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162
Forecasting with Factoraugmented Error Correction Models
, 2009
"... As a generalization of the factoraugmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2008) introduced the Factoraugmented Error Correction Model (FECM). The FECM combines errorcorrection, cointegration and dynamic factor models, and has several conceptual advant ..."
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As a generalization of the factoraugmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2008) introduced the Factoraugmented Error Correction Model (FECM). The FECM combines errorcorrection, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the longrun information lacking from the FAVAR because of the latter’s speci…cation in di¤erences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally o¤ers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
The Generalized Dynamic Factor Model
 Identication and Estimation”, The Review of Economics and Statistics
, 2000
"... Abstract. In the present paper we study a semiparametric version of the Generalized Dynamic Factor Model introduced in Forni, Hallin, Lippi and Reichlin (2000). Precisely, we suppose that the common components have rational spectral density, while no parametric structure is assumed for the idiosyncr ..."
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Abstract. In the present paper we study a semiparametric version of the Generalized Dynamic Factor Model introduced in Forni, Hallin, Lippi and Reichlin (2000). Precisely, we suppose that the common components have rational spectral density, while no parametric structure is assumed for the idiosyncratic components. The parametric structure assumed for the common components does not imply that the model has a static representation (though the converse implication holds), a strong restriction which is shared by most of the literature on largedimensional dynamic factor models. We use recent results on singular stationary processes with rational spectral density, to obtain a finite autoregressive representation for the common components. We construct an estimator for the model parameters and the common shocks. Consistency and rates of convergence are obtained. An empirical section, based on US macroeconomic time series, compares estimates based on our model with those based on the usual staticrepresentation restriction. We find convincing evidence that the latter is not supported by the data. JEL subject classification: C0, C01, E0.
2008b Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management. Ann. of applied stat
"... We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staff ..."
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We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analysis of Poisson variables, followed by time series modeling of factor score series. Time series forecasts of factor scores are combined with factor loadings to yield forecasts of future Poisson rate profiles. Penalized Poisson regressions on factor loadings guided by time series forecasts of factor scores are used to generate dynamic withinprocess rate updating. Methods are also developed to obtain distributional forecasts. Our methods are illustrated using simulation and real data. The empirical results demonstrate how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing. 1. Introduction. Queueing
Large dimension forecasting models and random singular value spectra
 European Physical Journal B
"... We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where ..."
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Cited by 11 (5 self)
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We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where singular values are expected in the absence of any true correlations between the variables under study. Our result can be seen as the natural generalization of the MarčenkoPastur distribution for the case of rectangular correlation matrices. We illustrate the interest of our method on a set of macroeconomic time series. 1
2009): “Business Cycle Analysis and VARMA Models
 Journal of Economic Dynamics & Control
"... Working papers from 1999 onwards are available as pdffiles on the bank’s ..."
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Cited by 10 (0 self)
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Working papers from 1999 onwards are available as pdffiles on the bank’s
The Great Recession: US dynamics and spillovers to the world economy
, 2010
"... The paper aims at assessing the mechanics of the Great Recession, considering both its domestic propagation within the US, as well as its spillovers to advanced and emerging economies. A total of 50 countries has been investigated by means of a largescale open economy macroeconometric model, provid ..."
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Cited by 9 (5 self)
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The paper aims at assessing the mechanics of the Great Recession, considering both its domestic propagation within the US, as well as its spillovers to advanced and emerging economies. A total of 50 countries has been investigated by means of a largescale open economy macroeconometric model, providing an accurate assessment of the international macro/finance interface over the whole 19802009 period. It is found that a boombust credit cycle interpretation of the crisis is consistent with the empirical evidence. Moreover, concerning the real effects of the crisis within the US, stronger evidence of an asset prices channel, rather than a liquidity channel, has been detected. The results also support the effectiveness of the expansionary fiscal/monetary policy mix implemented by the Fed and the US government. Concerning the spillovers to the world economy, it is
InfiniteDimensional VAR and Factor Models
, 2008
"... This paper introduces a novel approach for dealing with the ‘curse of dimensionality ’in the case of large linear dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the number of endogenous variables tends to infinity. It is show ..."
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Cited by 9 (1 self)
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This paper introduces a novel approach for dealing with the ‘curse of dimensionality ’in the case of large linear dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinitedimensional VAR (or IVAR) can be arbitrarily well characterized by a large number of finitedimensional models in the spirit of the global VAR model proposed in Pesaran et al. (JBES, 2004). The paper also considers IVAR models with dominant individual units and shows that this will lead to a dynamic factor model with the dominant unit acting as the factor. The problems of estimation and inference in a stationary IVAR with unknown number of unobserved common factors are also investigated. A cross section augmented least squares estimator is proposed and its asymptotic distribution is derived. Satisfactory small sample properties are documented by Monte Carlo experiments.
Fiscal foresight and the effect of government spending,” UAB manuscript
, 2010
"... We study the effects of government spending by using a structural, large dimensional, dynamic factor model. We find that the government spending shock is nonfundamental for the variables commonly used in the structural VAR literature, so that its impulse response functions cannot be consistently es ..."
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We study the effects of government spending by using a structural, large dimensional, dynamic factor model. We find that the government spending shock is nonfundamental for the variables commonly used in the structural VAR literature, so that its impulse response functions cannot be consistently estimated by means of a VAR. Government spending raises both consumption and investment, with no evidence of crowding out. The impact multiplier is 1.7 and the long run multiplier is 0.6. JEL classification: C32, E32, E62.
HOW IMPORTANT ARE COMMON FACTORS IN DRIVING NONFUEL COMMODITY PRICES? A DYNAMIC FACTOR ANALYSIS 1
, 1072
"... In 2009 all ECB publications feature a motif taken from the €200 banknote. This paper can be downloaded without charge from ..."
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Cited by 8 (0 self)
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In 2009 all ECB publications feature a motif taken from the €200 banknote. This paper can be downloaded without charge from
2008) FactorAugmented Error Correction Models
 CEPR Discussion Paper Series, No 6707
"... This paper brings together several important strands of the econometrics literature: errorcorrection, cointegration and dynamic factor models. It introduces the Factoraugmented Error Correction Model (FECM), where the factors estimated from a large set of variables in levels are jointly modelled w ..."
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Cited by 8 (1 self)
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This paper brings together several important strands of the econometrics literature: errorcorrection, cointegration and dynamic factor models. It introduces the Factoraugmented Error Correction Model (FECM), where the factors estimated from a large set of variables in levels are jointly modelled with a few key economic variables of interest. With respect to the standard ECM, the FECM protects, at least in part, from omitted variable bias and the dependence of cointegration analysis on the specific limited set of variables under analysis. It may also be in some cases a refinement of the standard Dynamic Factor Model since it allows us to include the error correction terms into the equations, and by allowing for cointegration prevent the errors from being noninvertible moving average processes. In addition, the FECM is a natural generalization of factor augmented VARs (FAVAR) considered by Bernanke, Boivin and Eliasz (2005) inter alia, which are specified in first differences and are therefore misspecified in the presence of cointegration. The FECM has a vast range of applicability. A set of Monte Carlo experiments and two detailed empirical examples highlight its merits in finite samples relative to standard ECM and FAVAR models. The analysis is conducted primarily within an insample framework, although the outofsample implications are also explored.