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"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer

"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com

Award-winning | Used by over 30 universities | Translated into 9 languages

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.

How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.

How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.

A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a

With over one million copies sold, The Undercover Economist has been hailed worldwide as a fantastic guide to the fundamental principles of economics. An economist's version of The Way Things Work, this engaging volume is part Economics 101 and part exposé of the economic principles lurking behind daily events, explaining everything from traffic jams to high coffee prices. New to this edition: This revised edition, newly updated to consider the banking crisis and economic turbulence of the last four years, is essential for anyone who has wondered why the gap between rich and poor nations is so great, or why they can't seem to find a decent second-hand car, or how to outwit Starbucks. Senior columnist for the Financial Times Tim Harford brings his experience and insight as he ranges from Africa, Asia, Europe, and the United States to reveal how supermarkets, airlines, and coffee chains--to name just a few--are vacuuming money from our wallets. Harford punctures the myths surrounding some of today's biggest controversies, including the high cost of health-care; he reveals why certain environmental laws can put a smile on a landlord's face; and he explains why some industries can have high profits for innocent reasons, while in other industries something sinister is going on. Covering an array of economic concepts including scarce resources, market power, efficiency, price gouging, market failure, inside information, and game theory, Harford sheds light on how these forces shape our day-to-day lives, often without our knowing it. Showing us the world through the eyes of an economist, Tim Harford reveals that everyday events are intricate games of negotiations, contests of strength, and battles of wits. Written with a light touch and sly wit, The Undercover Economist turns "the dismal science" into a true delight.
An accessible and fun guide to the essential tools of econometric research

Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful.

The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse.

Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect.

Shows why econometrics is importantExplains econometric research through humorous and accessible discussionOutlines empirical methods central to modern econometric practiceWorks through interesting and relevant real-world examples
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer

"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com

Award-winning | Used by over 30 universities | Translated into 9 languages

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.

How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.

How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.

A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a

DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level.

The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0.

It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals.

* First book on the new generation of data warehouse architecture, DW 2.0.
* Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network.
* Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.
With over one million copies sold, The Undercover Economist has been hailed worldwide as a fantastic guide to the fundamental principles of economics. An economist's version of The Way Things Work, this engaging volume is part Economics 101 and part exposé of the economic principles lurking behind daily events, explaining everything from traffic jams to high coffee prices. New to this edition: This revised edition, newly updated to consider the banking crisis and economic turbulence of the last four years, is essential for anyone who has wondered why the gap between rich and poor nations is so great, or why they can't seem to find a decent second-hand car, or how to outwit Starbucks. Senior columnist for the Financial Times Tim Harford brings his experience and insight as he ranges from Africa, Asia, Europe, and the United States to reveal how supermarkets, airlines, and coffee chains--to name just a few--are vacuuming money from our wallets. Harford punctures the myths surrounding some of today's biggest controversies, including the high cost of health-care; he reveals why certain environmental laws can put a smile on a landlord's face; and he explains why some industries can have high profits for innocent reasons, while in other industries something sinister is going on. Covering an array of economic concepts including scarce resources, market power, efficiency, price gouging, market failure, inside information, and game theory, Harford sheds light on how these forces shape our day-to-day lives, often without our knowing it. Showing us the world through the eyes of an economist, Tim Harford reveals that everyday events are intricate games of negotiations, contests of strength, and battles of wits. Written with a light touch and sly wit, The Undercover Economist turns "the dismal science" into a true delight.
Structural Macroeconometrics provides a thorough overview and in-depth exploration of methodologies, models, and techniques used to analyze forces shaping national economies. In this thoroughly revised second edition, David DeJong and Chetan Dave emphasize time series econometrics and unite theoretical and empirical research, while taking into account important new advances in the field.

The authors detail strategies for solving dynamic structural models and present the full range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The authors look at recent strides that have been made to enhance numerical efficiency, consider the expanded applicability of dynamic factor models, and examine the use of alternative assumptions involving learning and rational inattention on the part of decision makers. The treatment of methodologies for obtaining nonlinear model representations has been expanded, and linear and nonlinear model representations are integrated throughout the text. The book offers a rich array of implementation algorithms, sample empirical applications, and supporting computer code.



Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.

A systematic comparison of the three major economic theories, showing how they differ and why these differences matter in shaping economic theory and practice.

Contending Economic Theories offers a unique comparative treatment of the three main theories in economics as it is taught today: neoclassical, Keynesian, and Marxian. Each is developed and discussed in its own chapter, yet also differentiated from and compared to the other two theories. The authors identify each theory's starting point, its goals and foci, and its internal logic. They connect their comparative theory analysis to the larger policy issues that divide the rival camps of theorists around such central issues as the role government should play in the economy and the class structure of production, stressing the different analytical, policy, and social decisions that flow from each theory's conceptualization of economics.

The authors, building on their earlier book Economics: Marxian versus Neoclassical, offer an expanded treatment of Keynesian economics and a comprehensive introduction to Marxian economics, including its class analysis of society. Beyond providing a systematic explanation of the logic and structure of standard neoclassical theory, they analyze recent extensions and developments of that theory around such topics as market imperfections, information economics, new theories of equilibrium, and behavioral economics, considering whether these advances represent new paradigms or merely adjustments to the standard theory. They also explain why economic reasoning has varied among these three approaches throughout the twentieth century, and why this variation continues today—as neoclassical views give way to new Keynesian approaches in the wake of the economic collapse of 2008.

Hayashi's Econometrics promises to be the next great synthesis of modern econometrics. It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results.

Econometrics has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.


For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining.

Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book
uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes:

Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.

The worlds of Wall Street and The City have always held a certain allure, but in recent years have left an indelible mark on the wider public consciousness and there has been a need to become more financially literate. The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types, whether for general interest or because of the enormous monetary rewards on offer. An Introduction to Quantitative Finance concerns financial derivatives - a derivative being a contract between two entities whose value derives from the price of an underlying financial asset - and the probabilistic tools that were developed to analyse them. The theory in the text is motivated by a desire to provide a suitably rigorous yet accessible foundation to tackle problems the author encountered whilst trading derivatives on Wall Street. The book combines an unusual blend of real-world derivatives trading experience and rigorous academic background. Probability provides the key tools for analysing and valuing derivatives. The price of a derivative is closely linked to the expected value of its pay-out, and suitably scaled derivative prices are martingales, fundamentally important objects in probability theory. The prerequisite for mastering the material is an introductory undergraduate course in probability. The book is otherwise self-contained and in particular requires no additional preparation or exposure to finance. It is suitable for a one-semester course, quickly exposing readers to powerful theory and substantive problems. The book may also appeal to students who have enjoyed probability and have a desire to see how it can be applied. Signposts are given throughout the text to more advanced topics and to different approaches for those looking to take the subject further.
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools

• Illustrations of how to use the outlined concepts in real-world situations

• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials

• Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

HR leaders and practitioners: master the financial analysis skills you need to become true strategic business partners, gain an equal seat at the table, and get boardroom and CFO buy-in for your initiatives! In this one-of-a-kind book, Dr. Steven Director covers everything mid-to-senior-level HR professionals need to formulate, model, and evaluate their HR initiatives from a financial perspective. Drawing on his unsurpassed expertise working with HR executives, he walks through each crucial financial issue associated with strategic talent management, including quantifiable links between workforces and business value, cost-benefit analyses of HR and strategic financial initiatives, and specific issues related to total rewards programs, including stock, stock options, and pension costs. Unlike other finance books for non-financial managers, Financial Analysis for HR Managers focuses entirely on core HR issues. Director helps you answer questions such as: How do you model HR's financial role in corporate strategic initiatives such as the introduction of a new product line? How do you select bonus drivers to send the right signals to managers (and uncover suboptimal hidden signals you might be sending now)? How do you design compensation packages that are fully consistent with your goals? How do you identify and manage pension-finance costs and risks that can dramatically impact the long-term financial health of the business? HR leaders and aspiring leaders are under unprecedented pressure to provide credible, quantitative answers to questions like these. This is the one and only book that will help them do so.
Learn how to onboard ServiceNow ITSM tools by evangelizing, educating, and coordinating your organization's service desk, developers, and stakeholders. Drawing on his own story of lessons learned in spinning up the adoption of ServiceNow throughout the Al Jazeera Media Network, application architect Gabriele Kahlout shows IT service managers how to launch automated ServiceNow ticketing tools in seamless integration with their organization's existing email and Active Directory.
Spinning Up ServiceNow: IT Service Managers' Guide to Successful User Adoption shows you how to orchestrate your IT service desks and developers to facilitate the adoption and consumption of IT services by all users, supporting their various business needs while optimizing human-computer interaction and minimizing stress and productivity loss arising from poor human-system design.
What You'll Learn
Quick-start ServiceNow in a matter of days with the minimum configuration required to start processing tickets via email
Avoid the teething problems that can spoil your users’ onboarding experience with ServiceNow
Automate the process of scaling up new teams into ServiceNowShape your users' experiences so that they retain their familiar bearings in email and Active Directory while welcoming the power of ServiceNow enhancementsCreate a strategy to avoid common pitfalls that sabotage ITSM programs
Who This Book Is For
IT managers charged with implementing ServiceNow ITSM suites in their organizations and business analysts determining the requirements for such implementation. The secondary readership is system administrators and developers involved in ITSM.
Originally published in 2003, Mathematical Techniques in Finance has become a standard textbook for master's-level finance courses containing a significant quantitative element while also being suitable for finance PhD students. This fully revised second edition continues to offer a carefully crafted blend of numerical applications and theoretical grounding in economics, finance, and mathematics, and provides plenty of opportunities for students to practice applied mathematics and cutting-edge finance. Ales Cerný mixes tools from calculus, linear algebra, probability theory, numerical mathematics, and programming to analyze in an accessible way some of the most intriguing problems in financial economics. The textbook is the perfect hands-on introduction to asset pricing, optimal portfolio selection, risk measurement, and investment evaluation.

The new edition includes the most recent research in the area of incomplete markets and unhedgeable risks, adds a chapter on finite difference methods, and thoroughly updates all bibliographic references. Eighty figures, over seventy examples, twenty-five simple ready-to-run computer programs, and several spreadsheets enhance the learning experience. All computer codes have been rewritten using MATLAB and online supplementary materials have been completely updated.


A standard textbook for graduate finance courses
Introduction to asset pricing, portfolio selection, risk measurement, and investment evaluation
Detailed examples and MATLAB codes integrated throughout the text
Exercises and summaries of main points conclude each chapter
A leading economist contends that the recent financial crisis was caused not by the failure of mainstream economics but by corrupted monetary data constructed without reference to economics.

Blame for the recent financial crisis and subsequent recession has commonly been assigned to everyone from Wall Street firms to individual homeowners. It has been widely argued that the crisis and recession were caused by “greed” and the failure of mainstream economics. In Getting It Wrong, leading economist William Barnett argues instead that there was too little use of the relevant economics, especially from the literature on economic measurement. Barnett contends that as financial instruments became more complex, the simple-sum monetary aggregation formulas used by central banks, including the U.S. Federal Reserve, became obsolete. Instead, a major increase in public availability of best-practice data was needed. Households, firms, and governments, lacking the requisite information, incorrectly assessed systemic risk and significantly increased their leverage and risk-taking activities. Better financial data, Barnett argues, could have signaled the misperceptions and prevented the erroneous systemic-risk assessments.

When extensive, best-practice information is not available from the central bank, increased regulation can constrain the adverse consequences of ill-informed decisions. Instead, there was deregulation. The result, Barnett argues, was a worst-case toxic mix: increasing complexity of financial instruments, inadequate and poor-quality data, and declining regulation.

Following his accessible narrative of the deep causes of the crisis and the long history of private and public errors, Barnett provides technical appendixes, containing the mathematical analysis supporting his arguments.

This book presents the numerous tools for the econometric analysis of time series. The text is designed with emphasis on the practical application of theoretical tools. Accordingly, material is presented in a way that is easy to understand. In many cases intuitive explanation and understanding of the studied phenomena are offerd. Essential concepts are illustrated by clear-cut examples. The attention of readers is drawn to numerous applied works where the use of specific techniques is best illustrated. Such applications are chiefly connected with issues of recent economic transition and European integration. The outlined style of presentation makes the book also a rich source of references.
The text is divided into five major sections. The first section, “The Nature of Time Series”, gives an introduction to time series analysis. The second section, “Difference Equations”, describes briefly the theory of difference equations with an emphasis on results that are important for time series econometrics. The third section, “Univariate Time Series”, presents the methods commonly used in univariate time series analysis, the analysis of time series of one single variable. The fourth section, “Multiple Time Series”, deals with time series models of multiple interrelated variables. The fifth section “Panel Data and Unit Root Tests”, deals with methods known as panel unit root tests that are relevant to issues of convergence. Appendices contain an introduction to simulation techniques and statistical tables.

Kniha přináší soubor základních i pokročilých technik a postupů používaných v ekonometrické analýze časových řad. Kniha klade důraz na umožnění efektivního použití popsaných technik v aplikovaném ekonomickém výzkumu. Toho je dosaženo tím, že teoretické základy popsané ekonometrie jsou prezentovány spolu s intuitivním vysvětlením problematiky a jednotlivé techniky jsou ilustrovány na výsledcích současného výzkumu a to především v kontextu procesu nedávné ekonomické transformace a současné evropské integrace. Toto pojetí z knihy činí nejen učebnici v klasickém smyslu, ale také užitečný referenční zdroj neboť odkazy v knize spojují klasickou i moderní ekonometrickou literaturu se soudobými aplikacemi, na nichž je použití jednotlivých technik jasně pochopitelné. Mnohá použití vycházejí z bohaté předchozí práce autorů v oboru.
Text knihy je rozdělen do pěti hlavních částí. První část, “The Nature of Time Series”, přináší úvod do analýzy časových řad a popis jejich nejdůležitějších charakteristik, vlastností a procesů. Druhá část, “Difference Equations”, stručně popisuje teorii diferenciálních rovnic s důrazem na aspekty, které jsou klíčové v ekonometrii časových řad. Třetí část, “Univariate Time Series”, poměrně rozsáhle popisuje techniky, které se používají při analýze jednotlivých časových řad bez jejich vzájemené interakce a zahrnuje jak lineární tak nelineární modelované struktury. Čtvrtá část, “Multiple Time Series”, popisuje modely které umožňují analýzu několika časových řad a jejich vzájemných interakcí. Pátá část “Panel Data and Unit Root Tests”, zahrnuje některé techniky postavené na panelových datech, jež k průřezovým datům přidávají časovou dimenzi a vztahují se k analýze konvergence. Závěr knihy je doplněn o úvod do simulační techniky a statistické tabulky

An introduction to economic applications of the theory of continuous-time finance that strikes a balance between mathematical rigor and economic interpretation of financial market regularities.

This book introduces the economic applications of the theory of continuous-time finance, with the goal of enabling the construction of realistic models, particularly those involving incomplete markets. Indeed, most recent applications of continuous-time finance aim to capture the imperfections and dysfunctions of financial markets—characteristics that became especially apparent during the market turmoil that started in 2008.

The book begins by using discrete time to illustrate the basic mechanisms and introduce such notions as completeness, redundant pricing, and no arbitrage. It develops the continuous-time analog of those mechanisms and introduces the powerful tools of stochastic calculus. Going beyond other textbooks, the book then focuses on the study of markets in which some form of incompleteness, volatility, heterogeneity, friction, or behavioral subtlety arises. After presenting solutions methods for control problems and related partial differential equations, the text examines portfolio optimization and equilibrium in incomplete markets, interest rate and fixed-income modeling, and stochastic volatility. Finally, it presents models where investors form different beliefs or suffer frictions, form habits, or have recursive utilities, studying the effects not only on optimal portfolio choices but also on equilibrium, or the price of primitive securities. The book strikes a balance between mathematical rigor and the need for economic interpretation of financial market regularities, although with an emphasis on the latter.

This book offers guidance for understanding benefits options and plan structures, and making better decisions for your organization. Writing for both HR and finance professionals, internationally respected compensation and benefits professor and consultant Bashker Biswas drills comprehensively into today's most important benefits-related topics and challenges. Employee Benefits Design and Planning covers all this, and much more: Finance and accounting implications of Healthcare benefits Other risk benefits Severance benefits Disability and group life insurance programs Flexible benefits Non-qualified deferred arrangements 409A plans, ESOPs, Money Purchase Pension Plans, Cash Balance Plans, 401(k), 403(b) plans and 457 Plans Employee benefit plan financial reporting, legal compliance, and auditing Employee benefits in mergers and acquisitions Self-funding vs. insurance funding decisions Global employee benefits including umbrella pension plans and multi-national pooling Equity participation in employee benefit plans

Biswas introduces and explains key employee benefit metrics and ratios, and demonstrates best practices for forecasting costs and budgeting appropriately. For all compensation professionals, benefits professionals, human resource professionals, accounting professionals, labor attorneys, financial analysts, and finance professionals. Readers will have roles in benefits-related consulting, finance, accounting, and human resource management, both domestic and international.

The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.

The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis.

Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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