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Likelihood-based Analysis for Dynamic Factor Models
Jungbacker, Borus; Koopman, Siem Jan - 2008
is provided for the analysis of a large panel of macroeconomic time series. …
Persistent link: https://www.econbiz.de/10010325750
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Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time
Mesters, Geert; Koopman, Siem Jan - 2012
panel data model with unobserved random individual-specific and time-varying effects. We propose an estimation procedure … in both dimensions. We evaluate the method in a Monte Carlo study for dynamic panel data models with observations from … economic growth figures of countries listed in the Penn World Tables. It is shown that our dynamic panel data model can provide …
Persistent link: https://www.econbiz.de/10010326209
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Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models
Mesters, Geert; Koopman, Siem Jan; Ooms, Marius - 2011
An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear log-density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian...
Persistent link: https://www.econbiz.de/10010326501
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The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models
Bun, Maurice J. G.; Windmeijer, Frank - 2009
The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences … model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary panel … results are shown to extend to the panel data GMM estimators …
Persistent link: https://www.econbiz.de/10014202992
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Comparative Advantage, the Rank-size Rule, and Zipf's Law
Hinloopen, Jeroen; van Marrewijk, Charles - 2006
Using a comprehensive international trade data set we investigate empirical regularities (known as Zipf’s Law or the rank-size rule) for the distribution of the interaction between countries as measured by revealed comparative advantage. Using the recently developed estimator by Gabaix and...
Persistent link: https://www.econbiz.de/10010325192
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Nonparametric Estimation for Non-Homogeneous Semi-Markov Processes: An Application to Credit Risk
Monteiro, Andre; Smirnov, Georgi V.; Lucas, Andre - 2006
We propose procedures for estimating the time-dependent transition matrices for the general class of finite nonhomogeneous continuous-time semi-Markov processes. We prove the existence and uniqueness of solutions for the system of Volterra integral equations defining the transition matrices,...
Persistent link: https://www.econbiz.de/10010325348
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Nonparametric Estimation for Non-Homogeneous Semi-Markov Processes : An Application to Credit Risk
Lucas, Andre - 2006
We propose procedures for estimating the time-dependent transition matrices for the general class of finite nonhomogeneous continuous-time semi-Markov processes. We prove the existence and uniqueness of solutions for the system of Volterra integral equations defining the transition matrices,...
Persistent link: https://www.econbiz.de/10012733969
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Dynamic Factor Analysis in The Presence of Missing Data
Jungbacker, B.; Koopman, S.J.; van der Wel, M. - 2009
This paper concerns estimating parameters in a high-dimensional dynamic factormodel by the method of maximum likelihood. To accommodate missing data in theanalysis, we propose a new model representation for the dynamic factor model. Itallows the Kalman filter and related smoothing methods to...
Persistent link: https://www.econbiz.de/10010325649
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Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation
Monteiro, André A. - 2008
Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews,...
Persistent link: https://www.econbiz.de/10010325837
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Likelihood Functions for State Space Models with Diffuse Initial Conditions
Francke, Marc K.; Koopman, Siem Jan; de Vos, Aart - 2008
State space models with nonstationary processes and fixed regression effects require a state vector with diffuse initial conditions. Different likelihood functions can be adopted for the estimation of parameters in time series models with diffuse initial conditions. In this paper we consider...
Persistent link: https://www.econbiz.de/10010325962