A simple recursive forecasting model

By William A. Branch, George W. Evans. 2006

267 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/af9a2a97dba7b634d4e4747adffb849acbf93a17

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Citations in the corpus

Expectation Shocks and Learning as Drivers of the Business Cycle (2011, unknown): “Almost all forecasters in the survey simply report the BLS release as their perception of t − 1 values.(8) Forecasters observe the values of the variables in t− 1 when communicating forecasts for variables dated t and t + 1.”
Forecasting Random Walks Under Drift Instability (2008, unknown): “Recently, Branch and Evans (2006) consider a number of variations on this class of models and show that a particularly simple form, known as the ‘constant gain least squares’, works reasonably well in forecasting US inflation and GDP growth.”
Expected Returns in Treasury Bonds (2015, unknown): “This approach follows the literature using constant-gain learning to explain macroeconomic dynamics, and inflation dynamics in particular (e.g., Sargent, 1999; Kozicki and Tinsley, 2001, 2005; Orphanides and Williams, 2005; Branch and Evans, 2006).”
Expectations, Learning and Business Cycle Fluctuations (2011, unknown): “Branch and Evans (2006) show that a simple VAR with constant gain performs well in forecasting output growth and inflation, with respect to alternative more sophisticated models.”
The science of monetary policy: an imperfect knowledge perspective (2018, unknown): “In addition, such models successfully track the evolution of long- and short-term survey forecasts see Branch and Evans (2006), Edge, Laubach, and Williams (2007), Kozicki and Tinsley (2012) and Crump, Eusepi, and Moench (2015).”
Forecast Combination Across Estimation Windows (2011, unknown): “Closely related to our approach is the suggestion by Clark and McCracken (2009) that averaging expanding and rolling windows can be useful for forecasting in the presence of structural breaks.”
Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models (pp. 65-101) (2012, background): “Branch and Evans (2006) consider both constant gain and Kalman lter adaptive learning and nd that constant gain learning with gain 0.0345 ts the SPF expectations on GDP growth and GDP de ator in ation the best. Nunes (2009) considers in ation expectations to be a weighted average of rational expecta”
Sticky information and model uncertainty in survey data on inflation expectations (2007, background): “Under these timing assumptions j = 0 is equivalent to full-information.(8) Full information in this setting is not complete information since (1) is a reduced-form VAR that could represent model uncertainty (because of the time-varying parameters).”
Dynamic predictor selection in a new Keynesian model with heterogeneous expectations (2010, methodology): “This is a multinomial logit law of motion and was employed by Brock and Hommes (1997), and then extended to a stochastic setting by Branch and Evans (2006).”