The Role of Models and Probabilities in the Monetary Policy Process

By Christopher A. Sims. 2002

336 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/55e2c2b33b089d60db62469c51915713563477b8

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Citations in the corpus

Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks (2012, neutral): “…the formal macro models apparently in use by central banks have up until now either been based on parameters that have been calibrated (rather than estimated), or on parameter estimates from a large number of di ﬀ erent sector models, whose joint implications have not been thoroughly investigated.”
Practical Model-Based Monetary Policy Analysis: A How-To Guide (2006, background): “The Bayesian approach has a number of obvious advantages, but the main one is that it improves the transparency of the model-building process by forcing the model builder to reveal her priors—see Sims (2002), among others.”
Explaining the great moderation: it is not the shocks (2008, neutral): “Possibility (i) is unlikely since institutional forecasters are quite accurate (see, for example, Sims, 2002 ).”
Forecasting U.S. Inflation by Bayesian Model Averaging (2003, neutral): “…Atkeson and Ohanian (2001) found that Phillips curve based forecasts of inflation give larger out-of-sample prediction errors than a simple random walk forecast of inflation, although this specific result is very sensitive to the sample period and to the choice of inflation measure (Sims (2002)).”
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility (2011, background): “As discussed by such authors as Romer and Romer (2000), Sims (2002), and Croushore (2006), evaluating the accuracy of real-time forecasts requires a difficult decision on what to use as the actual data when calculating forecast errors.”
The Bank of England Quarterly Model (2005, background): “The results of Smets and Wouters (2004) on US data suggest a weight (for domestic price setting) of around 0.”
Common Drifting Volatility in Large Bayesian VARs (2012, neutral): “μ̄Π such that E[Π (ij) l ] = 0 ∀ i, j, l (35)”
Accounting Earnings and Gross Domestic Product (2014, neutral): “…respectively as ESPFq ðgqþ1Þ through ESPFq ðgqþ4Þ, from the Federal Reserve Bank of Philadelphia.2 The SPF is the longest and most well-regarded publicly available survey of quarterly macroeconomic forecasts, and it has been widely used in prior research (e.g., Sims, 2002; Ang et al., 2007).”
A comparison of forecast performance between federal reserve staff forecasts, simple reduced‐form models, and a DSGE model (2010, methodology): “ Sims (2003) , posterior odds can in some cases appear surprisingly decisive, and our results”
Central Bank Transparency and the Signal Value of Prices (2006, background): “Sims (2002) gives a vivid description of the way in which central bank forecasts are arrived at.”
The ECB Survey of Professional Forecasters (SPF) - A Review After Eight Years' Experience (2007, background): ““Sheppard’s correction” (Stuart and Ord (1994)), which adjusts the calculated variance by subtracting one-twelfth of the squared bin width.”
How Useful Are Estimated DSGE Model Forecasts for Central Bankers? (2010, neutral): “[21] Sims, C.”
Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? (2007, background): “In particular, Atkeson and Ohanian (2001), Fisher et al. (2002), Sims (2002), and Cecchetti et al. (2000), among others, show that the accuracy of Phillips curve-based forecasts depends crucially on the sample period.”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, background): “As discussed in such sources as Romer and Romer (2000), Sims (2002), and Croushore (2006), evaluating the accuracy of real-time forecasts requires a difficult decision on what to take as the actual data in calculating forecast errors.”