Nowcasting UK GDP During the Depression

Paul Smith

Research output: Working paperDiscussion paper

22 Citations (Scopus)

Abstract

Nowcasting UK GDP during the Depression reviews the performance of several statistical techniques in nowcasting preliminary estimates of UK GDP, particularly during the recent depression. Traditional bridging equations, MIDAS regressions and factor models are all considered. While there are various theoretical differences and perceived advantages for each technique, replicated real-time out-ofsample testing shows that, in practice, there is in fact little to choose between methods in terms of end-of-period nowcasting accuracy. The analysis also reveals that none of the aforementioned statistical models can consistently beat a consensus of professional economists in nowcasting preliminary GDP estimates. This inability of statistical models to beat the consensus may reflect several factors, one of which is the revisions and re-appraisal of trends inherent in UK GDP statistics. The suggestion is that these changes impact on observed relationships between GDP and indicator variables such as business surveys, which impairs nowcasting performance. Indeed, using a synthetic series based purely on observed preliminary GDP estimates, which introduces stability to the target variable series, the nowcasting accuracy of regressions including closely-watched PMI data is improved by 25-40 percentage points relative to a naive benchmark.
LanguageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Pages1-37
Number of pages38
Volume16
Publication statusPublished - Apr 2016

Fingerprint

Nowcasting
Statistical model
Business survey
Regression model
Testing
Factors
Economists
Statistics
Benchmark

Keywords

  • nowcasting
  • forecasting
  • real-time data
  • GDP
  • midas

Cite this

Smith, P. (2016). Nowcasting UK GDP During the Depression. (06 ed.) (pp. 1-37). Glasgow: University of Strathclyde.
Smith, Paul. / Nowcasting UK GDP During the Depression. 06. ed. Glasgow : University of Strathclyde, 2016. pp. 1-37
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Smith, P 2016 'Nowcasting UK GDP During the Depression' 06 edn, University of Strathclyde, Glasgow, pp. 1-37.

Nowcasting UK GDP During the Depression. / Smith, Paul.

06. ed. Glasgow : University of Strathclyde, 2016. p. 1-37.

Research output: Working paperDiscussion paper

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T1 - Nowcasting UK GDP During the Depression

AU - Smith, Paul

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PY - 2016/4

Y1 - 2016/4

N2 - Nowcasting UK GDP during the Depression reviews the performance of several statistical techniques in nowcasting preliminary estimates of UK GDP, particularly during the recent depression. Traditional bridging equations, MIDAS regressions and factor models are all considered. While there are various theoretical differences and perceived advantages for each technique, replicated real-time out-ofsample testing shows that, in practice, there is in fact little to choose between methods in terms of end-of-period nowcasting accuracy. The analysis also reveals that none of the aforementioned statistical models can consistently beat a consensus of professional economists in nowcasting preliminary GDP estimates. This inability of statistical models to beat the consensus may reflect several factors, one of which is the revisions and re-appraisal of trends inherent in UK GDP statistics. The suggestion is that these changes impact on observed relationships between GDP and indicator variables such as business surveys, which impairs nowcasting performance. Indeed, using a synthetic series based purely on observed preliminary GDP estimates, which introduces stability to the target variable series, the nowcasting accuracy of regressions including closely-watched PMI data is improved by 25-40 percentage points relative to a naive benchmark.

AB - Nowcasting UK GDP during the Depression reviews the performance of several statistical techniques in nowcasting preliminary estimates of UK GDP, particularly during the recent depression. Traditional bridging equations, MIDAS regressions and factor models are all considered. While there are various theoretical differences and perceived advantages for each technique, replicated real-time out-ofsample testing shows that, in practice, there is in fact little to choose between methods in terms of end-of-period nowcasting accuracy. The analysis also reveals that none of the aforementioned statistical models can consistently beat a consensus of professional economists in nowcasting preliminary GDP estimates. This inability of statistical models to beat the consensus may reflect several factors, one of which is the revisions and re-appraisal of trends inherent in UK GDP statistics. The suggestion is that these changes impact on observed relationships between GDP and indicator variables such as business surveys, which impairs nowcasting performance. Indeed, using a synthetic series based purely on observed preliminary GDP estimates, which introduces stability to the target variable series, the nowcasting accuracy of regressions including closely-watched PMI data is improved by 25-40 percentage points relative to a naive benchmark.

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Smith P. Nowcasting UK GDP During the Depression. 06 ed. Glasgow: University of Strathclyde. 2016 Apr, p. 1-37.