Measurement errors and data estimation: the quantification of survey data

Cunningham, Alastair; Smith, Richard J.; Weale, Martin

doi:10.1017/cbo9780511559785.004

Cited by 13 publications

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“…It was refined by Knö bl (1974), and Carlson and Parkin (1975), and was criticized and extended inter alia by Foster and Gregory (1977), Fishe and Lahiri (1981), Batchelor (1982), Batchelor and Orr (1988), Seitz (1988), Fishe and Idson (1990), Dasgupta and Lahiri (1992), Smith and McAleer (1995), Cunnigham, Smith, and Weale (1998), and Mitchell, Smith, and Weale (2002). There are alternative ways to derive the probability methods.…”

Section: Probability Methodsmentioning

confidence: 99%

Using Forecasts of Forecasters to Forecast

Nolte

Pohlmeier

2006

SSRN Journal

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Section: Probability Methodsmentioning

confidence: 99%

Using Forecasts of Forecasters to Forecast

Nolte

Pohlmeier

2006

SSRN Journal

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“…The four traditional quantifi cation methods against which MSW will be compared all use aggregate survey proportions, namely the balance statistic [BAL], the probability method of Carlson and Parkin (1975) [CP], the regression approach of Pesaran (1984Pesaran ( , 1987 [PES], and the reverse-regression approach of Cunningham et al (1998) [CSW]. Out-of-sample, we also consider a time series (auto-regressive) benchmark, the random walk model.…”

Section: Traditional Survey-based Nowcastsmentioning

confidence: 99%

Nowcasting and predicting data revisions using panel survey data

Matheson

Mitchell

Silverstone

2009

Journal of Forecasting

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The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real-time signal of official output changes. The most commonly used method to produce an aggregate quantitative indicator from business survey responses-the net balance or diffusion index-has changed little in 40 years. This paper investigates whether an improved real-time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in-sample signal about official data than traditional methods. Out-of-sample, it is less clear that it matters how survey data are quantified, with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data. Copyright © 2009 John Wiley & Sons, Ltd.

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“…We first compare the sector‐by‐sector performance of the indicators

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and

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against that of four traditional quantification techniques employed on aggregated proportions: the balance statistic (BAL), the probability method of Carlson and Parkin () (CP), the regression approach of Pesaran (, ) (PES) and the reverse‐regression approach of Cunningham et al . () (CSW) based on the logistic distribution; Appendix C presents a brief review of these various quantification methods. An assessment of their performance at the aggregate level for manufacturing output growth then follows in Section 4.3.1.…”

Section: Indicators Of Sectoral Output Growthmentioning

confidence: 99%

Efficient Aggregation of Panel Qualitative Survey Data

Mitchell¹,

Smith²,

Weale³

2012

J of Applied Econometrics

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SUMMARY Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report ‘up’ and ‘down’. This paper examines disaggregate or firm‐level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms' categorical responses to official data using ordered discrete‐choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm‐level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators. Copyright © 2012 John Wiley & Sons, Ltd.

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Measurement errors and data estimation: the quantification of survey data

Cited by 13 publications

References 0 publications

Using Forecasts of Forecasters to Forecast

Using Forecasts of Forecasters to Forecast

Nowcasting and predicting data revisions using panel survey data

Efficient Aggregation of Panel Qualitative Survey Data

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