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Applied Econometrics A Practical Guide

Applied Econometrics A Practical Guide

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics: Endogeneity and Two-stage Least Squares Simultaneous Equations Models Panel Data Models Qualitative and Limited Dependent Variable Models Vector Autoregressive (VAR) Models Autocorrelation and ARCH/GARCH Models Unit Root and Cointegration The book also illustrates the use of computer software (EViews SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental go-to guide for solid foundation in the fundamentals of econometrics. In addition this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases. | Applied Econometrics A Practical Guide

GBP 68.99
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Deep Learning in Practice

Deep Learning in Practice

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python NumPy and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project step by step.

GBP 77.99
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Numerical Methods for Finance

Numerical Methods for Finance

Featuring international contributors from both industry and academia Numerical Methods for Finance explores new and relevant numerical methods for the solution of practical problems in finance. It is one of the few books entirely devoted to numerical methods as applied to the financial field. Presenting state-of-the-art methods in this area the book first discusses the coherent risk measures theory and how it applies to practical risk management. It then proposes a new method for pricing high-dimensional American options followed by a description of the negative inter-risk diversification effects between credit and market risk. After evaluating counterparty risk for interest rate payoffs the text considers strategies and issues concerning defined contribution pension plans and participating life insurance contracts. It also develops a computationally efficient swaption pricing technology extracts the underlying asset price distribution implied by option prices and proposes a hybrid GARCH model as well as a new affine point process framework. In addition the book examines performance-dependent options variance reduction Value at Risk (VaR) the differential evolution optimizer and put-call-futures parity arbitrage opportunities. Sponsored by DEPFA Bank IDA Ireland and Pioneer Investments this concise and well-illustrated book equips practitioners with the necessary information to make important financial decisions.

GBP 59.99
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