Alternative procedures for converting qualitative response data to quantitative expectations: An application to Australian manufacturing

Jeremy P Smith · Michael J. McAleer

112 citations0/0 files storedJournal of Applied Econometrics

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Survey Methodology and Nonresponse

Abstract

Abstract This paper analyses and extends alternative procedures for converting qualitative expectations responses to quantitative expectations. A number of conversion procedures is investigated, including the probability model, the time‐varying parameter probability model, and the regression approach. The informational content of the survey expectations is compared with simple time series models. It is found that the expectations models are superior for many series, both in terms of producing lower forecast root mean square error (RMSE) values and in detecting turning points in the actual data. Survey expectations are also tested for rational expectations in aggregate using the orthogonality test.

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References

1992A Simple Nonparametric Test of Predictive Performance 1989The limits to rational expectations 1975Inflation Expectations 1988The Limits to Rational Expectations.1981The Formation of Inflationary Expectations 1952On the Time Shape of Economic Microvariables and the Munich Business Test 1992On the Time Shape of Economic Microvariables and the Munich Business Test 1985Formation of Inflation Expectations in British Manufacturing Industries

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2015Measuring Disagreement in Qualitative Expectations 2018Financial constraints on investment: Effects of firm size and the financial crisis 2002Quantification of Qualitative Firm‐Level Survey Data 2008Inflation expectations of experts and ECB communication 1994Cointegration and direct tests of the rational expectations hypothesis 2017Evolutionary Computation for Macroeconomic Forecasting

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