Averaging Forecasts from Vars with Uncertain Instabilities

By Todd E. Clark, Michael W. McCracken. 2006

157 citations recorded by the source corpus.

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combining various models of instability in improving VAR forecasts made with real-time data.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/cccb794c7ef552419ea97d4f3445bc51a4860f26

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In Economics and Econometrics

Citations in the corpus

Advances in Forecasting Under Instability (2012, unknown): “(A) P sup =m;:::;P j p Pb 1 A A ;P j; (49)”
Real-Time Inflation Forecasting in a Changing World (2009, unknown): “Clark and McCracken (2008), on the other hand, use VAR models with sequentially updating of lag orders, various windows for parameter estimation, (over-)differencing of variables, intercept corrections, and allowing for discrete breaks in parameters.”
Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “Proof of Theorem 1: Straightforward but tedious algebra along the lines of West (1996) and Clark and McCracken (2001) reveals that”
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility (2011, methodology): “Under the stochastic volatility model, from Cogley and Sargent (2005), the log variances in t follow random-walk processes.”
Combining forecast densities from VARs with uncertain instabilities (2010, methodology): “To facilitate comparisons with the literature on point forecast combination, we follow Clark and McCracken (2008) [CM, 2008] and consider the forecasts from Vector Autoregressive models (VARs) of output, prices and the short-term interest rate using the same real-time US data.”
A comparison of forecast performance between federal reserve staff forecasts, simple reduced‐form models, and a DSGE model (2010, background): “, Erceg et al. [2006]) have provided examples of richly-specified models with firm microeconomic foundations that can be applied to policy questions.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “Equally-weighted combinations have been found to be surprisingly effective for point forecasting, see Clemen (1989) and Stock and Watson (2004).”
The Evolution of Forecast Density Combinations in Economics (2018, background): “And there have been numerous applications with, for example, Clark and McCracken (2010) finding that linear combinations of point forecasts, as produced in real-time, are an effective means of mitigating the structural or what they call “uncertain instabilities” that are found to plague individual…”
Forecast combinations: An over 50-year review (2022, methodology): “The pseudo out-ofsample performance used in these weighting schemes is commonly computed based on rolling or recursive (expanding) windows (e.g., Stock and Watson, 1998; Clark and McCracken, 2010; Genre et al., 2013).”