72,326 research outputs found
Serial correlation in dynamic panel data models with weakly exogenous regressor and fixed effects
Our paper wants to present and compare two estimation methodologies for dynamic panel data models in the presence of serially correlated errors and weakly exogenous regressors. The ¯rst is the ¯rst di®erence GMM estimator as proposed by Arellano and Bond (1991) and the second is the transformed Maximum Likelihood Estimator as proposed by Hsiao, Pesaran, and Tahmiscioglu (2002). Thereby, we consider the ¯xed e®ects case and weakly exogenous regressors. The ¯nite sample properties of both estimation methodologies are analysed within a simulation experiment. Furthermore, we will present an empirical example to consider the performance of both estimators with real data. JEL Classification: C23, J6
Spatial Dependencies in German Matching Functions
This paper proposes a spatial panel model for German matching functions to avoid possibly biased and inefficient estimates due to spatial dependence. We provide empirical evidence for the presence of spatial dependencies in matching data. Based on an official data set containing monthly information for 176 local employment offices, we show that neglecting spatial dependencies in the data results in overestimated coefficients. For the incorporation of spatial information into our model, we use data on commuting relations between local employment offices. Furthermore, our results suggest that a dynamic modeling is more appropriate for matching functions.Empirical Matching, Geographic Labor Mobility, Spatial Dependence, Regional Unemployment
Relation between higher order comoments and dependence structure of equity portfolio
We study a relation between higher order comoments and dependence structure of equity portfolio in the US and UK by relying on a simple portfolio approach where equity portfolios are sorted on the higher order comoments. We find that beta and coskewness are positively related with a copula correlation, whereas cokurtosis is negatively related with it. We also find that beta positively associates with an asymmetric tail dependence whilst coskewness negatively associates with it. Furthermore, two extreme equity portfolios sorted on the higher order comoments are closely correlated and their dependence structure is strongly time varying and nonlinear. Backtesting results of value-at-risk and expected shortfall demonstrate the importance of dynamic modeling of asymmetric tail dependence in the risk management of extreme events
Copulas in finance and insurance
Copulas provide a potential useful modeling tool to represent the dependence structure
among variables and to generate joint distributions by combining given marginal
distributions. Simulations play a relevant role in finance and insurance. They are used to
replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so
on. Using copulas, it is easy to construct and simulate from multivariate distributions based
on almost any choice of marginals and any type of dependence structure. In this paper we
outline recent contributions of statistical modeling using copulas in finance and insurance.
We review issues related to the notion of copulas, copula families, copula-based dynamic and
static dependence structure, copulas and latent factor models and simulation of copulas.
Finally, we outline hot topics in copulas with a special focus on model selection and
goodness-of-fit testing
Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC)
We develop a novel advanced Particle Markov chain Monte Carlo algorithm that
is capable of sampling from the posterior distribution of non-linear state
space models for both the unobserved latent states and the unknown model
parameters. We apply this novel methodology to five population growth models,
including models with strong and weak Allee effects, and test if it can
efficiently sample from the complex likelihood surface that is often associated
with these models. Utilising real and also synthetically generated data sets we
examine the extent to which observation noise and process error may frustrate
efforts to choose between these models. Our novel algorithm involves an
Adaptive Metropolis proposal combined with an SIR Particle MCMC algorithm
(AdPMCMC). We show that the AdPMCMC algorithm samples complex, high-dimensional
spaces efficiently, and is therefore superior to standard Gibbs or Metropolis
Hastings algorithms that are known to converge very slowly when applied to the
non-linear state space ecological models considered in this paper.
Additionally, we show how the AdPMCMC algorithm can be used to recursively
estimate the Bayesian Cram\'er-Rao Lower Bound of Tichavsk\'y (1998). We derive
expressions for these Cram\'er-Rao Bounds and estimate them for the models
considered. Our results demonstrate a number of important features of common
population growth models, most notably their multi-modal posterior surfaces and
dependence between the static and dynamic parameters. We conclude by sampling
from the posterior distribution of each of the models, and use Bayes factors to
highlight how observation noise significantly diminishes our ability to select
among some of the models, particularly those that are designed to reproduce an
Allee effect
A General Framework for Observation Driven Time-Varying Parameter Models
We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity and single source of error models. In addition, the GAS specification gives rise to a wide range of new observation driven models. Examples include non-linear regression models with time-varying parameters, observation driven analogues of unobserved components time series models, multivariate point process models with time-varying parameters and pooling restrictions, new models for time-varying copula functions and models for time-varying higher order moments. We study the properties of GAS models and provide several non-trivial examples of their application.dynamic models, time-varying parameters, non-linearity, exponential family, marked point processes, copulas
Estimating the effect of state dependence in work-related training participation among British employees.
Despite the extensive empirical literature documenting the determinants of training participation and a broad consensus on the influence of previous educational attainment on the training participation decision, there is hardly any reference in the applied literature to the role of past experience of training on future participation. This paper presents evidence on the influence of serial persistence in the work-related training participation decision of British employees. Training participation is modelled as a dynamic random effects probit model and estimated using three different approaches proposed in the literature for tackling the initial conditions problem by Heckman (1981), Wooldrgidge (2005) and Orme (2001). The estimates are then compared with those from a dynamic limited probability model using GMM techniques, namely the estimators proposed by Arellano and Bond (1991) and Blundell and Bond (1998). The results suggest a strong state dependence effect, which is robust across estimation methods, rendering previous experience as an important determining factor in employees’ work-related training decision.state dependence; unobserved heterogeneity; training; dynamic panel data models; generalised method of moments
Improved asymptotic analysis of Gaussian QML estimators in spatial models
This paper presents a fundamentally improved statement on asymptotic behaviour of the well-known Gaussian QML estimator of parameters in high-order mixed regressive/autoregressive spatial model. We generalize the approach previously known in the econometric literature by considerably weakening assumptions on the spatial weight matrix, distribution of the residuals and the parameter space for the spatial autoregressive parameter. As an example application of our new asymptotic analysis we also give a statement on the large sample behaviour of a general fi xed effects design
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