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Macroeconomic Forecasting Using Alternative Data: Techniques for Applying Big Data and Machine Learning applies computer science to the demands of macroeconomic forecasting. It is the first book to combine machine learning methods with macroeconomics. By using artificial intelligence and machine learning techniques, it unlocks the increased forecasting accuracy offered by alternative data sources. Through its interdisciplinary approach, readers learn how to use big datasets efficiently and effectively. Combines big data/machine learning with macroeconomic forecasting Explains how alternative data improves forecasting accuracy when controlled for traditional data sources Provides new innovative methods for handling large databases and improving forecasting accuracy

Handbook of U.S. Consumer Economics presents a deep understanding on key, current topics and a primer on the landscape of contemporary research on the U.S. consumer. This volume reveals new insights into household decision-making on consumption and saving, borrowing and investing, portfolio allocation, demand of professional advice, and retirement choices. Nearly 70% of U.S. gross domestic product is devoted to consumption, making an understanding of the consumer a first order issue in macroeconomics. After all, understanding how households played an important role in the boom and bust cycle that led to the financial crisis and recent great recession is a key metric. Introduces household finance by examining consumption and borrowing choices Tackles macro-problems by observing new, original micro-data Looks into the future of consumer spending by using data, not questionnaires

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

Policymakers and business practitioners are eager to gain access to reliable information on the state of the economy for timely decision making. More so now than ever. Traditional economic indicators have been criticized for delayed reporting, out-of-date methodology, and neglecting some aspects of the economy. Recent advances in economic theory, econometrics, and information technology have fueled research in building broader, more accurate, and higher-frequency economic indicators. This volume contains contributions from a group of prominent economists who address alternative economic indicators, including indicators in the financial market, indicators for business cycles, and indicators of economic uncertainty.

A Companion to Economic Forecasting provides an accessible and comprehensive account of recent developments in economic forecasting. Each of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and views. Uniquely surveying forecasting in a single volume, the Companion provides a comprehensive account of the leading approaches and modeling strategies that are routinely employed.

The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.

Forecasts guide decisions in all areas of economics and finance. Economic policy makers base their decisions on business cycle forecasts, investment decisions of firms are based on demand forecasts, and portfolio managers try to outperform the market based on financial market forecasts. Forecasts extract relevant information from the past and help to reduce the inherent uncertainty of the future. The topic of this special issue of the Journal of Economics and Statistics is the theory and practise of forecasting and forecast evaluation and an overview of the state of the art of forecasting.

How do financial markets respond to concerns over debt sustainability and the level of public debt in emerging markets? We introduce a measure of debt sustainability – the difference between the debt stabilizing primary balance and the primary balance–in an otherwise standard spread regression model applied to a panel of 26 emerging market economies. We find that debt sustainability is an important determinant of spreads. In addition, using a panel smooth transition regression model, we find that the sensitivity of spreads to debt sustainability doubles as public debt increases above 45 percent of GDP. These results suggest that market interest rates react more to debt sustainability concerns in a country with a high level of debt compared to a country with a low level of debt.

Drawing on interviews with the UK government's Panel of Independent Forecasters, the author shows how economic models, forecasts and policy analysis depend crucially upon the judgements of economists.

This title was first published in 2000: This text offers a comprehensive collection of selected papers from the 24th Centre for International Research on Economic Tendency Surveys (CIRET) conference. Areas selected include leading indicators and turning points, classifications of business cycles, survey data and policy decisions, attitudes and behaviour of firms, and economic forecasting. The text aims to be of interest to all those concerned with the use of business and consumer surveys in a global context.

Since banking systems play a crucial role in maintaining the overall health of the economy, the adverse effects of poorly supervised systems may be quite severe. Without some form of vigilant external oversight, banking systems could fall prey to excessive risk taking, moral hazard, and corruption. Prudential supervision provides that oversight, using government regulation and monitoring to ensure the soundness of the banking system and, by extension, the economy at large. The contributors to this thoughtful volume examine the current state of prudential supervision, focusing on fundamental issues and key pragmatic concerns. Why is prudential supervision so important? What kinds of excess must it guard against? What particular forms does it take? Which of these are the most effective deterrents against mismanagement and system overload in today's rapidly shifting financial climate? The contributors foresee a continued movement beyond simple regulatory rules in banking and toward a more active evaluation and supervision of a bank's risk management practices.

Indexes the tables, graphs, and formatted data presented in the statistical publications of the EIA.

Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.

Bringing together a collection of previously published work, this book provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the Marshallian Macroeconomic Model that features demand, supply and entry equations for major sectors of economies is analysed and described. This volume will prove invaluable to professionals, academics and students alike.

The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics