Measuring Disagreement in Qualitative Expectations

Abstract

We assess how well measures of disagreement in qualitative survey expectations reflect disagreement in corresponding quantitative expectations. We consider a variety of measures, belonging to two categories: measures of dispersion in nominal and ordinal variables and measures based on the probability approach of Carlson and Parkin (Economica, 1975; 42, 123–138). Using data from two household surveys that collect both qualitative and quantitative inflation expectations, we find that the probability approaches with time‐varying categorization thresholds and either a piecewise uniform or t distribution perform best and the resulting disagreement estimates are highly correlated with the benchmark. Copyright © 2015 John Wiley & Sons, Ltd.

Explaining Disagreement on Interest Rates in a Taylor‐Rule Setting

“…Both the IQV and MOV for short‐run qualitative inflation expectations from consumers in the Surveys of Consumers are shown to be highly positively correlated with the corresponding cross‐sectional standard deviation of quantitative inflation expectations in Mokinski et al . (). In that sense, both measures should be viable.…”

“…Mokinski et al . () analyze this in detail for consumers in the Surveys of Consumers. However, our dataset differs slightly as we use a different truncation and sample period, and we calculate disagreement not within the full cross‐section at each moment in time, but within the rotating panel cohort.…”

Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies

“…The relationship between changes in expectations and economic growth has been widely investigated (Mokinski et al, 2015;Dees at al., 2013;Leduc and Sill, 2013;Lui, Mitchell and Weale, 2011a,b;Zanin, 2010;Claveria et al, 2007;Abberger, 2007;Nolte and Pohlmeier, 2007;Mitchell et al, 2005a,b), but never before by means of SR. By combining a SR approach with GP, we are able to identify the optimal combinations of a wide range of survey variables that best fits the actual evolution of the gross domestic product (GDP) in a set of countries of the Organisation for Economic Co-operation and Development (OECD).…”

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

“…There are many studies analysing the relationship between survey data and macroeconomic variables (Mokinski et al 2015;Dees and Brinca 2013;Leduc and Sill 2013;Lui et al 2011aLui et al , 2011bZanin 2010;Croux 2007, 2010;Claveria et al 2007;Abberger 2007;Nolte and Pohlmeier 2007;Cotsomitis and Kwan 2006;Mitchell et al 2005aMitchell et al , 2005b, but this is the first study to link both sources of information by means of symbolic regression (SR) to derive a leading economic indicator. As far as we know, this is the first attempt at designing an empirically generated SR-based indicator.…”