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561
The Generalized Dynamic Factor Model: onesided estimation and forecasting
"... This paper proposes a new forecasting method which makes use of information from a large panel of time series. As in Forni, Hallin, Lippi and Reichlin (2000), and in Stock and Watson (2002a,b), the method is based on a dynamic factor model. We argue that our method improves upon a standard principal ..."
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Cited by 102 (7 self)
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This paper proposes a new forecasting method which makes use of information from a large panel of time series. As in Forni, Hallin, Lippi and Reichlin (2000), and in Stock and Watson (2002a,b), the method is based on a dynamic factor model. We argue that our method improves upon a standard principal component predictor in that, first, it fully exploits all the dynamic covariance structure of the panel and, second, it weights the variables according to their estimated signaltonoise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples our forecast outperforms the standard principal components predictor.
Determining the number of primitive shocks in factor models
 Journal of Business and Economic Statistics
, 2007
"... A widely held but untested assumption underlying macroeconomic analysis is that the number of shocks driving economic fluctuations, q, is small. In this article we associate q with the number of dynamic factors in a large panel of data. We propose a methodology to determine q without having to estim ..."
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Cited by 96 (0 self)
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A widely held but untested assumption underlying macroeconomic analysis is that the number of shocks driving economic fluctuations, q, is small. In this article we associate q with the number of dynamic factors in a large panel of data. We propose a methodology to determine q without having to estimate the dynamic factors. We first estimate a VAR in r static factors, where the factors are obtained by applying the method of principal components to a large panel of data, then compute the eigenvalues of the residual covariance or correlation matrix. We then test whether their eigenvalues satisfy an asymptotically shrinking bound that reflects sampling error. We apply the procedure to determine the number of primitive shocks in a large number of macroeconomic time series. An important aspect of the present analysis is to make precise the relationship between the dynamic factors and the static factors, which is a result of independent interest. KEY WORDS: Common shocks; Dynamic factor model; Number of factors; Principal components
Cointegration Vector Estimation by Panel DOLS and LongRun Money Demand
 Oxford Bulletin of Economics and Statistics
, 2003
"... We study the panel dynamic ordinary least square (DOLS) estimator of a homogeneous cointegration vector for a balanced panel of N individuals observed over T time periods. Allowable heterogeneity across individuals include individualspecific time trends, individualspecific fixed effects and times ..."
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Cited by 92 (0 self)
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We study the panel dynamic ordinary least square (DOLS) estimator of a homogeneous cointegration vector for a balanced panel of N individuals observed over T time periods. Allowable heterogeneity across individuals include individualspecific time trends, individualspecific fixed effects and timespecific effects. The estimator is fully parametric, computationally convenient, and more precise than the single equation estimator. For fixed N as T fi 1, the estimator converges to a function of Brownian motions and the Wald statistic for testing a set of s linear constraints has a limiting v2(s) distribution. The estimator also has a Gaussian sequential limit distribution that is obtained first by letting T fi 1 and then letting N fi 1. In a series of MonteCarlo experiments, we find that the asymptotic distribution theory provides a reasonably close approximation to the exact finite sample distribution. We use panel DOLS to estimate coefficients of the longrun money demand function from a panel of 19 countries with annual observations that span from 1957 to 1996. The estimated income elasticity is 1.08 (asymptotic s.e. 0.26) and the estimated interest rate semielasticity is)0.02 (asymptotic s.e. 0.01). *This paper was previously circulated under the title ‘A Computationally Simple Cointegration
New eurocoin: Tracking economic growth in real time
 Review of Economics and Statistics . Forthcoming. Available as CEPR Discussion Paper 5633
, 2009
"... Removal of shortrun dynamics from a time series to isolate the medium to longrun component, can be obtained by a bandpass filter. However, it is well known that bandpass filters, being twosided, perform very poorly at the end of the sample. In this paper we develop a method to obtain smoothing ..."
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Cited by 70 (7 self)
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Removal of shortrun dynamics from a time series to isolate the medium to longrun component, can be obtained by a bandpass filter. However, it is well known that bandpass filters, being twosided, perform very poorly at the end of the sample. In this paper we develop a method to obtain smoothing of a time series by using only contemporaneous values of a large dataset, so that no endofsample deterioration occurs. Our construction is based on a special version of generalized principal components, which is designed to use leading variables in the dataset as proxies for missing future values in the variable of interest. Our method is applied to the construction of New Eurocoin, an indicator of economic activity for the euro area. New Eurocoin is an estimate of the medium to longrun component of the euro area GDP growth, defined by the bandpass filter, which performs equally well within and at the end of the sample. As our dataset is monthly and most of the series are updated with a short delay, we are able to produce a monthly, realtime indicator. Its performance, both as an estimator of the medium to longrun GDP growth and as a predictor of GDP growth is remarkable.
Macro factors in bond risk premia
 Review of Financial Studies
, 2009
"... Are there important cyclical fluctuations in bond market premiums and, if so, with what macroeconomic aggregates do these premiums vary? We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond retur ..."
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Cited by 61 (1 self)
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Are there important cyclical fluctuations in bond market premiums and, if so, with what macroeconomic aggregates do these premiums vary? We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond returns and macroeconomic fundamentals. We find that “real ” and “inflation ” factors have important forecasting power for future excess returns on U.S. government bonds, above and beyond the predictive power contained in forward rates and yield spreads. This behavior is ruled out by commonly employed affine term structure models where the forecastability of bond returns and bond yields is completely summarized by the crosssection of yields or forward rates. An important implication of these findings is that the cyclical behavior of estimated risk premia in both returns and longterm yields depends importantly on whether the information in macroeconomic factors is included in forecasts of excess bond returns. Without the macro factors, risk premia appear virtually acyclical, whereas with the estimated factors risk premia have a marked countercyclical component, consistent with theories that imply investors must be compensated for risks associated with macroeconomic activity. ( JEL E0, E4, G10, G12) 1.
Unit Roots and Cointegration in Panels
, 2007
"... This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the ..."
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Cited by 54 (3 self)
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This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.
How the Subprime Crisis Went Global: Evidence from Bank Credit Default Swap Spreads,” NBER Working Paper No. 14904
, 2009
"... How did the Subprime Crisis, a problem in a small corner of U.S. financial markets, affect the entire global banking system? To shed light on this question we use principal components analysis to identify common factors in the movement of banks ’ credit default swap spreads. We find that fortunes of ..."
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Cited by 54 (4 self)
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How did the Subprime Crisis, a problem in a small corner of U.S. financial markets, affect the entire global banking system? To shed light on this question we use principal components analysis to identify common factors in the movement of banks ’ credit default swap spreads. We find that fortunes of international banks rise and fall together even in normal times along with shortterm global economic prospects. But the importance of common factors rose steadily to exceptional levels from the outbreak of the Subprime Crisis to past the rescue of Bear Stearns, reflecting a diffuse sense that funding and credit risk was increasing. Following the failure of Lehman Brothers, the interdependencies briefly increased to a new high, before they fell back to the preLehman elevated levels – but now they more clearly reflected heightened funding and counterparty risk. After Lehman’s failure, the prospect of global recession became imminent, auguring the further deterioration of banks ’ loan portfolios. At this point the entire global financial system had become infected. 1
Testing Hypotheses About the Number of Factors in Large Factor Models
 Econometrica
"... In this paper we study highdimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k0 factors against the alternative that the number of factors is larger than k0 but no larger than k1> k0. Our test statistic equals maxk0<k≤k1 γk − γk+1 / γ ..."
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Cited by 48 (1 self)
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In this paper we study highdimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k0 factors against the alternative that the number of factors is larger than k0 but no larger than k1> k0. Our test statistic equals maxk0<k≤k1 γk − γk+1 / γk+1 − γk+2, where γi is the ith largest eigenvalue of the smoothed periodogram estimate of the spectral density matrix of data at a prespecified frequency. We describe the asymptotic distribution of the statistic, as the dimensionality and the number of observations rise, as a function of the TracyWidom distribution and tabulate the critical values of the test. As an application, we test different hypotheses about the number of dynamic factors in macroeconomic time series and about the number of dynamic factors driving excess stock returns.
Weak and Strong Cross Section Dependence and Estimation of Large Panels
, 2009
"... This paper introduces the concepts of timespecific weak and strong cross section dependence. A doubleindexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic ..."
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Cited by 44 (22 self)
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This paper introduces the concepts of timespecific weak and strong cross section dependence. A doubleindexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.
Bias in Dynamic Panel Estimation with Fixed Effects, Incidental Trends and Cross Section Dependence
, 2004
"... Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N →∞. The results extend earlier work by Nickell (1981) and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic t ..."
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Cited by 44 (8 self)
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Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N →∞. The results extend earlier work by Nickell (1981) and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic trends, predetermined and exogenous regressors, and errors that may be cross sectionally dependent. The asymptotic bias is found to be so large when incidental linear trends are fitted and the time series sample size is small that it changes the sign of the autoregressive coefficient. Another finding of interest is that, when there is cross section error dependence, the probability limit of the dynamic panel regression estimator is a random variable rather than a constant, which helps to explain the substantial variability observed in dynamic panel estimates when there is cross section dependence even in situations where N is very large. Some proposals for bias correction are suggested and finite sample performance is analyzed in simulations.