An overview of the macroeconomic forecasting industry in the United States that explains and evaluates the forecasting techniques used to make predictions about various aspects of the national economy.

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

Economic Forecasting provides a comprehensive overview of macroeconomic forecasting. The focus is first on a wide range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting.

Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

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 Congressional Budget Office (CBO) regularly evaluates the accuracy of its economic forecasts by comparing them with the economy¿s actual performance and with others¿ forecasts. Such evaluations help guide CBO¿s efforts to improve the quality of its forecasts and are also intended to assist Members of Congress in their use of the agency¿s estimates. Contents of this report: (1) Choice of Forecasts for the Evaluation; (2) Measuring the Quality of Forecasts: Statistical Bias; Accuracy; Alternative Measures of Forecast Quality; (3) Limitations of Forecast Evaluations: (4) The Effects of Bus. Cycles, Changes in the Trend Rate of Productivity Growth, and Oil Price Shocks; (5) Forecasting Record: 2-Year Forecasts; 5-Year Projections; (6) Historical and Forecast Data.

Before future-oriented information can be used as a basis for decision making in economics or business administration, it must be understood on a methodological level. This book provides decision makers with a thorough understanding of the possibilities offered by various forecasting methods as well as their limitations. If managers rely on a forecast with a long-term perspective to guide them in making short-term decisions, planning deficiencies will likely result. Likewise, if managers use short-term forecasts to inform their long-term strategic vision, failure could easily ensue. Graf provides the tools necessary to sidestep the common pitfall of using the wrong forecasting technique for the wrong purpose. This is not a detailed examination of the mathematical and statistical tools of empirical economic research. Instead, forecasting methods are explained so that they can be understood by the managers who employ them in their decision making. Graf demonstrates that understanding and—in special cases—cooperation between forecast developers and users is crucial to creating an effective forecast that results in informed management decisions. He discusses traditional, long-term, macroeconomic, and global economic forecasting; the scenario technique as a central instrument of long-term forecasting; and short-term economic and market forecasting.

The book's contributors assess the performance of economic forecasting methods, argue that data can be better exploited through model and forecast combination, and advocate for models that are adaptive and perform well in the presence of nonlinearity and structural change.

Economic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable. Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online at authors' website.

A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike