EUROMIND: A Monthly Indicator of the Euro Area Economic Conditions

Abstract

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.

Sentiment in oil markets

“…The Baltic Dry Index which tracks the cost of shipping goods across the oceans is used as an indicator of global industrial production following [Mitchell et al, 2005], [Frale et al, 2008], [Kilian, 2009], Fan and Xu [2011] and [Coleman, 2012] . A criticism of using the BDI is that it is inuenced by fuel costs, and so is an endogenous variable.…”

“…This is unexpected as the BDI has been used as a proxy for worldwide industrial activity by [Mitchell et al, 2005], [Frale et al, 2008], [Kilian, 2009], Fan andXu [2011], and [Coleman, 2012]. The cost of borrowing as measured by Moody's Aaa corporate bond rate has the expected positive coecient as found by Coleman [2012] indicating that as borrowing becomes more expensive so does oil.…”

Maximum likelihood estimation of time series models: the Kalman filter and beyond

“…Also, temporal aggregation can be converted into a systematic sampling problem an handled by sequential processing; see Harvey and Chung (2000) and Frale et al (2011), among others.…”

Un Gran Var Bayesiano Para La Economia Chilena

“…la otra alternativa consiste y se sustenta en una revisión previa de los datos, esta se basa en una percepción o noción de cuáles pueden llegar a ser los indicadores que más puedan estar relacionados a la variable en que uno está interesado en proyectar. esta última línea de investigación ha sido seguida por echavarría y gonzález (2011) para chile, camacho y pérez-Quiroz (2009) para españa y camacho y pérez-Quiroz (2010) y Frale et al (2008) para la Zona euro, pero la contribución más notable radica en mariano y murasawa (2003), los cuales siguiendo a stock y Watson (1988) realizan una revisión previa de variables que mejor representan la contabilidad nacional, tomando indicadores que de alguna manera capturen los efectos que tienen sobre el ingreso, oferta y demanda.…”