Monetary Aggregation and the Demand for Assets

Fisher, Douglas; Fleissig, Adrian R.

doi:10.2307/2953708

Cited by 60 publications

(47 citation statements)

References 11 publications

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“…Belongia and Chalfant (1989) find evidence for several groups including M1a for 1983-85. Fisher andFleissig (1997) find the strongest support for an unconventional narrow group, consisting of M1 minus business demand deposits. They reject M1, M2, and M3 over the majority of their sub-periods, which cover 1960-1993.…”

Section: Introductionmentioning

confidence: 93%

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Sweep programs and optimal monetary aggregation

Jones

Dutkowsky

Elger³

2005

Journal of Banking & Finance

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Section: Introductionmentioning

confidence: 93%

“…One might also want to separate household and business DD, which was emphasized inFisher and Fleissig (1997). The separation of household and business DD inThornton and Yue's (1992) widely used monetary dataset(underlying Fisher and Fleissig, 1997) was based on data that were discontinued in 1990.…”

mentioning

confidence: 99%

Sweep programs and optimal monetary aggregation

Jones

Dutkowsky

Elger³

2005

Journal of Banking & Finance

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“…One will …nd in Swo¤ord and Whitney (1987) or in Fisher and Fleissig (1997) implementations of such an approach.…”

Section: Preliminariesmentioning

confidence: 99%

Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Seminonparametric Test for Weak Separability

Barnett

Peretti

2009

Macroecon. Dynam.

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In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with seminonparametric tests for weak separability. It introduces both a necessary and su¢ cient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors.

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“…This follows from that utility maximization is a necessary condition for monetary aggregates to exist (Barnett, 1980). This, in turn, has motivated numerous studies to use revealed preference analysis to test whether aggregates satisfy this necessary condition; See for examples Swo¤ord and Whitney (1986, 1987, 1994 and Fisher and Fleissig (1997), and more recently Jones, Dutkowsky and Elger (2005), Elger, Jones, Edgerton and Binner (2008), and Jha and Longjam (2006). 4 From an applied perspective, revealed preference based tests are attractive since they are nonparametric in the sense of not having to stipulate any parametric form for the utility function.…”

Section: Introductionmentioning

confidence: 99%

A Simple Method to Account for Measurement Errors in Revealed Preference Tests

Hjertstrand

2013

SSRN Journal

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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 EconStor may AbstractRevealed preference tests are widely used in empirical applications of consumer rationality. These are static tests, and consequently, lack ability to handle measurement errors in the data. This paper extends and generalizes existing procedures that account for measurement errors in revealed preference tests. In particular, it introduces a very e¢ cient method to implement these procedures, which make them operational for large data sets. The paper illustrates the new method for both classical and Berkson measurement errors models.

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Monetary Aggregation and the Demand for Assets

Cited by 60 publications

References 11 publications

Sweep programs and optimal monetary aggregation

Sweep programs and optimal monetary aggregation

Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Seminonparametric Test for Weak Separability

A Simple Method to Account for Measurement Errors in Revealed Preference Tests

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