Boldrin, Michele, and David K. Levine. 2008.
“Perfectly Competitive Innovation.” Journal of
Monetary Economics
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ECONOMIC GROWTH Second Edition Robert J. Barro AND Xavier Sala-i-Martin
This graduate level text on economic growth surveys neoclassical and more recent growth theories, stressing their empirical implications and the relation of theory to data and evidence. The authors have undertaken a major revision for the long-awaited second edition of this widely used text, the first modern textbook devoted to growth theory. The book has been expanded in many areas and incorporates the latest research. After an introductory discussion of economic growth, the book examines neoclassical growth theories, from Solow-Swan in the 1950s and Cass-Koopmans in the 1960s to more recent refinements; this is followed by a discussion of extensions to the model, with expanded treatment in this edition of heterogenity of households. The book then turns to endogenous growth theory, discussing, among other topics, models of endogenous technological progress (with an expanded discussion in this edition of the role of outside competition in the growth process), technological diffusion, and an endogenous determination of labor supply and population. The authors then explain the essentials of growth accounting and apply this framework to endogenous growth models. The final chapters cover empirical analysis of regions and empirical evidence on economic growth for a broad panel of countries from 1960 to 2000. The updated treatment of cross-country growth regressions for this edition uses the new Summers-Heston data set on world income distribution compiled through 2000.
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Neural Networks in Finance: Gaining Predictive Edge in the Market. Paul D. McNelis
Review
"This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore. I recommend it to anyone who wants to understand methods used in nonlinear forecasting."-- Blake LeBaron, Professor of Finance, Brandeis University"An important addition to the select collection of books on financial econometrics, Paul Mcnelis' volume, Neural Networks in Finance, serves as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets." -- Roberto S. Mariano, Dean of School of Economics and Social Sciences & Vice-Provost for Research, Singapore Management University; Professor Emeritus of Economics, University of Pennsylvania"This book represents an impressive step forward in the exposition and application of evolutionary computational tools. The author illustrates the potency of evolutionary computational tools through multiple examples, which contrast the predictive outcomes from the evolutionary approach with others of a linear and general non-linear variety. The book will be of utmost appeal to both academics throughout the social sciences as well as practitioners, especially in the area of finance." -- Carlos Asilis, Portfolio Manager, VegaPlus Capital Partners; formerly Chief Investment Strategist, JPMorgan Chase "...an excellent, easy-to read introduction to the math behind neural networks."- Financial Engineering News
Modeling Structured Finance Cash Flows with Microsoft Excel: A Step-by-Step Guide
Description
A practical guide to building fully operational financial cash flow models for structured finance transactions
Structured finance and securitization deals are becoming more commonplace on Wall Street. Up until now, however, market participants have had to create their own models to analyze these deals, and new entrants have had to learn as they go. Modeling Structured Finance Cash Flows with Microsoft Excel provides readers with the information they need to build a cash flow model for structured finance and securitization deals. Financial professional Keith Allman explains individual functions and formulas, while also explaining the theory behind the spreadsheets. Each chapter begins with a discussion of theory, followed by a section called "Model Builder," in which Allman translates the theory into functions and formulas. In addition, the companion website features all of the modeling exercises, as well as a final version of the model that is created in the text.
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