Central bank research hub - Papers by Matteo Barigozzi
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Research hub papers by author Matteo BarigozzienCommon Factors, Trends, and Cycles in Large Datasets
https://www.federalreserve.gov/econres/feds/files/2017111pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Matteo Barigozzi and Matteo LucianiCommon Factors, Trends, and Cycles in Large Datasets2017-11-13T00:00:00ZThis paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap.Common Factors, Trends, and Cycles in Large DatasetsFull texthttps://www.federalreserve.gov/econres/feds/files/2017111pap.pdfMatteo BarigozziMatteo LucianiMatteo Barigozzi and Matteo Luciani2017-11-13Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC32C38C55E00Non-Stationary Dynamic Factor Models for Large Datasets
http://www.federalreserve.gov/econresdata/feds/2016/files/2016024pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Matteo Barigozzi, Marco Lippi, and Matteo LucianiNon-Stationary Dynamic Factor Models for Large Datasets2016-03-04T13:37:00ZWe develop the econometric theory for Non-Stationary Dynamic Factor models for large panels of time series, with a particular focus on building estimators of impulse response functions to unexpected macroeconomic shocks. We derive conditions for consistent estimation of the model as both the cross-sectional size, n, and the time dimension, T, go to infinity, and whether or not cointegration is imposed. We also propose a new estimator for the non-stationary common factors, as well as an information criterion to determine the number of common trends. Finally, the numerical properties of our estimator are explored by means of a MonteCarlo exercise and of a real-data application, in which we study the effects of monetary policy and supply shocks on the US economy.Non-Stationary Dynamic Factor Models for Large DatasetsFull texthttp://www.federalreserve.gov/econresdata/feds/2016/files/2016024pap.pdfMatteo BarigozziMatteo LucianiMarco LippiMatteo Barigozzi, Marco Lippi, and Matteo Luciani2016-03Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC0C01E0Dynamic Factor Models, Cointegration, and Error Correction Mechanisms
http://www.federalreserve.gov/econresdata/feds/2016/files/2016018pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Matteo Barigozzi, Marco Lippi, and Matteo LucianiDynamic Factor Models, Cointegration, and Error Correction Mechanisms2016-02-08T13:37:00ZThe paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors F are I(1) and singular, i.e. F has dimension r and is driven by a q-dimensional white noise, the common shocks, with q < r, and (2) the idiosyncratic components are I(1). We show that F is driven by r-c permanent shocks, where c is the cointegration rank of F, and q-(r-c) < c transitory shocks, thus the same result as in the non-singular case for the permanent shocks but not for the transitory shocks. Our main result is obtained by combining the classic Granger Representation Theorem with recent results by Anderson and Deistler on singular stochastic vectors: if (1-L)F is singular and has rational spectral density then, for generic values of the parameters, F has an autoregressive representation with a finite-degree matrix polynomial fulfilling the restrictions of a Vector Error Correction Mechanism with c error terms. This result is the basis for consistent estimation of Non-Stat ionary Dynamic Factor Models. The relationship between cointegration of the factors and cointegration of the observable variables is also discussed.Dynamic Factor Models, Cointegration, and Error Correction MechanismsFull texthttp://www.federalreserve.gov/econresdata/feds/2016/files/2016018pap.pdfMatteo BarigozziMatteo LucianiMarco LippiMatteo Barigozzi, Marco Lippi, and Matteo Luciani2016-03Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC0C01E0Which model to match?
http://www.bde.es/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/12/Fich/dt1229e.pdf
Bank of Spain Working Papers by Matteo Barigozzi, Roxana Halbleib and David VeredasWhich model to match?2012-08-14T16:16:00ZThe asymptotic efficiency of indirect estimation methods, such as the efficient method of moments and indirect inference, depends on the choice of the auxiliary model. To date, this choice has been somewhat ad hoc and based on an educated guess. In this article we introduce a class of information criteria that helps the user to optimize the choice between nested and non-nested auxiliary models. They are the indirect analogues of the widely used Akaike-type criteria. A thorough Monte Carlo study based on two simple and illustrative models shows the usefulness of the criteria.Which model to match?Full texthttp://www.bde.es/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/12/Fich/dt1229e.pdfDavid VeredasRoxana HalbleibMatteo BarigozziMatteo Barigozzi, Roxana Halbleib and David Veredas2012-08Bank of Spain Working PapersC13C52Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1115.pdf
European Central Bank Working papers by Lucia Alessi, Matteo Barigozzi, Marco CapassoEstimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors2009-11-17T17:20:00Z(JEL: C52, C53) We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a multi-step estimation technique which combines asymptotic principal components and multivariate GARCH. We also prove consistency of the estimated conditional covariances. We present simulation results in order to assess the finite sample properties of the estimation technique. Finally, we carry out two empirical applications respectively on macroeconomic series, with a particular focus on different measures of inflation, and on financial asset returns. Our model outperforms the benchmarks in fore-casting the inflation level, its conditional variance and the volatility of returns. Moreover, we are able to predict all the conditional covariances among the observable series.Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factorsECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1115.pdfLucia AlessiMarco CapassoMatteo BarigozziLucia Alessi, Matteo Barigozzi, Marco Capasso2009-11-16European Central Bank Working PapersThe distribution of households consumption-expenditure budget shares
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1061.pdf
European Central Bank Working papers by Matteo Barigozzi, Lucia Alessi, Marco CapassoThe distribution of households consumption-expenditure budget shares2009-06-16T17:36:59Z(JEL: D3, D12, C12.) This paper explores the statistical properties of house-hold consumption-expenditure budget share distributions ¿defined as the share of household total expenditure spent for purchasing a specific category of commodities¿ for a large sample of Italian households in the period 1989-2004. We find that household budget share distributions are fairly stable over time for each specific category, but profoundly heterogeneous across commodity categories. We then derive a para-metric density that is able to satisfactorily characterize household budget share distributions and: (i) is consistent with the observed statistical properties of the underlying levels of household consumption-expenditure distributions; (ii) can accommodate the observed across-category heterogeneity in household budget share distributions. Finally, we taxonomize commodity categories according to the estimated parameters of the proposed density. We show that the resulting classification is consistent with the traditional economic scheme that labels commodities as necessary, luxury or inferior.The distribution of households consumption-expenditure budget sharesECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1061.pdfLucia AlessiMarco CapassoMatteo BarigozziMatteo Barigozzi, Lucia Alessi, Marco Capasso2009-06-16European Central Bank Working PapersC12D12D3