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Working Paper
Financial variables and macroeconomic forecast errors
A large set of financial variables has only limited power to predict a latent factor common to the year-ahead forecast errors for real Gross Domestic Product (GDP) growth, the unemployment rate, and Consumer Price Index (CPI) inflation for three sets of professional forecasters: the Federal Reserve?s Greenbook, the Survey of Professional Forecasters (SPF), and the Blue Chip Consensus Forecasts. Even when a financial variable appears to be fairly robust across sample periods in explaining the latent factor, from an economic standpoint its contribution appears modest. Still, several financial variables retain economic significance over certain subsamples; when non-linear effects are accounted for, these variables have an improved ability to consistently predict the latent factor over the business cycle.
AUTHORS: Barnes, Michelle L.; Olivei, Giovanni P.
DATE: 2017-10-31

Working Paper
How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data
FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which thosemethods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression models. To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over the near horizon than beyond a year. This study then shows that having more data up to, but not beyond, a certain quantity enhances the predictive accuracy of the ML methods relativeto that of parametric models. The likely explanation is that there has been data or model drift over time, so that methods that fit more complex models with more data can in fact suffer greater out-of-sample misses. Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data need to be sufficiently informative as a whole to help consumers with little or no credit history. This study further explores whether the greater functional flexibility of ML methods yields unequal benefit to consumers with different attributes or who reside in locales with varying economic conditions. It finds that the ML methods produce more favorable ratings for different groups of consumers, although those already deemed less risky seem to benefit more on balance.
AUTHORS: Perkins, Charles B.; Wang, J. Christina
DATE: 2019-10-14

Working Paper
Forecasts from Reduced-form Models under the Zero-Lower-Bound Constraint
In this paper, I consider forecasting from a reduced-form VAR under the zero lower bound (ZLB) for the short-term nominal interest rate. I develop a method that a) computes the exact moments for the first n + 1 periods when n previous periods are tracked and b) approximates moments for the periods beyond n + 1 period using techniques for truncated normal distributions and approximations a la Kim (1994). I show that the algorithm produces satisfactory results for VAR systems with moderate to high persistence even when only one previous period is tracked. For very persistent VAR systems, however, tracking more periods is needed in order to obtain reliable approximations. I also show that the method is suitable for affine term-structure modeling, where the underlying state vector includes the short-term interest rate as in Taylor rules with inertia.
AUTHORS: Pasaogullari, Mehmet
DATE: 2015-08-05

Working Paper
Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting
Financial data often contain information that is helpful for macroeconomic forecasting, while multistep forecast accuracy also benefits by incorporating good nowcasts of macroeconomic variables. This paper considers the role of nowcasts of financial variables in making conditional forecasts of real and nominal macroeconomic variables using standard quarterly Bayesian vector autoregressions (BVARs). For nowcasting the quarterly value of a variety of financial variables, we document that the average of the available daily data and a daily random walk forecast to fill in the missing days in the quarter typically outperforms other nowcasting approaches. Using real-time data and out-of-sample forecasting exercises, we find that the inclusion of financial variable nowcasts by themselves generally improves forecast accuracy for macroeconomic variables relative to unconditional forecasts, although we document several exceptions in which current-quarter forecast accuracy worsens with the inclusion of the financial nowcasts. Incorporating financial nowcasts and nowcasts of macroeconomic variables generally improves the forecast accuracy for all the macroeconomic indicators of interest, beyond including the nowcasts of the macroeconomic variables alone. Conditional forecasts generated from quarterly BVARs augmented with nowcasts of key financial variables rival the forecast accuracy of mixed-frequency dynamic factor models (MF-DFMs) and mixed-data sampling (MIDAS) models that explicitly link the quarterly data and forecasts to high-frequency financial data.
AUTHORS: Zaman, Saeed; Knotek, Edward S.
DATE: 2017-03-17

Working Paper
Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter
Although the trajectory and path of future outcomes plays an important role in policy decisions, analyses of forecast accuracy typically focus on individual point forecasts. However, it is important to examine the path forecasts errors since they include the forecast dynamics. We use the link between path forecast evaluation methods and the joint predictive density to propose a test for differences in system path forecast accuracy. We also demonstrate how our test relates to and extends existing joint testing approaches. Simulations highlight both the advantages and disadvantages of path forecast accuracy tests in detecting a broad range of differences in forecast errors. We compare the Federal Reserve?s Greenbook point and path forecasts against four DSGE model forecasts. The results show that differences in forecast-error dynamics can play an important role in the assessment of forecast accuracy.
AUTHORS: Martinez, Andrew
DATE: 2017-11-02

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors
We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee?s Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
AUTHORS: McCracken, Michael W.; Clark, Todd E.; Mertens, Elmar
DATE: 2017-09-25

Working Paper
Lessons for Forecasting Unemployment in the U.S.: Use Flow Rates, Mind the Trend
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 useful framework, even though it was designed to be mainly a tool to uncover long-run labor market dynamics such as the ?natural? rate. Its usefulness is amplified at specific points in the business cycle when unemployment rate is away from the longer-run natural rate. Judgmental forecasts from professional economists tend to be the single best predictor of future unemployment rates. However, combining those guesses with flows based approaches yields significant gains in forecasting accuracy.
AUTHORS: Meyer, Brent; Tasci, Murat
DATE: 2015-02-13

Working Paper
Have Standard VARs Remained Stable since the Crisis?
Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier literature focused on whether there were sizable parameter changes in the early 1980s, in either the conditional mean or variance parameters, and in the subsequent period till the beginning of the new century. In this paper we conduct a similar analysis but focus on the effects of the recent crisis. Using a range of techniques, we provide substantial evidence against parameter stability. The evolution of the unemployment rate seems particularly different relative to its past behavior. We then discuss and evaluate alternative methods to handle parameter instability in a forecasting context. While none of the methods clearly emerges as best, some techniques turn out to be useful to improve the forecasting performance.
AUTHORS: Carriero, Andrea; Marcellino, Massimiliano; Clark, Todd E.; Aastveit, Knut Are
DATE: 2014-09-18

Working Paper
Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts
This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in density accuracy than does just tilting based on the nowcast means. Hence entropic tilting can offer?more so for persistent variables than not-persistent variables?some benefits for accurately estimating the uncertainty of multi-step forecasts that incorporate nowcast information.
AUTHORS: Clark, Todd E.; Krueger, Fabian; Ravazzolo, Francesco
DATE: 2015-01-07

Working Paper
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy
This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for inflation are substantial, statistically significant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points.
AUTHORS: Zaman, Saeed; Tallman, Ellis W.
DATE: 2018-06-22

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