The Experts below are selected from a list of 191904 Experts worldwide ranked by ideXlab platform
Siem Jan Koopman - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data
SSRN Electronic Journal, 2020Co-Authors: Francisco Blasques, Meindert Heres Hoogerkamp, Siem Jan Koopman, Ilka Van De WerveAbstract:We propose a Dynamic Factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the Factor loadings associated with the Dynamic macroeconomic Factor, we can measure to what extent the different types of education exhibit similarities in their relationship with macroeconomic cycles. Since unemployment data is available for a longer time period than our detailed education data panel, we propose a twostep estimation procedure. First, we consider a score-driven model which filters the conditional expectation of the unemployment rate. Second, we consider a multivariate regression model for the number of students featuring the Dynamic macroeconomic Factor as a regressor, and we further apply the k-means method to estimate the clustered loading matrix. In a Monte Carlo study we analyze the performance of the proposed procedure in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high-dimensional, nation-wide data set from The Netherlands, we empirically investigate the impact of the rate of unemployment on choices in education over time. Our analysis confirms that the number of students in part-time education covaries more strongly with unemployment than those in full-time education.
-
empirical bayes methods for Dynamic Factor models
The Review of Economics and Statistics, 2017Co-Authors: Siem Jan Koopman, Geert MestersAbstract:We consider the Dynamic Factor model where the loading matrix, the Dynamic Factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and Factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.
-
observation driven mixed measurement Dynamic Factor models with an application to credit risk
The Review of Economics and Statistics, 2014Co-Authors: Drew Creal, Siem Jan Koopman, Bernd Schwaab, Andre LucasAbstract:Abstract We propose an observation-driven Dynamic Factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common Dynamics and cross-sectional dependence due to shared Dynamic latent Factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 to March 2010.
-
forecasting macroeconomic variables using collapsed Dynamic Factor analysis
International Journal of Forecasting, 2014Co-Authors: Falk Brauning, Siem Jan KoopmanAbstract:Abstract We explore a new approach to the forecasting of macroeconomic variables based on a Dynamic Factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a Dynamic Factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our Dynamic Factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.
-
observation driven mixed measurement Dynamic Factor models with an application to credit risk
Social Science Research Network, 2013Co-Authors: Drew Creal, Siem Jan Koopman, Bernd Schwaab, Andre LucasAbstract:We propose a Dynamic Factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common Dynamics and cross-sectional dependence due to shared exposure to Dynamic latent Factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obtained by means of simulation, thus enabling straightforward parameter estimation by standard maximum likelihood. We use the new mixed-measurement framework for the signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody’s-rated firms from January 1982 until March 2010. Our joint modelling framework allows us to construct predictive (conditional) loss densities for portfolios of corporate bonds in the presence of different sources of credit risk such as frailty effects and systematic recovery risk.
Mark W Watson - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Factor models Factor augmented vector autoregressions and structural vector autoregressions in macroeconomics
Handbook of Macroeconomics, 2016Co-Authors: James H Stock, Mark W WatsonAbstract:This chapter provides an overview of and user's guide to Dynamic Factor models (DFMs), their estimation, and their uses in empirical macroeconomics. It also surveys recent developments in methods for identifying and estimating SVARs, an area that has seen important developments over the past 15 years. The chapter begins by introducing DFMs and the associated statistical tools, both parametric (state-space forms) and nonparametric (principal components and related methods). After reviewing two mature applications of DFMs, forecasting and macroeconomic monitoring, the chapter lays out the use of DFMs for analysis of structural shocks, a special case of which is Factor-augmented vector autoregressions (FAVARs). A main focus of the chapter is how to extend methods for identifying shocks in structural vector autoregression (SVAR) to structural DFMs. The chapter provides a unification of SVARs, FAVARs, and structural DFMs and shows both in theory and through an empirical application to oil shocks how the same identification strategies can be applied to each type of model.
-
chapter 8 Dynamic Factor models Factor augmented vector autoregressions and structural vector autoregressions in macroeconomics
Handbook of Macroeconomics, 2016Co-Authors: James H Stock, Mark W WatsonAbstract:Abstract This chapter provides an overview of and user's guide to Dynamic Factor models (DFMs), their estimation, and their uses in empirical macroeconomics. It also surveys recent developments in methods for identifying and estimating SVARs, an area that has seen important developments over the past 15 years. The chapter begins by introducing DFMs and the associated statistical tools, both parametric (state-space forms) and nonparametric (principal components and related methods). After reviewing two mature applications of DFMs, forecasting and macroeconomic monitoring, the chapter lays out the use of DFMs for analysis of structural shocks, a special case of which is Factor-augmented vector autoregressions (FAVARs). A main focus of the chapter is how to extend methods for identifying shocks in structural vector autoregression (SVAR) to structural DFMs. The chapter provides a unification of SVARs, FAVARs, and structural DFMs and shows both in theory and through an empirical application to oil shocks how the same identification strategies can be applied to each type of model.
-
consistent Factor estimation in Dynamic Factor models with structural instability
Journal of Econometrics, 2013Co-Authors: Brandon J Bates, James H Stock, Mikkel Plagborgmoller, Mark W WatsonAbstract:Abstract This paper considers the estimation of approximate Dynamic Factor models when there is temporal instability in the Factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the Factors is consistent and find that these instabilities can be larger than earlier theoretical calculations suggest. We also discuss implications of our results for the robustness of regressions based on the estimated Factors and of estimates of the number of Factors in the presence of parameter instability. Simulations calibrated to an empirical application indicate that instability in the Factor loadings has a limited impact on estimation of the Factor space and diffusion index forecasting, whereas estimation of the number of Factors is more substantially affected.
-
Dynamic Factor models
Research Papers in Economics, 2011Co-Authors: James H Stock, Mark W WatsonAbstract:This article surveys work on a class of models, Dynamic Factor models (DFMs), that has received considerable attention in the past decade because of their ability to model simultaneously and consistently data sets in which the number of series exceeds the number of time series observations. The aim of this survey is to describe the key theoretical results, applications, and empirical findings in the recent literature on DFMs. The article is organized as follows. The first issue at hand for the econometrician is to estimate the Factors and to ascertain how many Factors there are; these two topics are covered in Sections 2 and 3. Once one has reliable estimates of the Factors, there are a number of things one can do with them beyond using them for forecasting, including using them as instrumental variables, estimating Factor-augmented vector autoregressions, and estimating Dynamic stochastic general equilibrium models; these applications are covered in Section 4. Section 5 discusses some extensions.
-
implications of Dynamic Factor models for var analysis
National Bureau of Economic Research, 2005Co-Authors: James H Stock, Mark W WatsonAbstract: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.
Guangjian Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Factor Analysis Models With Time-Varying Parameters
Multivariate Behavioral Research, 2011Co-Authors: Sy-miin Chow, Jiyun Zu, Kim Shifren, Guangjian ZhangAbstract:Dynamic Factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying Dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a Dynamic Factor model with vector autoregressive relations and time-varying cross-regression parameters at the Factor level. Using techniques drawn from the state-space literature, the model was fitted to a set of daily affect data (over 71 days) from 10 participants who had been diagnosed with Parkinson's disease. Our empirical results lend partial support and some potential refinement to the Dynamic Model of Activation with regard to how the time dependencies between positive and negative affects change over time. A simulation study is conducted to examine the performance of the proposed techniques when (a) changes in the time-varying parameters are represented using the true model...
Mario Forni - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Journal of Econometrics, 2017Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Abstract Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni et al., (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni et al. (2004) . Those estimators, however, rely on Brillinger’s concept of Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard ( static ) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts their generality—prohibiting, for instance, autoregressive Factor loadings. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni et al., (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Research Papers in Economics, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Social Science Research Network, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor spaces one sided representations
Journal of Econometrics, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Abstract Factor model methods recently have become extremely popular in the theory and practice of large panels of time series data. Those methods rely on various Factor models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forniet al. (2000). That paper, however, rests on Brillinger’s Dynamic principal components . The corresponding estimators are two-sided filters whose performance at the end of the observation period or for forecasting purposes is rather poor. No such problem arises with estimators based on standard principal components, which have been dominant in this literature. On the other hand, those estimators require the assumption that the space spanned by the Factors has finite dimension. In the present paper, we argue that such an assumption is extremely restrictive and potentially quite harmful. Elaborating upon recent results by Anderson and Deistler (2008a, b) on singular stationary processes with rational spectrum, we obtain one-sided representations for the GDFM without assuming finite dimension of the Factor space. Construction of the corresponding estimators is also briefly outlined. In a companion paper, we establish consistency and rates for such estimators, and provide Monte Carlo results further motivating our approach.
-
the generalized Dynamic Factor model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized Dynamic Factor model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the Dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on Dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
Marc Hallin - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Journal of Econometrics, 2017Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Abstract Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni et al., (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni et al. (2004) . Those estimators, however, rely on Brillinger’s concept of Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard ( static ) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts their generality—prohibiting, for instance, autoregressive Factor loadings. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni et al., (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Research Papers in Economics, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor space asymptotic analysis
Social Science Research Network, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's Dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the Factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional Factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting.
-
Dynamic Factor models with infinite dimensional Factor spaces one sided representations
Journal of Econometrics, 2015Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Paolo ZaffaroniAbstract:Abstract Factor model methods recently have become extremely popular in the theory and practice of large panels of time series data. Those methods rely on various Factor models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forniet al. (2000). That paper, however, rests on Brillinger’s Dynamic principal components . The corresponding estimators are two-sided filters whose performance at the end of the observation period or for forecasting purposes is rather poor. No such problem arises with estimators based on standard principal components, which have been dominant in this literature. On the other hand, those estimators require the assumption that the space spanned by the Factors has finite dimension. In the present paper, we argue that such an assumption is extremely restrictive and potentially quite harmful. Elaborating upon recent results by Anderson and Deistler (2008a, b) on singular stationary processes with rational spectrum, we obtain one-sided representations for the GDFM without assuming finite dimension of the Factor space. Construction of the corresponding estimators is also briefly outlined. In a companion paper, we establish consistency and rates for such estimators, and provide Monte Carlo results further motivating our approach.
-
generalized Dynamic Factor models and volatilities recovering the market volatility shocks
LSE Research Online Documents on Economics, 2015Co-Authors: Matteo Barigozzi, Marc HallinAbstract:Decomposing volatilities into a common market-driven component and an idiosyncratic itemspecific one is an important issue in financial econometrics. This, however, requires the statistical analysis of large panels of time series, hence faces the usual challenges associated with highdimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the analysis of volatilities. Focusing on the reconstruction of the unobserved market shocks and the way they are loaded by the various items (stocks) in the panel, we propose an entirely non-parametric and model-free two-step general Dynamic Factor approach to the problem, which avoids the usual curse of dimensionality. Applied to the S&P100 asset return dataset, the method provides evidence that a non-negligible proportion of the market-driven volatility of returns originates in the volatilities of the idiosyncratic components of returns.
