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Working Paper
Online Estimation of DSGE Models
Herbst, Edward; Schorfheide, Frank; Cai, Michael; Matlin, Ethan; Del Negro, Marco; Sarfati, Reca (2020-02-28)
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation (that is, re-estimating a model asnew data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity ofthe predictive performance to ...
Finance and Economics Discussion Series , Paper 2020-023

Working Paper
Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions
Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano (2020-01-16)
A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks. In this paper we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification featuring a common volatility factor that is a function of past financial conditions. Even though the conditional ...
Working Papers , Paper 20-02

Working Paper
Inflation as a global phenomenon - some implications for policy analysis and forecasting
Kabukcuoglu, Ayse; Martinez-Garcia, Enrique (2016-01-01)
We evaluate the performance of inflation forecasts based on the open-economy Phillips curve by exploiting the spatial pattern of international propagation of inflation. We model these spatial linkages using global inflation and either domestic slack or oil price fluctuations, motivated by a novel interpretation of the forecasting implications of the workhorse openeconomy New Keynesian model (Martnez-Garca and Wynne (2010), Kabukcuoglu and Martnez-Garca (2014)). We find that incorporating spatial interactions yields significantly more accurate forecasts of local inflation in 14 advanced ...
Globalization Institute Working Papers , Paper 261

Working Paper
The Fed's Asymmetric Forecast Errors
Chang, Andrew C. (2018-04-16)
I show that the probability that the Board of Governors of the Federal Reserve System staff's forecasts (the "Greenbooks'") overpredicted quarterly real gross domestic product (GDP) growth depends on both the forecast horizon and also whether the forecasted quarter was above or below trend real GDP growth. For forecasted quarters that grew below trend, Greenbooks were much more likely to overpredict real GDP growth, with one-quarter ahead forecasts overpredicting real GDP growth more than 75% of the time, and this rate of overprediction was higher for further ahead forecasts. For forecasted ...
Finance and Economics Discussion Series , Paper 2018-026

Working Paper
Forecasting Low Frequency Macroeconomic Events with High Frequency Data
Galvão, Ana B.; Owyang, Michael T. (2020-09)
High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is compared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive ...
Working Papers , Paper 2020-028

Report
Revisiting useful approaches to data-rich macroeconomic forecasting
Kapetanios, George; Groen, Jan J. J. (2008)
This paper analyzes the properties of a number of data-rich methods that are widely used in macroeconomic forecasting, in particular principal components (PC) and Bayesian regressions, as well as a lesser-known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the covariance between a target variable and these common components is maximized. Existing studies have focused on modelling the target variable as a function of a finite set of unobserved common factors that ...
Staff Reports , Paper 327

Working Paper
Lessons for Forecasting Unemployment in the U.S.: Use Flow Rates, Mind the Trend
Meyer, Brent; Tasci, Murat (2015-02-13)
This paper evaluates the ability of autoregressive models, professional forecasters, and models that leverage unemployment flows to forecast the unemployment rate. We pay particular attention to flows-based approaches?the more reduced form approach of Barnichon and Nekarda (2012) and the more structural method in Tasci (2012)?to generalize whether data on unemployment flows is useful in forecasting the unemployment rate. We find that any approach that leverages unemployment inflow and outflow rates performs well in the near term. Over longer forecast horizons, Tasci (2012) appears to be a ...
Working Papers (Old Series) , Paper 1502

Report
Recent changes in the U.S. business cycle
Chauvet, Marcelle; Potter, Simon M. (2001)
The U.S. business cycle expansion that started in March 1991 is the longest on record. This paper uses statistical techniques to examine whether this expansion is a onetime unique event or whether its length is a result of a change in the stability of the U.S. economy. Bayesian methods are used to estimate a common factor model that allows for structural breaks in the dynamics of a wide range of macroeconomic variables. We find strong evidence that a reduction in volatility is common to the series examined. Further, the reduction in volatility implies that future expansions will be ...
Staff Reports , Paper 126

Discussion Paper
Exploring the use of anonymized consumer credit information to estimate economic conditions: an application of big data
Wilshusen, Stephanie M. (2015-11-06)
The emergence of high-frequency administrative data and other big data offers an opportunity for improvements to economic forecasting models. This paper considers the potential advantages and limitations of using information contained in anonymized consumer credit reports for improving estimates of current and future economic conditions for various geographic areas and demographic markets. Aggregate consumer credit information is found to be correlated with macroeconomic variables such as gross domestic product, retail sales, and employment and can serve as leading indicators such that lagged ...
Consumer Finance Institute discussion papers , Paper 15-5

Working Paper
Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting
Wu, Ping; McIntyre, Stuart; Koop, Gary; Poon, Aubrey; Koop, Gary; Mitchell, James (2023-05-08)
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, ...
Working Papers , Paper 23-09

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