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2 edition of Alternative procedures for testing the rational expectations hypothesis with qualitative data found in the catalog.

Alternative procedures for testing the rational expectations hypothesis with qualitative data

Shinobu Kobayashi

Alternative procedures for testing the rational expectations hypothesis with qualitative data

an application toJapanese manufacturers

by Shinobu Kobayashi

  • 205 Want to read
  • 30 Currently reading

Published by typescript in [s.l.] .
Written in English


Edition Notes

Dissertation (M.Sc.) - University of Warwick, 1992.

Statementby Shinobu Kobayashi.
ID Numbers
Open LibraryOL20700257M

C. Lupi: Direct Tests of the Rational Expectations Hypothesis From property i) it derives that unbiasedness is a necessary, though not sufficient, condition for the expectations to be rational. A first test can therefore be based on the estimation of . Once you have a hypothesis: Testing it! Four Steps: 1. State the null and alternative hypothesis 2. Set criteria for decision 3. Collect data 4. Analyze data and decide if null hypothesis can be rejectedFile Size: KB.

In section we introduce the rational expectations hypothesis and discuss the importance of allowing for heterogeneity of expec-tations in relating theory to survey expectations. To this end a weak form of the rational expectations hypothesis which focusses on average expectations rather individual expecta-tions is advanced. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis .

Previous work with survey data on inflationary expectations casts doubt on the Rational Expectations Hypothesis. In this paper, we develop a model of expectation formation where agents form their forecasts of inflation by selecting a predictor function from a set of costly alternatives whereby they may rationally choose a method other than the most accurate. This paper focuses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focuses on average expectations rather Author: Mohammad Hashem Pesaran and Martin R. Weale.


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Alternative procedures for testing the rational expectations hypothesis with qualitative data by Shinobu Kobayashi Download PDF EPUB FB2

Testing for rational expectations with qualitative survey data. Manchester School of Economic and Social Statistics, 53, Qualitative Survey Data on Expectations Simon (, p. ) support direct empirical testing of the rational expectations hypothesis, Prescott is but one of a number of distinguished economists holding the opposite viewpoint.

hypothesis, on the other hand, agents use Z, in forming their expectations for periods t + 1 and beyond, and candidates for explanatory variables are Iv+ 1, w r+2, etc., with Hansen’s method used for the estimation.

The rational expectations hypothesis is tested by comparing the estimated belief function with the "true" offer function which is estimated using data on offers actually made to contestants. We find that there is a significant difference between these two functions, and hence we reject the rational expectations hypothesis.

and fails to provide an adequate confidence level of whether the conditions jointly hold. This article builds adequate statistics for testing the hypothesis of subjective mode and median rationality when. agents provide point predictions of categorical : Carlos Madeira. Regarding the former option, Common () generated simulated expectations to test the rational expectations hypothesis.

Nardo and Cabeza-Gutés () designed a simulation experiment to assess the performance of the different quantification methods. By means of simulation-based expectations, Löffler () and Terai () estimated. The test procedures are related to the sort of data available, generally obtained from tendency surveys.

We distinguish between qualitative and quantitative data, we define in each case the notion of rational expectation. Then it is possible to characterize the rational expectation hypothesis and to develop adapted test by: The rational expectations hypothesis can be characterized as individuals forming their subjective expectations of a variable (ye) consistent with an objective mathematical expectation of (y) that is conditional on all available information (ω).

The dominant approach for the past several decades, of course, has made use of the hypothesis of model-consistent or “rational expectations” (RE): the assumption that people have probability beliefs that coincide with the probabilities predicted by one’s model. Qualitative studies use data collected from participant observations, the observations of researchers, interviews, texts and similar sources of information.

Unlike. Expected Versus Realized Income Expectations: A Test of the Rational Expectations Hypothesis. We analyze answers to household survey questions on whether household income has changed in the past twelve months, and on whether respondents expect their household income to change in the next twelve months.

the forecasts of real GDP growth rate intensively for testing the rational expectations hypothesis. In the survey, the individual data of fifty forecasters are available from April to August We choose the data of thirty three forecasters who reported their forecasts for more than consecutive thirty six months.

This paper focuses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized.

A weak form of the rational expectations hypothesis which focuses on average expectations rather than individual expectations. Data obtained from business and consumer surveys are often used in forecasting models and in testing different expectation formation schemes.

Their use, however, requires a previous step of transformation of the qualitative data into quantitative figures. This paper contains a critical review of the different quantification methods. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized.

A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. The test size α therefore becomes a metric for rationality, since α=0 implies complete insensitivity to evidence and α=1 implies continual rational expectations.

This paper focuses on survey expectations and discusses their uses for testing and modelling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized.

A weak form of the rational expectations hypothesis which focuses on average expectations rather than individual expectations is. When alternative testing methods are used what the Chow Test showed to be irrational has often been shown as rational.

Thus, any conclusion about the irrationality of the rational expectations hypothesis based on these tests can not be assumed to be accurate. The figures show that the validity of the expectations hypothesis may be sensitive to the choice of central tendency measure used to represent public expectations.

This alternative hypothesis represents an alternative so dramatically at variance with the expectations model of the term structure that it could not be reconciled with the model by Cited by: the shortage of empirical data for a more detailed evaluation of expectation measures, this article focuses more on the presentation of the most commonly established methods for their capturing and the methodological aspect of the prevailing models.

KEYWORDS: expectations, rational expectations, surveys, qualitative measures, quantitative. The paper then provides an account of the various surveys of expectations, reviews alternative methods of quantifying the qualitative surveys, and discusses the use of aggregate and individual survey responses in the analysis of expectations and for of expectations formation, survey data, heterogeneity, tests of rational Author: M.

Hashem Pesaran and Martin Weale.Hypothesis Testing: Methodology and Limitations Hypothesis tests are part of the basic methodological toolkit of social and behavioral scientists.

The philo-sophical and practical debates underlying their ap-plication are, however, often neglected. The fruitful application of hypothesis testing can benefit from a.use quantitative expectations data and qualitative survey data that has been quantified.

The main finding is that the power of tests for rational expectations against constant gain learning may be very small, making it impossible to distinguish the hypotheses.