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Michele Modugno

28 April 2009
WORKING PAPER SERIES - No. 1044
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Abstract
This paper investigates whether information from foreign yield curves helps forecast domestic yield curves out-of-sample. A nested methodology to forecast yield curves in domestic and international settings is applied on three major countries (the US, Germany and the UK). This novel methodology is based on dynamic factor models, the EM algorithm and the Kalman filter. The domestic model is compared vis-á-vis an international one, where information from foreign yield curves is allowed to enrich the information set of the domestic yield curve. The results have interesting and original implications. They reveal clear international dependency patterns, strong enough to improve forecasts of Germany and to a lesser extent UK. The US yield curve exhibits a more independent behaviour. In this way, the paper also generalizes anecdotal evidence on international interest rate linkages to the whole yield curve.
JEL Code
F31 : International Economics→International Finance→Foreign Exchange
19 January 2010
WORKING PAPER SERIES - No. 1145
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Abstract
This paper describes how we constructed a real-time database for the euro area covering more than 200 series regularly published in the European Central Bank Monthly Bulletin, as made available ahead of publication to the Governing Council members before their first meeting of the month. We describe the database in details and study the properties of the euro area real-time data flow and data revisions, also providing comparisons with the United States and Japan. We finally illustrate how such revisions can contribute to the uncertainty surrounding key macroeconomic ratios and the NAIRU.
JEL Code
C01 : Mathematical and Quantitative Methods→General→Econometrics
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
20 May 2010
WORKING PAPER SERIES - No. 1189
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Abstract
In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing data. We also extend the model to the case with serially correlated idiosyncratic component. The framework allows to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant e.g. for young economies for which many indicators are compiled only since recently. We also show how to extract a model based news from a statistical data release within our framework and we derive the relationship between the news and the resulting forecast revision. This can be used for interpretation in e.g. nowcasting applications as it allows to determine the sign and size of a news as well as its contribution to the revision, in particular in case of simultaneous data releases. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting and backdating of euro area GDP.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
8 April 2011
WORKING PAPER SERIES - No. 1324
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Abstract
This paper proposes a methodology to nowcast and forecast inflation using data with sampling frequency higher than monthly. The nowcasting literature has been focused on GDP, typically using monthly indicators in order to produce an accurate estimate for the current and next quarter. This paper exploits data with weekly and daily frequency in order to produce more accurate estimates of inflation for the current and followings months. In particular, this paper uses the Weekly Oil Bulletin Price Statistics for the euro area, the Weekly Retail Gasoline and Diesel Prices for the US and daily World Market Prices of Raw Materials. The data are modeled as a trading day frequency factor model with missing observations in a state space representation. For the estimation we adopt the methodology exposed in Banbura and Modugno (2010). In contrast to other existing approaches, the methodology used in this paper has the advantage of modeling all data within a unified single framework that, nevertheless, allows one to produce forecasts of all variables involved. This offers the advantage of disentangling a model-based measure of ”news” from each data release and subsequently to assess its impact on the forecast revision. The paper provides an illustrative example of this procedure. Overall, the results show that these data improve forecast accuracy over models that exploit data available only at monthly frequency for both countries.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
9 July 2013
WORKING PAPER SERIES - No. 1564
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Abstract
The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. In this paper we survey recent developments in economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland).
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C01 : Mathematical and Quantitative Methods→General→Econometrics
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
9 December 2016
WORKING PAPER SERIES - No. 1986
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Abstract
We propose a model for analyzing euro area trade based on the interaction between macroeconomic and trade variables. First, we show that macroeconomic variables are necessary to generate accurate short-term trade forecasts; this result can be explained by the high correlation between trade and macroeconomic variables, with the latter being released in a more timely manner. Second, the model tracks well the dynamics of trade variables conditional on the path of macroeconomic variables during the great recession; this result makes our model a reliable tool for scenario analysis.
JEL Code
F17 : International Economics→Trade→Trade Forecasting and Simulation
F47 : International Economics→Macroeconomic Aspects of International Trade and Finance→Forecasting and Simulation: Models and Applications
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models