Interest Rates, Uncertainty and the Livingston Data

Bomberger, William A.; Frazer, William J.

doi:10.2307/2327525

Cited by 22 publications

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“…One of the survey questions solicits forecasts of the Consumer Price Index, 6 and 12 months ahead (Carlson, 1977). The imputed inflation forecasts have been extensively analyzed in the economics literature (Caskey, Hafer & Resler, 1982;Bomberger & Frazer, 1981;Jacobs & Jones, 1980;Pearce, 1979;Mullineaux, 1978;Pesando, 1975 In most modeling situations actual expectations data are unavailable and the analyst must use prior estimates of the parameters. To test this procedure, the analysis of the Livingston data was performed entirely with prior estimates of the parameters of the TREND function.…”

Section: Example I: Inflationary Expectationsmentioning

confidence: 99%

Systems Simulation. Expectation formation in behavioral simulation models

Sterman

1987

Syst. Res.

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The nature and rationality of expectations are hotly debated in economics and management science. Expectations are usually portrayed in behavioral simulation models and system dynamics as adaptive learning proceeses. This paper presents a behavioral model of trend expectation formation. The model assumes expectations about the growth rate of a quantity are formed adaptively from the recent growth rate of the input variable itself. The model is then tested directly against actual forecasts in two quite different domains: short-term expectations of inflation and long-term energy demand forecasts. In both cases the model replicates the evolution of the expectations quite well over extended time periods. The results support the use of adaptive expectations and trend extrapolation. The results also suggest the presence of additional judgmental heuristics which can have dynamic and policy significance. In particular, there seem to be substantial conservatism in both inflation expectations and energy demand forecasts: forecasters systematically underestimate the growth rate of the input. Such conservatism are consistent with the empirical literature on judgment and decision making. The results show it is possible t o test the expectation formation processes assumed in behavioral simulation modele; implications for use of adaptive expectations in behavioral models when empirical data are unavailable are ale0 die-KEY WORDS: individual, organizational and societal systems; system dynamics; forecaets; inflationary expectations; energy demand; computer simulation; behavioral simulation models c*J EXPECTATION FORMATION IN BEHAVIORAL SIMULATION MODELShe nature and rationality of expecta-T tions are hotly debated in economics and management science. Expectation formation is particularly important in behavioral simulation models, a class of dynamic models which share the following characteristics (Simon, 1982;Cyert & March, 1963;Nelson & Winter, 1982;Forrester, 1961;Morecroft, 1983Morecroft, , 1985Sterman, 1985;Sterman & Richardson, 1985): (i) A descriptive rather than normative representation of human behavior. Decision making behavior is portrayed in terms of the heuristics and routines used by the I am indebted to the members of the MIT system dynamics group for their comments and criticisms, and to several anonymous referees for useful suggestions. Tim Schiller of the Federal Reserve Bank of Philadelphia supplied me with the Livingston data.190 Behavioral Science, Volume 32, 1987 actors in the system rather than as the behavior which maximizes utility.(ii) The limitations of human cognitive capabilities are explicitly accounted for in modeling behavior.(iii) The availability and quality of information is explicitly treated including possible bias, misinterpretation, distortion, and delay.(iv) The physical and institutional structure of the system is explicit, including organizational design such as task and goal segmentation, the stock and flow networks that characterize the physical processes under study, and lags betwee...

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Section: Example I: Inflationary Expectationsmentioning

confidence: 99%

Systems Simulation. Expectation formation in behavioral simulation models

Sterman

1987

Syst. Res.

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“…Among others, Bomberger and Frazer (1981), Cukierman and Wachtel (1982), and Dua (1993, 1996) propose the root mean squared error (rmse s ) as a measure of uncertainty. It is calculated by averaging the individual squared forecast errors in each period t:…”

Section: Survey-based Measuresmentioning

confidence: 99%

“…That is, an observation for uncertainty at time t is available only when realized inflation is observed at time t + 12. Bomberger and Frazer (1981), Bomberger (1996Bomberger ( , 1999, and Giordani and Söderlind (2003) propose the cross-sectional dispersion of point forecasts (disagreement) as a measure of uncertainty. Instead of using the cross-sectional standard deviation of forecasts, we follow Mankiw et al (2003) and rely on the interquartile range (iqr s ) since it is more robust to outliers.…”

Section: Survey-based Measuresmentioning

confidence: 99%

Inflation uncertainty revisited: a proposal for robust measurement

2014

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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 AbstractAny measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information contained in different measures derived from survey data, a variety of forecast models, and volatility models. We show that all measures are driven by a common component which constitutes an indicator for inflation uncertainty. Moreover, the idiosyncratic component of survey disagreement contains systematic measurement error during economic downturns. Finally, we study the Friedman-Ball hypothesis. Using the indicator, it turns out that higher inflation is followed by higher uncertainty. By contrast, we obtain contradictory results for the individual measures. We also document that, after an inflationary shock, uncertainty decreases in the first two months which is traceable to the energy component in CPI inflation.JEL Code: C53, E31, E37.

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“…Disagreement in inflation expectations is often regarded as a measure of IU (Bomberger and Frazer, 1981;Holland, 1993). While Capistrán and Ramos-Francia (2010) report that the disagreement of inflation expectations is lower in economies where inflation targeting strategies are adopted, Dovern et al (2012) document that the dispersion diminishes with increasing degrees of central bank independence.…”

Section: Related Studies On the Determinants Of Iumentioning

confidence: 99%

Cross-Sectional Evidence on the Relation between Monetary Policy, Macroeconomic Conditions and Low-Frequency Inflation Uncertainty

Conrad

Hartmann

2014

SSRN Journal

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We examine how the interaction between monetary policy and macroeconomic conditions affects inflation uncertainty in the long-term. The unobservable inflation uncertainty is quantified by means of the slowly evolving long-term variance component of inflation in the framework of the Spline-GARCH model (Engle and Rangel, 2008). For a cross-section of 13 developed economies, we find that long-term inflation uncertainty is high if central bank governors are perceived as less inflation-averse and if the conduct of monetary policy is ad-hoc rather than rule-based. JEL classification: E58, E65, E31

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Interest Rates, Uncertainty and the Livingston Data

Cited by 22 publications

References 0 publications

Systems Simulation. Expectation formation in behavioral simulation models

Systems Simulation. Expectation formation in behavioral simulation models

Inflation uncertainty revisited: a proposal for robust measurement

Cross-Sectional Evidence on the Relation between Monetary Policy, Macroeconomic Conditions and Low-Frequency Inflation Uncertainty

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