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Working Paper
Assessing Macroeconomic Tail Risk
Matthes, Christian; Zhang, Donghai; Loria, Francesca (2019-04-19)
What drives macroeconomic tail risk? To answer this question, we borrow a definition of macroeconomic risk from Adrian et al. (2019) by studying (left-tail) percentiles of the forecast distribution of GDP growth. We use local projections (Jord, 2005) to assess how this measure of risk moves in response to economic shocks to the level of technology, monetary policy, and financial conditions. Furthermore, by studying various percentiles jointly, we study how the overall economic outlook?as characterized by the entire forecast distribution of GDP growth?shifts in response to shocks. We find that ...
Working Paper , Paper 19-10

Working Paper
Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting
Chang, Andrew C.; Levinson, Trace J. (2020-10-23)
We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's ...
Finance and Economics Discussion Series , Paper 2020-090

Journal Article
Machine Learning Approaches to Macroeconomic Forecasting
Smalter Hall, Aaron (2018-10)
Forecasting macroeconomic conditions can be challenging, requiring forecasters to make many discretionary choices about the data and methods they use. Although forecasters underpin the choices they make about models and complexity with economic intuition and judgement, these assumptions can be flawed. {{p}} Machine learning approaches, on the other hand, automate as many of those choices as possible in a manner that is not subject to the discretion of the forecaster. Aaron Smalter Hall applies machine learning techniques to find an optimal forecasting model for the unemployment rate. His ...
Economic Review , Issue Q IV , Pages 63-81

Report
800,000 Years of Climate Risk
Adrian, Tobias; Boyarchenko, Nina; Giannone, Domenico; Prasad, Ananthakrishnan; Seneviratne, Dulani; Xiao, Yanzhe (2022-09-01)
We use a long history of global temperature and atmospheric carbon dioxide (CO2) concentration to estimate the conditional joint evolution of temperature and CO2 at a millennial frequency. We document three basic facts. First, the temperature–CO2 dynamics are non-linear, so that large deviations in either temperature or CO2 concentrations take a long time to correct–on the scale of multiple millennia. Second, the joint dynamics of temperature and CO2 concentrations exhibit multimodality around historical turning points in temperature and concentration cycles, so that prior to the start of ...
Staff Reports , Paper 1031

Working Paper
Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model
Hajdini, Ina (2022-02-16)
The paper considers a New Keynesian framework in which agents form expectations based on a combination of mis-specified forecasts and myopia. The proposed expectations formation process is found to be consistent with all three empirical facts on consensus inflation forecasts, namely, that forecasters under-react to ex-ante forecast revisions, that forecasters over-react to recent events, and that the response of forecast errors to a shock initially under-shoots but then over-shoots. The paper then derives the general equilibrium solution consistent with the proposed expectations formation ...
Working Papers , Paper 22-03

Working Paper
Regular Variation of Popular GARCH Processes Allowing for Distributional Asymmetry
Prono, Todd (2017-09-22)
Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails are Pareto like, under conditions that allow the innovations from the, respective, processes to be skewed. Skewness is considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in establishing the asymptotic properties of certain GARCH estimators proposed in the literature.
Finance and Economics Discussion Series , Paper 2017-095

Working Paper
A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression
Anderson, Gary S.; Audzeyeva, Alena (2019-10-17)
We propose a coherent framework using support vector regression (SRV) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for models with serially correlated economic variables, to produce robust sets of tuning parameters for SRV kernel functions. In contrast to previous approaches identifying a single "best" tuning parameter setting, a task that is pragmatically improbable to achieve in many applications, we proceed with ...
Finance and Economics Discussion Series , Paper 2019-074

Working Paper
Better the Devil You Know: Improved Forecasts from Imperfect Models
Oh, Dong Hwan; Patton, Andrew J. (2021-11-05)
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- specied model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecication of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signicant fore- cast improvements from ...
Finance and Economics Discussion Series , Paper 2021-071

Working Paper
Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims
Seo, Boyoung; Butters, R. Andrew; Aaronson, Daniel; Brave, Scott A.; Sacks, Daniel (2020-04-16)
We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty ...
Working Paper Series , Paper WP-2020-10

Working Paper
Is China Fudging Its GDP Figures? Evidence from Trading Partner Data
Spiegel, Mark M.; Fernald, John G.; Hsu, Eric (2019-09-04)
We propose using imports, measured as reported exports of trading partners, as an alternative benchmark to gauge the accuracy of alternative Chinese indicators (including GDP) of fluctuations in economic activity. Externally-reported imports are likely to be relatively well measured, as well as free from domestic manipulation. Using principal components, we derive activity indices from a wide range of indicators and examine their fit to (trading-partner reported) imports. We choose a preferred index of eight non-GDP indicators (which we call the China Cyclical Activity Tracker, or C-CAT). ...
Working Paper Series , Paper 2019-19

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