This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to 2011:Q2, based on an in-sample estimates for 1963:Q1 to 1982:Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) FA, SSVS, and BSS predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex-post out-of-sample forecast performance of the 26 models using the relative average Mean Square Error for one-, two-, four-, and eight-quarters-ahead forecasts and test their significance based on the McCracken (2004, 2007) MSE-F statistic. We find that, on average, the SSVS-Large model provides the best forecasts amongst all the models. We also find that one of the individual regression models, using house for sale (H4SALE) as a predictor, performs best at the four- and eight-quarters-ahead horizons. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex-ante forecast exercise from 2011:Q3 to 2012:Q4. The SSVS-Large model forecasts the turning points more accurately, although the H4SALE model does better toward the end of the sample. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.

Models of the American economy exist in government, research institutes, universities, and private corporations. Given the proliferation, it is wise to take stock because these models come from diverse sources and describe different conditions from alternative points of view. They could be saying different things about the economy. The high-level comparative studies in this volume, gathered from several issues of the International Economic Review, with a substantive introduction and the addition of more comparative material, evaluate the performance of eleven models of the American economy: the Wharton Mark Ill Model; Brookings Model; Hickman-Coen Annual Model; Liu-Hwa Monthly Model; Data Resources, Inc. (DRI) Model; Federal Reserve Bank of St. Louis Model; Michigan Quarterly Econometric (MOEM) Model; Wharton Annual and Industry Model; Anticipation Version of the Wharton Mark Ill Model/Fair Model; U.S. Department of Commerce (BEA) Model. Each of the proprietors or builders of these models describes his own system in his own words. These studies come closer than ever before to standardizing model operations for testing purposes. Some of the models are monthly, while others are annual. but the quarterly unit of time is the most frequent. Some are demand oriented, others are supply oriented, and focus on the input-output sectors of the economy. Some use only observed. objective data; others use subjective. anticipatory data. Both large and small models are included. In spite of the diversity, the contributors have cooperated to trace the differences between their models to root causes and to report jointly the results of their research. There are also some general papers that look at model performance from outside the CEME group.

The basic tools for analyzing macroeconomic fluctuations and policies, applied to concrete issues and presented within an integrated New Keynesian framework. This textbook presents the basic tools for analyzing macroeconomic fluctuations and policies and applies them to contemporary issues. It employs a unified New Keynesian framework for understanding business cycles, major crises, and macroeconomic policies, introducing students to the approach most often used in academic macroeconomic analysis and by central banks and international institutions. The book addresses such topics as how recessions and crises spread; what instruments central banks and governments have to stimulate activity when private demand is weak; and what “unconventional” macroeconomic policies might work when conventional monetary policy loses its effectiveness (as has happened in many countries in the aftermath of the Great Recession.). The text introduces the foundations of modern business cycle theory through the notions of aggregate demand and aggregate supply, and then applies the theory to the study of regular business-cycle fluctuations in output, inflation, and employment. It considers conventional monetary and fiscal policies aimed at stabilizing the business cycle, and examines unconventional macroeconomic policies, including forward guidance and quantitative easing, in situations of “liquidity trap”—deep crises in which conventional policies are either ineffective or have very different effects than in normal time. This book is the first to use the New Keynesian framework at the advanced undergraduate level, connecting undergraduate learning not only with the more advanced tools taught at the graduate level but also with the large body of policy-oriented research in academic journals. End-of-chapter problems help students master the materials presented.

Short-term Forecasting for Empirical Economists seeks to close the gap between research and applied short-term forecasting. The authors review some of the key theoretical results and empirical findings in the recent literature on short-term forecasting, and translate these findings into economically meaningful techniques to facilitate their widespread application to compute short-term forecasts in economics, and to monitor the ongoing business cycle developments in real time.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.