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Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models
Arias, Jonas; Rubio-Ramírez, Juan Francisco; Shin, Minchul - 2021
We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP-SV. First, the ordering does not affect point...
Persistent link: https://www.econbiz.de/10014048674
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DSGE Model-Based Forecasting of Non-Modelled Variables
Schorfheide, Frank; Sill, D. Keith; Kryshko, Maxym - 2019
This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not …
Persistent link: https://www.econbiz.de/10014214672
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Improving GDP Measurement : A Forecast Combination Perspective
Aruoba, S. Borağan; Diebold, Francis X.; Nalewaik, Jeremy - 2011
widely-used income-side version GDI . The authors propose and explore a "forecast combination" approach to combining them …
Persistent link: https://www.econbiz.de/10014177383
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Can Oil Prices Forecast Exchange Rates?
Ferraro, Domenico; Rogoff, Kenneth S.; Rossi, Barbara - 2011
This paper investigates whether oil prices have a reliable and stable out-of-sample relationship with the Canadian/U.S. dollar nominal exchange rate. Despite state-of-the-art methodologies, the authors find little systematic relation between oil prices and the exchange rate at the monthly and...
Persistent link: https://www.econbiz.de/10014178173
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Forecasting Consumption Spending Using Credit Bureau Data
Croushore, Dean Darrell; Wilshusen, Stephanie - 2020
real-time, out-of-sample exercise is conducted to forecast one-quarter ahead consumption spending. The exercise is run … using credit bureau data produces lower or higher root-mean-squared-forecast errors than the baseline model. Key features of … the analysis include the use of real-time data, out-of-sample forecast tests, a strong parsimonious benchmark model, and …
Persistent link: https://www.econbiz.de/10014048857
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Real-Time Datasets Really Do Make a Difference : Definitional Change, Data Release, and Forecasting
Fernández, Andrés; Swanson, Norman R. - 2019
estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models …
Persistent link: https://www.econbiz.de/10014202227
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Do Phillips Curves Conditionally Help to Forecast Inflation?
Dotsey, Michael - 2017
This paper reexamines the forecasting ability of Phillips curves from both an unconditional and conditional perspective by applying the method developed by Giacomini and White (2006). We find that forecasts from our Phillips curve models tend to be unconditionally inferior to those from our...
Persistent link: https://www.econbiz.de/10012948667
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Do Phillips Curves Conditionally Help to Forecast Inflation?
Dotsey, Michael - 2017
This paper reexamines the forecasting ability of Phillips curves from both an unconditional and conditional perspective by applying the method developed by Giacomini and White (2006). We find that forecasts from our Phillips curve models tend to be unconditionally inferior to those from our...
Persistent link: https://www.econbiz.de/10012948669
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Out-of-Sample Forecast Tests Robust to the Choice of Window Size
Rossi, Barbara - 2011
choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over …
Persistent link: https://www.econbiz.de/10013121687
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One Threshold Doesn't Fit All : Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas
Meursault, Vitaly; Moulton, Daniel; Santucci, Larry; … - 2022
Modeling advances create credit scores that predict default better overall, but raise concerns about their effect on protected groups. Focusing on low- and moderate-income (LMI) areas, we use an approach from the Fairness in Machine Learning literature — fairness constraints via group-specific...
Persistent link: https://www.econbiz.de/10014236201