A simple recursive forecasting model

Branch, William A.; Evans, George W.

doi:10.1016/j.econlet.2005.09.005

Cited by 228 publications

(150 citation statements)

References 23 publications

Supporting

Mentioning

137

Contrasting

Order By: Relevance

“…Experimental evidence is provided by Heemeijer et al (2004) and Hommes et al (2005). This paper extends Branch (2004) by enlarging the set of competing reduced form models. Our approach, like Mankiw et al (2003), tests for a particular form of sticky information flows in agents' survey expectations.…”

Section: Article In Pressmentioning

confidence: 76%

“…Recent approaches impose bounded rationality at the primitive level; see, for example, Mankiw and Reis (2002), Ball et al (2005), Reis (2004), Branch et al (2004) and Sims (2003). Of these the sticky information model of Mankiw and Reis (2002) yields important (and tractable) implications for macroeconomic policy.…”

Section: Introductionmentioning

confidence: 99%

“…Carroll (2003) provides evidence that the median response in the Survey of Consumers is a distributed lag of the median response from the Survey of Professional Forecasters. Branch (2004), adapting Brock and Hommes (1997), develops a methodology for assessing the forecasting models agents use in forming expectations. In that paper, evidence suggests survey responses are distributed heterogeneously across univariate and multivariate forecasting models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sticky information and model uncertainty in survey data on inflation expectations

Branch

2007

Journal of Economic Dynamics and Control

Self Cite

141

106

View full text Add to dashboard Cite

This paper compares three reduced-form models of heterogeneity in survey inflation expectations. On the one hand, we specify two models of forecasting inflation based on limited information flows of the type developed in Mankiw and Reis [2002. Sticky information versus sticky prices: a proposal to replace the new Keynesian Phillips curve. Quarterly Journal of Economics 117(4), 1295-1328]. We present maximum likelihood results that suggests a sticky information model with a time-varying distribution structure is consistent with the Michigan survey of inflation expectations. We also compare these 'sticky information' models to the endogenous model uncertainty approach in Branch [2004. The theory of rationally heterogeneous expectations: evidence from survey data on inflation expectations. Economic Journal 114, 497]. Non-parametric evidence suggests that models which allow the degree of heterogeneity to change over time provide a better fit of the data. r

show abstract

Section: Article In Pressmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sticky information and model uncertainty in survey data on inflation expectations

Branch

2007

Journal of Economic Dynamics and Control

Self Cite

141

106

View full text Add to dashboard Cite

show abstract

“…It has also been argued by Orphanides and Williams (2005a) that constant gain learning is more reasonable than RLS learning because the learning rule itself is stationary whereas it is time-dependent in RLS. Empirical support for constant gain learning is provided in Orphanides and Williams (2005b), Evans (2006b), andMilani (2005).…”

Section: Real-time Learning With Constant Gainmentioning

confidence: 99%

Model uncertainty and endogenous volatility

Branch

Evans

2007

Review of Economic Dynamics

Self Cite

View full text Add to dashboard Cite

This paper identifies two channels through which the economy can generate endogenous inflation and output volatility, an empirical regularity, by introducing model uncertainty into a Lucas-type monetary model. The equilibrium path of inflation depends on agents' expectations and a vector of exogenous random variables. Following Branch and Evans agents are assumed to underparameterize their forecasting models [Branch, W., Evans, G.W., 2006a. Intrinsic heterogeneity in expectation formation. Journal of Economic Theory 127, 264-295]. A Misspecification Equilibrium arises when beliefs are optimal, given the misspecification, and predictor proportions are based on relative forecast performance. We show that there may exist multiple Misspecification Equilibria, a subset of which is stable under least squares learning and dynamic predictor selection. The dual channels of least squares parameter updating and dynamic predictor selection combine to generate regime switching and endogenous volatility.

show abstract

“…The usual choice for this purpose has been the Least Squares (LS) algorithm (Branch and Evans, 2006;Markiewicz and Pick, 2014), possibly due to its widespread popularity between econometricians. A computationally simpler alternative is offered by the Stochastic Gradient (SG) algorithm (Barucci and Landi, 1997;Evans and Honkapohja, 1998).…”

Section: Introductionmentioning

confidence: 99%

A note on the representative adaptive learning algorithm

Berardi

Galimberti

2014

Economics Letters

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting accuracy and in matching (future) survey forecasts.

show abstract

A simple recursive forecasting model

Cited by 228 publications

References 23 publications

Sticky information and model uncertainty in survey data on inflation expectations

Sticky information and model uncertainty in survey data on inflation expectations

Model uncertainty and endogenous volatility

A note on the representative adaptive learning algorithm

Contact Info

Product

Resources

About