This paper asks whether the slow recovery of the US economy from the trough of the Great Recession was anticipated, and identifies some of the factors that contributed to surprises in the course of the recovery. It constructs a narrative using news reports and government announcements to identify policy and financial shocks. It then compares forecasts and forecast revisions of GDP to the narrative. Successive financial and fiscal shocks emanating from Europe, together with self-inflicted wounds from the political stalemate over the US fiscal situation, help explain the slowing of the pace of an already slow recovery.

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track by a specific form of intercept correction. Among all these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and following recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment and very persistent negative effects on trend growth. Similar findings emerge also for the other G7 countries.

Reality-based modeling for today's unique economic recovery Economic Modeling in the Post Great Recession Era presents a more realistic approach to modeling, using direct statistical applications to address the characteristics and trends central to current market behaviors. This book's unique focus on the reality of today's markets makes it an invaluable resource for students and practitioners seeking a comprehensive guide to more accurate forecasting. While most books treat the economy as if it were in a vacuum, building models around idealized or perception-biased behaviors, this book deals with the economy as it currently stands—in a state of recovery, limited by financial constraints, imperfect information, and lags and disparities in price movements. The authors identify how these characteristics impact various markets' behaviors, and quantify those behaviors using SAS as the primary statistical tool. Today's economy bears a number of unique attributes that usual modeling methods fail to consider. This book describes how to approach modeling based on real-world, observable data in order to make better-informed decisions in today's markets. Discover the three economic characteristics with the greatest impact on various markets Create economic models that mirror the current post-recession reality Adopt statistical methods that identify and adapt to structural breaks and lags Factor real-world imperfections into modeling for more accurate forecasting The past few years have shown a clear demarcation between policymakers' forecasts and actual outcomes. As the dust settles on the Great Recession, after-effects linger—and impact our current recovery in ways that diverge from past experience and theoretical expectations. Economic Modeling in the Post Great Recession Era provides comprehensive guidance grounded in reality for today's economic decision-makers.

We describe the evolution of forecasts in the run-up to recessions. The GDP forecasts cover 63 countries for the years 1992 to 2014. The main finding is that, while forecasters are generally aware that recession years will be different from other years, they miss the magnitude of the recession by a wide margin until the year is almost over. Forecasts during non-recession years are revised slowly; in recession years, the pace of revision picks up but not sufficiently to avoid large forecast errors. Our second finding is that forecasts of the private sector and the official sector are virtually identical; thus, both are equally good at missing recessions. Strong booms are also missed, providing suggestive evidence for Nordhaus’ (1987) view that behavioral factors—the reluctance to absorb either good or bad news—play a role in the evolution of forecasts.

This article shows how the recovery of inflation in 2009-10 occurred precisely at the only time (since 1985) the models would predict disinflation, i.e., inflation went up when the models said it should go down.

The book provides one of the most detailed and comprehensive reviews of the growth experience of a group of low and middle income countries before and during the global crisis. It then explores their growth prospects after the recovery and how they may be shaped by the new global economic environment.