On non-contemporaneous short-run co-movements

Cubadda, Gianluca; Hecq, Alain

doi:10.1016/s0165-1765(01)00514-6

Cited by 45 publications

(59 citation statements)

References 9 publications

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“…The number of studies of comovements among stationary time series has increased considerably since the 1990s, and the different common features that have been defined and proposed include codependence (Gourieroux et al, 1991) and polynomial serial correlation (Cubadda & Hecq, 2001). Most of these features can be encompassed in the notion of the weak form of polynomial serial correlation proposed by Cubadda (2007).…”

Section: Theoretical Efficiency Estimation Uncertainty and Relevant mentioning

confidence: 99%

Forecasting aggregates and disaggregates with common features

Espasa

Mayo-Burgos

2013

International Journal of Forecasting

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This paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure developed is a disaggregated approach based on single components, which take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, the UK and the US. Our forecasts of the aggregate and other indirect forecasts. This paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure developed is a disaggregated approach based on single-equation models for the take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of s a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, the UK and the US. Our forecasts are significantly more accurate than a direct forecast of the aggregate and other indirect forecasts. Forecasting aggregates and disaggregates with common featuresThis paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure equation models for the take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of s a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, are significantly more accurate than a direct forecast

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Section: Theoretical Efficiency Estimation Uncertainty and Relevant mentioning

confidence: 99%

Forecasting aggregates and disaggregates with common features

Espasa

Mayo-Burgos

2013

International Journal of Forecasting

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“…As shown by Vahid and Engle (1993), the presence of s SCCF's is equivalent to the existence of (n s) common and synchronous cycles 5 .…”

Section: Accepted M Manuscriptmentioning

confidence: 92%

Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling

2008

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“…In order to allow for adjustment delays, Cubadda and Hecq (2001) propose to look at the presence of non-synchronous common cycles in the context of the polynomial serial correlation common feature (PSCCF hereafter) modeling. In this framework there exists a full-rank (N × s) matrix δ 0 such that under the null hypothesis that PSCCF of…”

Section: Synchronous and Non-synchronous Common Cyclesmentioning

confidence: 99%

“…These three clusters will be obtained thanks to a measure of the degree of cyclical commonality among the various economies. In particular, the first group is such that there is a common synchronous cycle among these N 1 time series (Engle and Kozicki, 1993;Vahid and Engle, 1993); the N 2 variables of the second group share a non-synchronous common cycle (Cubadda and Hecq, 2001), and the last group comprises N 3 series with idiosyncratic short-run dynamics. For small dimensional systems, a VAR analysis with additional reduced rank restrictions can be undertaken to discover these groups (Cubadda, 2007b).…”

Section: Introductionmentioning

confidence: 99%

A general to specific approach for constructing composite business cycle indicators

2013

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Combining economic time series with the aim to obtain an indicator for business cycle analyses is an important issue for policy makers. In this area, econometric techniques usually rely on systems with either a small number of series, N, or, at the other extreme, a very large N. In this paper we propose tools to select the relevant business cycle indicators in a "medium" N framework, a situation that is likely to be the most frequent in empirical works. An example is provided by our empirical application, in which we study jointly the short-run co-movements of 24 European countries. We show, under not too restrictive conditions, that parsimonious single-equation models can be used to split a set of N countries in three groups. The first group comprises countries that share a synchronous common cycle, a non-synchronous common cycle is present among the countries of the second group, and the third group collects countries that exhibit idiosyncratic cycles. Moreover, we offer a method for constructing a composite coincident indicator that explicitly takes into account the existence of these various forms of short-run co-movements among variables.

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On non-contemporaneous short-run co-movements

Cited by 45 publications

References 9 publications

Forecasting aggregates and disaggregates with common features

Forecasting aggregates and disaggregates with common features

Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling

A general to specific approach for constructing composite business cycle indicators

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