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Domenico Giannone

23 March 2006
WORKING PAPER SERIES - No. 595
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Abstract
The paper analyses output dynamics in Euro area countries in the last thirty years. It establishes robust stylized facts on output differentials within the union, on the synchronization of recessions within the area countries and on similarities and differences with respect to the US case. Finally, it provides estimates of changes in the degree of risk sharing since the early nineties. The paper finds that, since 1970, within the Euro area, gaps in levels of income per capita have been persistent. This implies that it is difficult to distinguish convergence patterns from persistent fluctuations around different means. However, these gaps are small and business cycle characteristics, when measured by levels of output, have been very similar across countries. Output variance, the paper finds, is mainly explained by common shocks with similar propagation mechanisms while idiosyncratic shocks, although persistent, are small. These characteristics are in line with what found for US regions. An implication of this result is that policy should be concerned with the common characteristic of the European cycle. The next step of the analysis is then to compare the characteristics of the Euro area cycle with the US cycle. It is found that the two cycles are driven by a common world shock, but that the propagation of the shock differs across the two areas: the Euro Area lags the US and its cycle is more persistent, but less volatile. Low growth, persistence of shocks and low volatility are common characteristics of the Euro area and the gap with respect to the US has been stable over the last thirty years. Facing these historical characteristics, the process of European integration, has however helped to smooth the cross-sectional correlation of consumption conditional on output. This finding supports the hypothesis that, since the early nineties, risk sharing has increased within the Euro Area.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
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
F2 : International Economics→International Factor Movements and International Business
F43 : International Economics→Macroeconomic Aspects of International Trade and Finance→Economic Growth of Open Economies
Network
Proceedings of June 2005 workshop on what effects is EMU having on the euro area and its member countries?
20 April 2006
WORKING PAPER SERIES - No. 605
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Abstract
This paper documents a new stylized fact of the greater macroeconomic stability of the U.S. economy over the last two decades. Using 131 monthly time series, three popular statistical methods and the forecasts of the Federal Reserve's Greenbook and the Survey of Professional Forecasters, we show that the ability to predict several measures of inflation and real activity declined remarkably, relative to naive forecasts, since the mid-1980s. This break down in forecast ability appears to be an inherent feature of the most recent period and thus represents a new challenge for competing explanations of the 'Great Moderation'.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
26 May 2006
WORKING PAPER SERIES - No. 633
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Abstract
This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation while GDP is only affected by real variables and interest rates.
JEL Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
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
26 May 2006
WORKING PAPER SERIES - No. 632
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Abstract
This paper asks two questions. First, can we detect empirically whether the shocks recovered from the estimates of a structural VAR are truly structural Second, can the problem of nonfundamentalness be solved by considering additional information? The answer to the first question is "yes" and that to the second is "under some conditions".
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
E00 : Macroeconomics and Monetary Economics→General→General
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
O3 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights
11 September 2006
WORKING PAPER SERIES - No. 674
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Abstract
This paper considers quasi-maximum likelihood estimations of a dynamic approximate factor model when the panel of time series is large. Maximum likelihood is analyzed under different sources of misspecification: omitted serial correlation of the observations and cross-sectional correlation of the idiosyncratic components. It is shown that the effects of misspecification on the estimation of the common factors is negligible for large sample size (T) and the cross sectional dimension (n). The estimator is feasible when n is large and easily implementable using the Kalman smoother and the EM algorithm as in traditional factor analysis. Simulation results illustrate what are the empirical conditions in which we can expect improvement with respect to simple principle components considered by Bai (2003), Bai and Ng (2002), Forni, Hallin, Lippi, and Reichlin (2000, 2005b), Stock and Watson (2002a,b).
JEL Code
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
13 October 2006
WORKING PAPER SERIES - No. 680
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Abstract
This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. As in Stock and Watson (2002), we find that effciency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts. In contrast to Boivin and Ng (2005), we show that the dynamic restrictions imposed by the procedure of Forni, Hallin, Lippi, and Reichlin (2005) are not harmful for predictability. Our main conclusion is that for the dataset at hand the two methods have a similar performance and produce highly collinear forecasts.
JEL Code
C31 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Social Interaction Models
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
20 December 2006
WORKING PAPER SERIES - No. 700
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Abstract
This paper considers Bayesian regression with normal and double exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
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
16 January 2007
WORKING PAPER SERIES - No. 712
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Abstract
This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse response functions. In particular, we argue that, if the data follow an approximate factor structure, the “problem of fundamentalness”, which is intractable in structural VARs, can be solved provided that the impulse responses are sufficiently heterogeneous. Finally, we propose a consistent method (and n, T rates of convergence) to estimate the impulse-response functions, as well as a bootstrapping procedure for statistical inference.
JEL Code
E0 : Macroeconomics and Monetary Economics→General
C1 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General
23 February 2008
WORKING PAPER SERIES - No. 873
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Abstract
This paper shows that general equilibrium effects can partly rationalize the high correlation between saving and investment rates observed in OECD countries. We find that once controlling for general equilibrium effects the saving-retention coefficient remains high in the 70’s but decreases considerably since the 80’s, consistently with the increased capital mobility in OECD countries.
JEL Code
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
F32 : International Economics→International Finance→Current Account Adjustment, Short-Term Capital Movements
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
23 February 2008
WORKING PAPER SERIES - No. 865
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Abstract
This paper shows that the explanation of the decline in the volatility of GDP growth since the mid-eighties is not the decline in the volatility of exogenous shocks but rather a change in their propagation mechanism.
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
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
30 September 2008
WORKING PAPER SERIES - No. 936
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Abstract
We consider the problem of portfolio selection within the classical Markowitz meanvariance optimizing framework, which has served as the basis for modern portfolio theory for more than 50 years. Efforts to translate this theoretical foundation into a viable portfolio construction algorithm have been plagued by technical difficulties stemming from the instability of the original optimization problem with respect to the available data. Often, instabilities of this type disappear when a regularizing constraint or penalty term is incorporated in the optimization procedure. This approach seems not to have been used in portfolio design until very recently. To provide such a stabilization, we propose to add to the Markowitz objective function a penalty which is proportional to the sum of the absolute values of the portfolio weights. This penalty stabilizes the optimization problem, automatically encourages sparse portfolios, and facilitates an effective treatment of transaction costs. We implement our methodology using as our securities two sets of portfolios constructed by Fama and French: the 48 industry portfolios and 100 portfolios formed on size and book-to-market. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve portfolio comprising equal investments in each available asset. In addition to their excellent performance, these portfolios have only a small number of active positions, a desirable feature for small investors, for whom the fixed overhead portion of the transaction cost is not negligible.
JEL Code
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
C00 : Mathematical and Quantitative Methods→General→General
28 October 2008
WORKING PAPER SERIES - No. 949
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Abstract
Global financial integration unlocks a huge potential for international risk sharing. We examine the degree to which international equity holdings act as a risk sharing device in industrial and emerging economies. We split equity returns into investment income (dividend distribution) and capital gains to investigate which of the two channels delivers the largest potential for risk sharing. Our evidence suggests that net capital gains are a more potent channel of risk sharing. They behave in a countercyclical way, that is they tend to be positive (negative) when the domestic economy is growing more slowly (rapidly) than the rest of the world. Countries with more countercyclical net capital gains experience improved consumption risk sharing. The empirical analysis furthermore suggests that these risk sharing properties of net capital gains have increased through time, in particular in the 1990s and early-2000s, on the back of a declining equity home bias and financial market deepening.
JEL Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
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
14 November 2008
WORKING PAPER SERIES - No. 966
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Abstract
This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
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
19 February 2009
WORKING PAPER SERIES - No. 1010
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Abstract
This paper shows that the EMU has not affected historical characteristics of member countries' business cycles and their cross-correlations. Member countries which had similar levels of GDP per-capita in the seventies have also experienced similar business cycles since then and no significant change associated with the EMU can be detected. For the other countries, volatility has been historically higher and this has not changed in the last ten years. We also find that the aggregate euro area per-capita GDP growth since 1999 has been lower than what could have been predicted on the basis of historical experience and US observed developments. The gap between US and euro area GDP per capita level has been 30% on average since 1970 and there is no sign of catching up or of further widening.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
C5 : Mathematical and Quantitative Methods→Econometric Modeling
F2 : International Economics→International Factor Movements and International Business
F43 : International Economics→Macroeconomic Aspects of International Trade and Finance→Economic Growth of Open Economies
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
14 April 2010
WORKING PAPER SERIES - No. 1167
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Abstract
The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the na
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
8 December 2010
WORKING PAPER SERIES - No. 1275
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Abstract
We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long delays. Until recently, nowcasting had received very little attention by the academic literature, although it was routinely conducted in policy institutions either through a judgemental process or on the basis of simple models. We argue that the nowcasting process goes beyond the simple production of an early estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable. We design a statistical model which produces a sequence of nowcasts in relation to the real time releases of various economic data. The methodology allows to process a large amount of information, as it is traditionally done by practitioners using judgement, but it does it in a fully automatic way. In particular, it provides an explicit link between the news in consecutive data releases and the resulting forecast revisions. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008.
JEL Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
14 January 2011
WORKING PAPER SERIES - No. 1290
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Abstract
Standard accounts of the Great Depression attribute an important causal role to monetary policy errors in accounting for the catastrophic collapse in economic activity observed in the early 1930s. While views vary on the relative importance of money versus credit contraction in the propagation of this policy error to the wider economy and ultimately price developments, a broad consensus exists in the economics profession around the view that the collapse in financial intermediation was a crucial intermediary step. What lessons have monetary policy makers taken from this episode? And how have they informed the conduct of monetary policy by leading central banks in recent times? This paper sets out to address these questions, in the context of the financial crisis of 2008-09 and with application to the euro area. It concludes that the Eurosystem’s non-standard monetary policy measures have supported monetary policy transmission and avoided the calamity of the 1930s.
JEL Code
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E4 : Macroeconomics and Monetary Economics→Money and Interest Rates
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
16 November 2012
WORKING PAPER SERIES - No. 1494
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Abstract
Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious na
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
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
21 November 2012
WORKING PAPER SERIES - No. 1496
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Abstract
We analyse the impact on the euro area economy of the ECB’s non-standard monetary policy measures by studying the effect of the expansion of intermediation of interbank transactions across the central bank balance sheet. We exploit data drawn from the aggregated Monetary and Financial Institutions (MFI) balance sheet, which allows us to construct a measure of the ‘policy shock’ represented by the ECB’s increasing role as a financial intermediary. We find small but significant effects both on loans and real economic activity.
JEL Code
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
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
5 August 2014
WORKING PAPER SERIES - No. 1707
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Abstract
This study evaluates the macroeconomic effects of Outright Monetary Transaction (OMT) announcements by the European Central Bank (ECB). Using high-frequency data, we find that OMT announcements decreased the Italian and Spanish 2-year government bond yields by about 2 percentage points, while leaving unchanged the bond yields of the same maturity in Germany and France. These results are used to calibrate a scenario in a multi-country model describing the macro-financial linkages in France, Germany, Italy, and Spain. The scenario analysis suggests that the reduction in bond yields due to OMT announcements is associated with a significant increase in real activity, credit, and prices in Italy and Spain with relatively muted spillovers in France and Germany.
JEL Code
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
12 September 2014
WORKING PAPER SERIES - No. 1733
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Abstract
This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. Both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
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
29 August 2016
WORKING PAPER SERIES - No. 1951
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Abstract
We assess professional forecasters
JEL Code
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes
16 November 2017
WORKING PAPER SERIES - No. 2112
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Abstract
In this paper we extract latent factors from a large cross-section of commodity prices, including fuel and non-fuel commodities. We decompose each commodity price series into a global (or common) component, block-specific components and a purely idiosyncratic shock. We find that the bulk of the fluctuations in commodity prices is well summarised by a single global factor. This global factor is closely related to fluctuations in global economic activity and its importance in explaining commodity price variations has increased since the 2000s, especially for oil prices.
JEL Code
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
Q02 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→General→Global Commodity Markets
27 February 2018
WORKING PAPER SERIES - No. 2132
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Abstract
We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
16 October 2018
RESEARCH BULLETIN - No. 51
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Abstract
There is a strong co-movement in the prices of international commodities. This is explained by a single common factor that is closely related to fluctuations in global economic activity. The common factor, which is indicative of global demand pressures, explains a large share of commodity price fluctuations, and its importance has increased since the early 2000s, especially for oil and metal prices.
JEL Code
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
Q02 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→General→Global Commodity Markets
25 January 2019
WORKING PAPER SERIES - No. 2226
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Abstract
This paper studies the relationship between the business cycle and financial intermediation in the euro area. We establish stylized facts and study their stability during the global financial crisis and the European sovereign debt crisis. Long-term interest rates have been exceptionally high and long-term loans and deposits exceptionally low since the Lehman collapse. Instead, short-term interest rates and short-term loans and deposits did not show abnormal dynamics in the course of the financial and sovereign debt crisis.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
12 August 2020
WORKING PAPER SERIES - No. 2453
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Abstract
Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three \Vs": the large number of time series continuously released (Volume), the complexity of the data covering various sectors of the economy, published in an asynchronous way and with different frequencies and precision (Variety), and the need to incorporate new information within minutes of their release (Velocity). In this paper, we explore alternative routes to bring Bayesian Vector Autoregressive (BVAR) models up to these challenges. We find that BVARs are able to effectively handle the three Vs and produce, in real time, accurate probabilistic predictions of US economic activity and, in addition, a meaningful narrative by means of scenario analysis.
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
27 April 2021
WORKING PAPER SERIES - No. 2542
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Abstract
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models that often include many predictors.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?