The Quantification of Qualitative Survey Data<b>:</b> A Critical Assessment

Michela, Nardo

doi:10.1046/j.1467-6419.2003.00208.x

Cited by 128 publications

(78 citation statements)

References 67 publications

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“…Various modifications of this setup have been proposed in the literature (see Nardo, 2003, for a survey of the literature) and we briefly discuss some of these modified procedures below. One extension of the original framework is to include z t in the information set (cf.…”

Section: Quantification Methodsmentioning

confidence: 99%

“…In this situation, a common procedure is to apply some quantification algorithm to convert qualitative survey expectations into quantitative forecasts. While many shortcomings of available quantification methods have been discussed (see Nardo, 2003, for an overview), it is still common to apply variants of these quantification techniques when confronted with qualitative data (e.g. Doepke et al, 2008, Mankiw et al, 2003, Menkhoff et al, 2009.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying survey expectations: What’s wrong with the probability approach?

Breitung

Schmeling

2013

International Journal of Forecasting

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Provided in Cooperation with Quantifying survey expectations:What's wrong with the probability approach? AbstractWe study a matched sample of individual stock market forecasts consisting of both qualitative and quantitative forecasts. This allows us to test for the quality of forecast quantification methods by comparing quantified qualitative forecasts with actual quantitative forecasts. Focusing mainly on the widely used quantification framework advocated by Carlson and Parkin (1975), the so-called "probability approach", we find that quantified expectations derived from the probability approach display a surprisingly weak correlation with reported quantitative stock return forecasts. We trace the reason for this low correlation to the importance of asymmetric and time-varying thresholds, whereas individual heterogeneity across forecasters seems to play a minor role. Hence, our results suggest that qualitative survey data may not be a very useful device to obtain quantitative forecasts and we suggest ways to remedy this problem when designing qualitative surveys.JEL-Classification: C53, D84, G17

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Section: Quantification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying survey expectations: What’s wrong with the probability approach?

Breitung

Schmeling

2013

International Journal of Forecasting

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“…Th erefore, in making a comparison it is sensible to consider lags of PMI ■ standardise the units of measurement -ONS data is reported as a growth rate whereas PMI data as a balance statistic, so the measures are not directly comparable. Th ere is a large literature on how balance statistic data may be mapped to continuous growth rates which is summarised by Nardo (2003). However, here we simply standardise both data sources, which reduces each Office for National Statistics lead to a divergence between the two data sources, especially if the above industries which tend to fall in the distribution and public services, behave diff erently to the rest of the services sector.…”

Section: Monitoring the Coherence Between Ons And Pmi Data -An Updatementioning

confidence: 99%

Economic review

Chamberlin

2010

Econ Lab Market Rev

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The Preliminary estimate of Gross Domestic Product (GDP) reports that the UK economy grew by 0.2 per cent in 2010 Q1 -slightly weaker than the previous quarter when GDP expanded by 0.4 per cent. Previous work looking at the coherence of ONS and Purchasing Managers' Index (PMI) data is updated, which shows that PMI is suggesting a stronger recovery in activity than offi cial data. Although unemployment increased to 8.0 per cent in DecemberFebruary 2010, the labour market has not weakened to the same extent as in the previous two recessions. Annual infl ation in the Consumer Prices Index (CPI) picked up to 3.4 per cent in March, of which one-and-three-quarters percentage points is estimated to be accounted for by the reversion in the rate of VAT to 17.5 per cent in January 2010. SUMMARY May 2010Graeme Chamberlin Offi ce for National Statistics UK economy expands by 0.2 per cent in fi rst quarter of 2010A ft er growing by 0.4 per cent in the fi nal quarter of last year, Preliminary estimates of Gross Domestic Product (GDP) report that the UK economy expanded by 0.2 per cent in the fi rst quarter of 2010. Th is, the second successive quarter of positive growth, follows six quarters of contraction where the level of GDP fell from peak to trough by around 6.2 per cent (Figure 1). As a result, output in the economy as whole and also in the production and services sub sectors, remains signifi cantly below prerecession highs. Th e slowdown in growth from the fi rst quarter may partly refl ect the disruption caused by heavy snowfall across the country in January.

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“…Furthermore, there is evidence about the important role that this kind of surveys play to anticipated changes on future economic fluctuations (Nardo 2003;Vermeulen 2014;Girardi 2014;Leduc and Liu 2012;Leduc and Sill 2013). Yet, we use the Economic Sentiment Indicator (ESI henceforth), which has close relation with key economic aggregates (Nardo 2003;Pesaran and Weale 2005;Banerjee et al 2005;Silgoner 2007;Giannone et al 2009;Girardi 2014). In fact, there is evidence in favor of its ability to forecast turning points over the business cycle (Tayor and McNabb 2007;Ozyildirim et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?

Rubilar-González

Pino

2017

SERIEs

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By using the Economic Sentiment Indicator and Autoregressive Markov Switching models, this paper provides an effective tool to identify and characterize expectations of business cycle phases for Germany, Spain, the Euro Area, and the European Union. This information is useful for policy makers who can focus their efforts on strengthening economies identified as more sensitive to international disturbances. Our results also reveal a lack of synchronization of the expectations across the Euro-Area. This also takes importance for policy implications given common public policies can have undesired impacts across the different Euro-Area economies.

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The Quantification of Qualitative Survey Data: A Critical Assessment

Cited by 128 publications

References 67 publications

Quantifying survey expectations: What’s wrong with the probability approach?

Quantifying survey expectations: What’s wrong with the probability approach?

Economic review

Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?

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