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96
Implications of dynamic factor models for VAR analysis
 NBER, WORKING PAPER
, 2005
"... This paper considers VAR models incorporating many time series that interact through a few dynamic factors. Several econometric issues are addressed including estimation of the number of dynamic factors and tests for the factor restrictions imposed on the VAR. Structural VAR identification based on ..."
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Cited by 162 (5 self)
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This paper considers VAR models incorporating many time series that interact through a few dynamic factors. Several econometric issues are addressed including estimation of the number of dynamic factors and tests for the factor restrictions imposed on the VAR. Structural VAR identification based on timing restrictions, long run restrictions, and restrictions on factor loadings are discussed and practical computational methods suggested. Empirical analysis using U.S. data suggest several (7) dynamic factors, rejection of the exact dynamic factor model but support for an approximate factor model, and sensible results for a SVAR that identifies money policy shocks using timing restrictions.
Integer Factorization
, 2005
"... Many public key cryptosystems depend on the difficulty of factoring large integers. This thesis serves as a source for the history and development of integer factorization algorithms through time from trial division to the number field sieve. It is the first description of the number field sieve fro ..."
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Cited by 123 (8 self)
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Many public key cryptosystems depend on the difficulty of factoring large integers. This thesis serves as a source for the history and development of integer factorization algorithms through time from trial division to the number field sieve. It is the first description of the number field sieve from an algorithmic point of view making it available to computer scientists for implementation. I have implemented the general number field sieve from this description and it is made publicly available from the Internet. This means that a reference implementation is made available for future developers which also can be used as a framework where some of the sub
Likelihoodbased analysis for dynamic factor models
, 2008
"... We present new results for the likelihoodbased analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modeled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated ..."
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Cited by 38 (7 self)
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We present new results for the likelihoodbased analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modeled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by (quasi)maximum likelihood. An illustration is provided for the analysis of a large panel of macroeconomic time series
Factor Modeling for HighDimensional Time Series: Inference for the Number of Factors ∗
"... This paper deals with the factor modeling for highdimensional time series based on a dimensionreduction viewpoint. Under stationary settings, the inference is simple in the sense that both the number of factors and the factor loadings are estimated in terms of an eigenanalysis for a nonnegative d ..."
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Cited by 18 (2 self)
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This paper deals with the factor modeling for highdimensional time series based on a dimensionreduction viewpoint. Under stationary settings, the inference is simple in the sense that both the number of factors and the factor loadings are estimated in terms of an eigenanalysis for a nonnegative definite matrix, and is therefore applicable when the dimension of time series is in the order of a few thousands. Asymptotic properties of the proposed method are investigated under two settings: (i) the sample size goes to infinity while the dimension of time series is fixed; and (ii) both the sample size and the dimension of time series go to infinity together. In particular, our estimators for zeroeigenvalues enjoy faster convergence (or slower divergence) rates, hence making the estimation for the number of factors easier. In particular when the sample size and the dimension of time series go to infinity together, the estimators for the eigenvalues are no longer consistent. However our estimator for the number of the factors, which is based on the ratios of the estimated eigenvalues, still works fine. Furthermore, this estimation shows the socalled ‘blessing of dimensionality ’ property in the sense that the performance of the estimation may improve when the dimension of time series increases. A twostep procedure is investigated when the factors are of different degrees of strength. Numerical illustration with both simulated and real data is also reported. Key words and phrases. Autocovariance matrices, blessing of dimensionality, eigenanalysis, fast convergence rates, multivariate time series, ratiobased estimator, strength of factors, white noise.
Is a DFM WellSuited in Forecasting Regional House Price Inflation?” Working paper No
, 2008
"... 1 Is a DFM WellSuited for Forecasting Regional House Price Inflation? ..."
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Cited by 11 (10 self)
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1 Is a DFM WellSuited for Forecasting Regional House Price Inflation?
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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Cited by 9 (1 self)
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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.
Are disaggregate data useful for factor analysis in forecasting French GDP?
, 2008
"... This paper compares the forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting ..."
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Cited by 8 (0 self)
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This paper compares the forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for the GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with two disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice.
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