensor computations.Integrate tensor methods with machine learning in econometrics.Design efficient data structures for tensor computations.Apply hierarchical Tucker decomposition for large-scale data reduction.Conduct classification tasks with multilinear discriminant analysis in econometrics.Boost model efficiency with sparse tensor methods.Investigate quantum tensor networks for groundbreaking data analysis. Read more