She's been compared to a beacon shining through the fog. Her thorough research, meticulous analyses, and extraordinarily accurate forecasts have won her the respect and admiration of colleagues up and down the Street. A protégée of the master technical analyst Alan Shaw, she is currently Senior Technical Analyst, Vice President for Research at Salomon Smith Barney. But what some insiders remember most about Louise Yamada is that in 1994 she was among the very first to predict the greatest bull market of the twentieth century.

In Market Magic, Louise Yamada shares her formidable skills to look beyond the daily noise of trading and help guide your investments through the perils and uncertainties of the next ten years. At a time when classical forecasting techniques seem to be failing us and even the professionals are at a loss as to which way the markets will go, Yamada marshals her experience and talent to offer on-target analyses of today's macro forces and specific trend forecasts for the next decade.

Reading this book, you will understand why her weekly reports on various markets are so eagerly awaited by investors everywhere. Yamada describes what she saw in 1994 that led her to argue for an extended bull market. In addition, she describes her "two-tier market thesis" and explains why U.S. equities with global exposure have outperformed domestically focused stocks and why this trend should continue into the future.

Yamada reveals how macro changes in U.S. demographics have subtly altered the business and investment landscapes, and how these demographic shifts are impacting the stock market in ways that have been largely unnoticed. Her case for an extension of this bull market into the next century is must reading for all serious (and nervous) investors. Firm in her belief that new technology will continue to drive the economy, Yamada identifies the industries and business sectors she believes will thrive under its expanding influence.

Market Magic offers a fresh perspective on the new and emerging realities. Forging links between the forces that will be at work in the future, Louise Yamada reveals a thought-provoking scenario for the market's next ten years, and details how investors can track its course through technical analysis. Market Magic is an enlightening analysis of the big picture from one of the best minds in the investment community.

"Few on Wall Street can match Louise Yamada for analytical ability as well as insight on the big issues affecting investors. We are fortunate she is willing to share the results of her thoughts and research with us." —Mark Haines, CNBC.

"Louise Yamada has a special talent for anticipating future financial trends. Market Magic is a must read for investors as we prepare for the exciting decade ahead." —David Cork, F.C.S.I. author of The Pig and the Python: How to Prosper from the Aging Baby Boom.

"Market Magic demystifies the voodoo of technical analysis and relates technical indicators to the real world of stocks and bonds and demographic and economic trends worldwide." —Oscar S. Schafer, General Partner Cumberland Associates; Member, Barron's Roundtable.

The material contained in this book originated in interrogations about modern practice in time series analysis. * Why do we use models optimized with respect to one-step ahead foreca- ing performances for applications involving multi-step ahead forecasts? * Why do we infer 'long-term' properties (unit-roots) of an unknown process from statistics essentially based on short-term one-step ahead forecasting performances of particular time series models? * Are we able to detect turning-points of trend components earlier than with traditional signal extraction procedures? The link between 'signal extraction' and the first two questions above is not immediate at first sight. Signal extraction problems are often solved by su- ably designed symmetric filters. Towards the boundaries (t = 1 or t = N) of a time series a particular symmetric filter must be approximated by asymm- ric filters. The time series literature proposes an intuitively straightforward solution for solving this problem: * Stretch the observed time series by forecasts generated by a model. * Apply the symmetric filter to the extended time series. This approach is called 'model-based'. Obviously, the forecast-horizon grows with the length of the symmetric filter. Model-identification and estimation of unknown parameters are then related to the above first two questions. One may further ask, if this approximation problem and the way it is solved by model-based approaches are important topics for practical purposes? Consider some 'prominent' estimation problems: * The determination of the seasonally adjusted actual unemployment rate.

This book, and its companion volume, present a collection of papers by Clive W.J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

This book concentrates on the five biggest recessions in the twentieth century. It focuses on the UK, but makes numerous comparisons to recessions in other countries. Two major recessions are identified in the interwar period; three more in the years 1973-1995. The main conclusion reached is that major recessions reflect abrupt fallings off in demand not supply, and can be explained by identifiable demand shocks. The concluding chapter offers advice on how to avoid future severe recessions: a combination of prudent policy-making beforehand and special measures in the downturn and recovery.

In 1950 the world population was 2.5 billion; fifty years later there are over 6 billion people. The demographic of this explosion has essentially occurred in the developing areas of the world. The key to understanding many contemporary development problems that have arisen from this rapid growth is in understanding the relationships between population and the economy.
This book offers an analysis of such relationships, encompassing a review of the major positions in the academic debate.
Population, Economic Growth and Agriculture in Less Developed Countries will serve as a useful introduction and reference tool for students, academics and all with an interest in the population debate and economics.

eBook available with sample pages: 0203103106

'An urgent read ... Karl Popper for the 21st century' Robert Phillips, former CEO, Edelman EMEA and author of Trust me, PR is Dead 'Heffernan is ... a deft storyteller. Uncharted is ... wise and appealingly human' Tim Harford, Financial Times How can we think about the future? What do we need to do - and who do we need to be? In her bold and invigorating new book, distinguished businesswoman and author Margaret Heffernan explores the people and organisations who aren't daunted by uncertainty. We are addicted to prediction, desperate for certainty about the future. But the complexity of modern life won't provide that; experts in forecasting are reluctant to look more than 400 days out. History doesn't repeat itself and even genetics won't tell you everything you want to know. Ineradicable uncertainty is now a fact of life. In complex environments, efficiency is a hazard not a help; being robust is the better, safer option. Drawing on a wide array of people and places, Margaret Heffernan looks at long-term projects developed over generations that could never have been planned the way that they have been run. Experiments, led by individuals and nations, discover new possibilities and options. Radical exercises in forging new futures with wildly diverse participants allow everyone to create outcomes together that none could do alone. Existential crises reveal the vital social component in resilience. Death is certain, but how we approach it impacts the future of those we leave behind. And preparedness - doing everything today that you might need for tomorrow - provides the antidote to passivity and prediction. Ranging freely through history and from business to science, government to friendships, this refreshing book challenges us to resist the false promises of technology and efficiency and instead to mine our own creativity and humanity for the capacity to create the futures we want and can believe in.

We live in a world where capital is free to move. Increasingly this determines the pattern of international growth. Savings are invested in the country yielding the highest return, thus adding to its stock of capital. This development is espe- cially true of common markets such as the European Union, which are based on free trade and financial openness. The present monograph deals with internatio- nal growth, featuring the dynamics of foreign debt and domestic capital. I had many helpful talks with my colleagues at Hamburg: Michael Schmid (now at Bamberg), Franco Reither, Wolf Schlifer, Thomas Straubhaar and Johannes Hackmann. In addition, Michael Brauninger and Philipp Lichtenauer carefully discussed with me all parts of the manuscript. Last but not least, Doris Ehrich did the secretarial work as excellently as ever. I wish to thank all of them. Contents INTRODUCTION 3 BRIEF SURVEY OF THE LITERATURE 9 SMALL OPEN ECONOMY 15 CHAPTER I. 1. Solow Model 15 1.1. Foreign Assets 15 1.1.1. Steady State 15 Process of Adjustment 25 1.1.2.

UPDATED FOR 2020 WITH A NEW PREFACE BY NATE SILVER "One of the more momentous books of the decade." -The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger-all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver's insights are an essential read.

In the completely revised second edition of this highly praised book, Susumu Yabuki, one of Japan's leading China experts, and Stephen M. Harner, A Shanghai-based investment banker, present a comprehensive and accessible analysis of China's political economy.The authors provide dozens of easy-to-grasp and up-to-date graphs, charts, tables, and maps to illustrate the reality of China, as they explain and comment on political, economic, financial and trade trends.Placing issues in historical perspective, and with a view to trends into the twenty-first century, the authors survey the realities of China's area and population, agriculture, energy needs, pollution, industrial structure, township and village enterprises, state-owned enterprise reform, unemployment, economic regions, foreign investment, state finances, fiscal and monetary policy, China's financial institutions, foreign financial institutions in China, stock markets, international finance, balance of payments and exchange rate policy, corporate finance, the role of Shanghai, government institution reform, foreign trade, the roles of Hong Kong and Taiwan, U.S.-China relations, and Japan-China relations.Useful as an introduction to China's economics, finance, and politics for students, and as a desktop reference volume for corporations, organizations, and individuals considering doing business in China, this unique study fills a genuine gap in the literature.

A critical examination of The prevailing orthodoxy according to which all macroeconomic theory should be reducible to microeconomics. The book provides a mathematical extension of the Lucas theory to allow for the effects of creation of knowledge upon economic development so as to improve the prediction of business cycle data.

1. Introduction and overview Until still few years ago, economic growth theory (going back to Solow, 1956; for an introduction cf. Burmeister and Dobell, 1970) predicted convergence of both growth rates and level of per capita income of economies which share identical preferences, technologies and same population growth rates, independently of initial conditions. Countries with a low capital stock grow faster than those with a higher capital stock, until, in the long-run, they all converge to a common constant growth rate. This prediction is due to the way how growth is "explained" in models of this kind. Growth of output per capita resulted, in the simplest model, from an exogenous growth oflabour productivity (see e. g. Sala-i-Martin, 1990; Grossman and Helpman, 1991a, ch. 2). Si 1ce this increase of productivity is exogenously given, the model itselfdoes not give any explanation ofits source. The prediction ofconvergence ofgrowth rates, itself, is very doubtful and observations show, that on an international level either convergence is not given at all, or that it takes a very long time. The literature of the "new" theory of growth provides a rich variety of models whose theoretical implications range from divergence to convergence and thus offers much better working tools in order to analyze real world observations. These models (starting with Romer, 1986 and Lucas, 1988) explain growth of GNP or per capita income from within the model by includingexternal effects such as a public stock ofknowledge capital (e. g.

Smart Economic Decision-Making in a Complex World is a fresh and reality-based perspective on decision-making with significant implications for analysis, self-understanding and policy. The book examines the conditions under which smart people generate outcomes that improve their place of work, their household and society. Within this work, the curious reader will find interesting open questions on many fascinating areas of current economic debate, including, the role of realistic assumptions robust model building, understanding how and when non-neoclassical behavior is best practice, why the assumption of smart decision-makers is best to understand and explain our economies and societies, and under what conditions individuals can make the best possible choices for themselves and society at large. Additional sections cover when and how efficiency is achieved, why inefficiencies can persist, when and how consumer welfare is maximized, and what benchmarks should be used to determine efficiency and rationality.

A discussion of various aspects of dynamic behavior of empirical macroeconomic, and in particular, macroeconometric models, is presented in this book. The book addresses in depth several theoretical and practical aspects concerning the modeling and analysis of long-run equilibrium behavior, adjustment dynamics and stability. Tools are developed to identify and interpret the main determinants of the dynamics of models. The tools involve, among others, error-correction mechanisms, eigenvalue analysis, feedback closure rules, graph theory, learning behavior, steady-state analysis, and stochastic simulation. Their usefulness is demonstrated by interesting applications to a number of well-known national and multi-national models.

This book offers a wide-ranging overview of the state of labour market forecasting in selected OECD countries. Besides presenting forecasting models, the contributions provide an introduction to past experiences of forecasting, highlight the requirements for building appropriate data sets and present the most up-to-date forecasts available. In most cases the forecasts project mismatches in the labour market as they are likely to occur in the coming years with respect to occupational groups, qualifications and employment in specific sectors. The authors demonstrate how these insights might be used to help reduce employment risks both for the individual worker and the national labour market as a whole. The country examples also show how information on labour market trends is disseminated and used by various actors, such as policymakers, firms and individuals. In a world of rapid structural change, the results of the research presented in this book could help cushion the impact of potential shocks from future mismatches and skill shortages in the job market. Policymakers at the supranational, national and regional level, and academics in the fields of labour market theory and policy can all draw valuable information from this insightful study.

Many models in this volume can be used in solving portfolio problems, in assessing forecasts, in understanding the possible effects of shocks and disturbances.

Global construction data is vital for contractors, governments, international organisations, policy makers, academic researchers and statisticians. As the global population of the world expands, the sustainability of the built environment raises the political agenda and the need to manage infrastructure and buildings in both urban and rural contexts becomes ever more pressing. How much more can the built environment grow and how can it be managed sustainably? This edited volume addresses how we can find a possible way through the inconsistencies between national construction data sets to devise a consistent approach to national construction data to further the global sustainability agenda and inform policy making. This search begins in Part I, which looks at the methods and definitions used in construction statistics in different countries. Part II considers examples of different types of construction data from the cost of materials, measuring work on high rise buildings and existing stock. In Part III, the authors consider construction data internationally, beginning with the problem of comparing data in different countries using exchange rates and purchasing power parities (PPPs), comparing innovation processes in different countries and looking at the provision of building design internationally. In Part IV, the international theme is continued by comparing accounting practices and company performance in different countries and concludes with an international comparison of construction industries. This book raises awareness of the significance of the construction industry globally and the importance of data to measure it. It informs the discussion of the best ways of handling the consequences of policies affecting the built environment and the effect of the built environment on the rest of the economy and society. It is essential reading for international economists, construction industry consultants, policy makers, construction statisticians and academics.

Business leaders know that accurate forecasting is a critical organizational capability. Forecasting is predicting the future, and the list of what needs to be predicted to run a world-class organization and its supply chain is virtually endless. Forecasting goes well beyond simply predicting demand or sales. Accurate forecasts are essential for identifying new market opportunities, forecasting risks, events, supply chain disruptions, innovation, competition, market growth and trends. It also includes the ability to conduct a what-ifa analysis to understand the tradeoff implications of decisions. Over the past few years the ability to make accurate and useful forecasts has become particularly challenging due to a spike in the competitiveness of global markets coupled with a global economic recession. Customers are demanding increasingly shorter response times, improved quality, and greater product choice. Increased competition is exacerbated by a downward global economy and rising fuel prices, which increase uncertainty, risk, and operating costs. The result has been a sharp rise in the complexity of what needs to be forecasted. In an era of rapid change, historical data that are typically used to make forecasts can be of limited value. At the same time information technology has enabled forecasts to drive entire supply chains and enterprise resources planning systems. However, more technology and software, without an understanding of how they can most effectively be utilized, are not the answer to improving forecast accuracy

Every plan needs a forecast - a reasonable prediction of the future. No business plan can be implemented without one. But the academic literature on forecasting is vast and spans disciplines such as statistics, economics, operations management and informed judgment and decision making. Recommendations from this literature have been implemented in a vast array of commercial software, and almost all modern companies have access to some decision support models that provide demand forecasts. In the long run, the demand forecast shapes decisions to build or close down plants, add or remove products from a portfolio, and bolster or challenge investor confidence in the stock price. In the short run forecasting software greatly aids managers in making functional decisions (how much are we going to sell next month, next year, or 5 years from now?) but without a proper understanding of the basics of forecasting, such software appears as a black-box, and the output from this software garners little trust within an organization. The intention of this book is to underscore the importance of demand forecasting and to demonstrate what an executive should know about it. It discusses the value of forecasting, presents both basic and advanced forecasting models, introduces the subject of time series and the technique of exponential smoothing (critical for accurate forecasts), examines the role that human judgment plays in interpreting the numbers and identifying forecasting errors. Finally, the book offers an organizational context by creating a rational framework that shows how forecasting is an integral part of business planning and demonstrates how to use forecasts within an organization.

Whether it's an unforeseen financial crash, a shock election result or a washout summer that threatens to ruin a holiday in the sun, forecasts are part and parcel of our everyday lives. We rely wholeheartedly on them, and become outraged when things don't go exactly to plan. But should we really put so much trust in predictions? Perhaps gut instincts can trump years of methodically compiled expert knowledge? And when exactly is a forecast not a forecast? Forewarned will answer all of these intriguing questions, and many more. Packed with fun anecdotes and startling facts, Forewarned is a myth-busting guide to prediction, based on the very latest scientific research. It lays out the many ways forecasting can help us make better decisions in an unpredictable modern world, and reveals when forecasts can be a reliable guide to the uncertainties of the future - and when they are best ignored.

The field of behavioural economics can tell us a great deal about cognitive bias and unconscious decision-making, challenging the orthodox economic model whereby consumers make rational and informed choices. But it is in the arena of health that it perhaps offers individuals and governments the most value. In this important new book, the most pernicious health issues we face today are examined through a behavioral economic lens. It provides an essential and timely overview of how this growing field of study can reframe and offer solutions to some of the biggest health issues of our age. The book opens with an overview of the core theoretical concepts, after which each chapter assesses how behavioral economic research and practice can inform public policy across a range of health issues. Including chapters on tobacco, alcohol and drug use, physical activity, dietary intake, cancer screening and sexual health, the book integrates the key insights from the field to both developed and developing nations. Also asking important ethical questions around paternalism and informed choice, this book will be essential reading for students and researchers across psychology, economics and business and management, as well as public health professionals wishing for a concise overview of the role behavioral economics can potentially play in allowing people to live healthier lives.

Alexa is Stealing Your Job is a guided tour of where the world has been with artificial intelligence and how it affects the future of work. Artificial intelligence is taking over. Ask Alexa to call a client or confirm your schedule for the day and she does just that immediately. Ask her a question, give her a command, or just share a joke together, and she becomes your new best employee. A conversation with Alexa can nix the need for millions of front-line workers. Today's companies must keep up with artificial intelligence to keep their customers, and today's employees must find new ways to provide value to their companies if they want to keep their job. Author and speaker Rhonda Scharf shows readers how a willingness to adapt to the new normal keeps both businesses and their employees relevant in these changing times. Alexa Is Stealing Your Job reveals what the future entails by diving into the world of AI and exploring how it impacts lives, careers, and the future.

Vince Cable's bestselling book, The Storm, explored and explained the causes of the 2008 world economic crisis and how Britain should respond to the great challenges it brought. In After the Storm, Cable, who was Business Secretary in the 2010-2015 Coalition Government, provides a unique perspective on the state of the global financial markets and how the British economy has fared since 2008. Providing a previously unreported inside view of the Coalition, After the Storm offers a carefully considered perspective on how the British economy should be managed over the next decade and beyond. This timely book is a fascinating and urgent intervention from one of the key figures in British politics of the past two decades.

This book is written for business persons who wish to forecast consumer demands and sales. It is also designed for economic, financial, governmental, and private practitioners, who make decisions based on costs, benefits, economic trends, and growth. Finally, the book is written for first year MBA students, undergraduate students, and other readers who wish to acquire a fundamental knowledge of forecasting for the purpose of pursuing jobs in the fields of economics and finance, or for making personal plans for the future. The book emphasizes applied aspects of forecasting rather than theoretical aspects, so a working knowledge of high-school statistics and college algebra is all the mathematics that is needed. Because regression-based forecasts do not appear until the last three chapters of the book, knowledge of econometrics or time-series modeling is not a prerequisite. Additionally, although each chapter is designed to be self-explanatory, a basic understanding of econometrics might be helpful, but not necessary. The book discusses most of the forecasting methods frequently used in business and economics, such as time-series, demand and sales, investment, short-term planning, long-term growth, and regressions. To keep the book concise, only a brief introduction to the ARIMA process is introduced. Readers who wish to gain in-depth knowledge of ARIMA models are encouraged to read a book written specifically for time-series modeling.

This book allows those with a basic knowledge of econometrics to learn the main nonparametric and semiparametric techniques used in econometric modelling, and how to apply them correctly. It looks at kernel density estimation, kernel regression, splines, wavelets, and mixture models, and provides useful empirical examples throughout. Using empirical application, several economic topics are addressed, including income distribution, wage equation, economic convergence, the Phillips curve, interest rate dynamics, returns volatility, and housing prices. A helpful appendix also explains how to implement the methods using R. This useful book will appeal to practitioners and researchers who need an accessible introduction to nonparametric and semiparametric econometrics. The practical approach provides an overview of the main techniques without including too much focus on mathematical formulas. It also serves as an accompanying textbook for a basic course, typically at undergraduate or graduate level.

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting-from the basics all the way to leading-edge models-will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.