A comparison of methods for the construction of composite coincident and leading indexes for the UK

Carriero, Andrea; Marcellino, Massimiliano

doi:10.1016/j.ijforecast.2007.01.005

Cited by 19 publications

(13 citation statements)

References 43 publications

(33 reference statements)

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“…The multivariate leading indicator is simply a multivariate single equation regression rather than an NBER style composite index of leading indicators. Carriero and Marcellino (2007) construct indexes using various methods but do not formally test the indexes' usefulness for forecasting. Rather the authors test the performance of various vector autoregression (VAR) models that all only include a traditional NBER type index.…”

Section: Data and Methodologiesmentioning

confidence: 99%

Seven Leading Indexes of New Zealand Employment*

Claus

2010

Economic Record

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This article constructs seven leading indexes of New Zealand employment and assesses their relative usefulness in terms of forecasting quarterly employment growth. Leading indexes have been widely used since their introduction in the late 1930s. One construction method dominated until academic research interest into alternative techniques re‐appeared in the late 1980s. What has been missing so far in the literature is a thorough comparison of old and new techniques in terms of forecasting performance. This article is a step in that direction. The methods covered here reflect varying degrees of technical sophistication, ranging from simple scoring of changes to relying on frequency domain methods to extract dynamic latent factors from a large dataset. The results show that no single index dominates in terms of forecasting employment growth one to four quarters ahead. This suggests that relying on a suite of models may be the optimal forecasting strategy.

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Section: Data and Methodologiesmentioning

confidence: 99%

Seven Leading Indexes of New Zealand Employment*

Claus

2010

Economic Record

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“…In both cases there is a single unobservable force underlying the current status of the economy, but in the former approach this is a continuous variable, whereas in the latter it is a discrete variable that evolves according to a Markov chain. Whereas Markov switching models do not perform particularly well in this context for European countries, probably because of the availability of quite short and noisy time series (see for example Carriero and Marcellino (2007b)), factor models have been more successfully used.…”

Section: Overview Of the Recent Related Literaturementioning

confidence: 99%

“…Examples of factor‐based indicators include Carriero and Marcellino (2007b) for the UK, Charpin (2005) and Altissimo et al. (2001, 2007) for the euro area.…”

Section: Overview Of the Recent Related Literaturementioning

confidence: 99%

EUROMIND: A Monthly Indicator of the Euro Area Economic Conditions

Frale

Marcellino

Mazzi

et al. 2010

Journal of the Royal Statistical Society Series A: Statistics in Society

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Continuous monitoring of the evolution of the economy is fundamental for the decisions of public and private decision makers. The paper proposes EUROMIND, which is a new monthly indicator of the euro area economic conditions, based on tracking real gross domestic product monthly, relying on information provided in the Eurostat Euro-IND database. EURO-MIND has several original economic and statistical features. First, it considers both the output and the expenditure sides of the economy, as it provides a monthly estimate of the value added of the six branches of economic activity and of the main gross domestic product components by type of expenditure (final consumption, gross capital formation and net exports), and combines the estimates with optimal weights reflecting their relative precision. Second, the indicator is based on information at both the monthly and the quarterly level, modelled with a dynamic factor specification cast in state space form. Third, since estimation of the multivariate dynamic factor model with mixed frequency data can be numerically complex, computational efficiency is achieved by implementing univariate filtering and smoothing procedures. Finally, special attention is paid to chain linking and its implications, via a multistep procedure that exploits the additivity of the volume measures expressed at the prices of the previous year.

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“…The forecast accuracy can possibly be improved by incorporating the forecasting aim in the construction of the index. Several model-based approaches are available for this purpose, see Marcellino (2006) for discussion and Carriero and Marcellino (2007) for an empirical comparison. Here we consider an alternative approach, which retains the simplicity of non-model based composite indexes but which takes the…”

Section: Index Construction and Forecastingmentioning

confidence: 99%

Forecasting with Leading Indicators by means of the Principal Covariate Index

Heij¹,

Dijk²,

Groenen³

2011

OECD Journal: Journal of Business Cycle Measurement and Analysis

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. We thank two anonymous referees for useful comments and suggestions and Tatyana Bulavskaya for excellent computational assistance. A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast

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A comparison of methods for the construction of composite coincident and leading indexes for the UK

Cited by 19 publications

References 43 publications

Seven Leading Indexes of New Zealand Employment*

Seven Leading Indexes of New Zealand Employment*

EUROMIND: A Monthly Indicator of the Euro Area Economic Conditions

Forecasting with Leading Indicators by means of the Principal Covariate Index

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