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Working Paper
On Model Aggregation and Forecast Combination
Massoumi, Esfandiar; Gospodinov, Nikolay (2025-10-09)
Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the decision-making process. We argue that entropy-based aggregation is a convenient device to confront this uncertainty and summarize relevant information from a set of candidate models and forecasts. The proposed aggregation tends to robustify the decision-making process to various sources of risks and ...
FRB Atlanta Working Paper , Paper 2025-12

Working Paper
GDPNow: A Model for GDP "Nowcasting"
Higgins, Patrick C. (2014-07-01)
This paper documents GDPNow, a "nowcasting" model for gross domestic product (GDP) growth that synthesizes the "bridge equation" approach relating GDP subcomponents to monthly source data with the factor model approach used by Giannone, Reichlin, and Small (2008). The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the U.S. Bureau of Economic Analysis. Using current vintage data, out-of-sample GDPNow model forecasts are found to be more accurate than a number of statistical benchmarks since 2000. Using real-time ...
FRB Atlanta Working Paper , Paper 2014-7

Working Paper
Forecasting China's Economic Growth and Inflation
Zhong, Karen; Zha, Tao; Higgins, Patrick C. (2016-07-01)
Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample model-based forecasts of China's real gross domestic product (GDP) growth and consumer price index inflation. This paper fills this research gap by providing a replicable forecasting model that beats a host of other competing models when measured by root mean square errors, especially over long-run forecast horizons. The model is shown to be capable of predicting turning points and usable for policy analysis ...
FRB Atlanta Working Paper , Paper 2016-7

Working Paper
The role of commodity prices in forecasting U.S. core inflation
Gospodinov, Nikolay (2016-02-01)
This note documents a curious finding about the substantial forecast ability of a simple aggregator of three commodity futures prices for U.S. core inflation. The proposed aggregator reduces the out-of-sample root mean squared error for 12-month-ahead inflation forecasts of the benchmark AR(1) model by 28 percent (20 percent) for the PCE (CPI) measure of core inflation. To avoid obfuscation of the sources of forecast ability, the model is intentionally kept simple, although extensions for improving and increasing the robustness of the forecast procedure are also discussed.
FRB Atlanta Working Paper , Paper 2016-5

Working Paper
Foreign exchange predictability during the financial crisis: implications for carry trade profitability
Liu, Xiaochun; Gospodinov, Nikolay; Jamali, Ibrahim; Anatolyev, Stanislav (2015-08-01)
In this paper, we study the effectiveness of carry trade strategies during and after the financial crisis using a flexible approach to modeling currency returns. We decompose the currency returns into multiplicative sign and absolute return components, which exhibit much greater predictability than raw returns. We allow the two components to respond to currency-specific risk factors and use the joint conditional distribution of these components to obtain forecasts of future carry trade returns. Our results suggest that the decomposition model produces higher forecast and directional accuracy ...
FRB Atlanta Working Paper , Paper 2015-6

Working Paper
Lessons for forecasting unemployment in the United States: use flow rates, mind the trend
Meyer, Brent; Tasci, Murat (2015-02-01)
This paper evaluates the ability of autoregressive models, professional forecasters, and models that incorporate 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 are useful in forecasting the unemployment rate. We find that any approach that considers unemployment inflow and outflow rates performs well in the near term. Over longer forecast horizons, Tasci (2012) appears to be a ...
FRB Atlanta Working Paper , Paper 2015-1

Working Paper
Dividend Momentum and Stock Return Predictability: A Bayesian Approach
Rubio-Ramirez, Juan F.; Petrella, Ivan; Antolin-Diaz, Juan (2021-11-10)
A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra ...
FRB Atlanta Working Paper , Paper 2021-25

Working Paper
The Transmission of Financial Shocks and Leverage of Financial Institutions: An Endogenous Regime-Switching Framework
Hubrich, Kirstin; Waggoner, Daniel F. (2022-06-02)
We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose, we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities that depend on the state of the economy. We propose new identification techniques for regime switching models.Recently developed theoretical models emphasize the role of bank balance sheets for the build-up of financial instabilities and the amplification of financial shocks. We build a market-based ...
FRB Atlanta Working Paper , Paper 2022-5

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Goulet Coulombe, Philippe; Rapach, David E.; Schwenk-Nebbe, Sander; Montes Schütte, Erik Christian; Borup, Daniel (2022-11-07)
We develop metrics based on Shapley values for interpreting time-series forecasting models, including“black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the ...
FRB Atlanta Working Paper , Paper 2022-16

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Goulet Coulombe, Philippe; Rapach, David E.; Schwenk-Nebbe, Sander; Montes Schütte, Erik Christian; Borup, Daniel (2024-02-21)
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a researcher to anatomize out-of-sample forecasting accuracy, thereby providing valuable information for interpreting time-series forecasting models. The PBSV is model agnostic—so it can be applied to any forecasting model, including "black box" models in machine learning, and it can be used for any loss function. We also develop the TS-Shapley-VI, a version of the conventional Shapley value that ...
FRB Atlanta Working Paper , Paper 2022-16b

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