LESSONS LEARNED FROM U.S. GOVERNMENT TRADE CAPACITY BUILDING PROGRAMS

International Relations/Trade,

Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model

This paper introduces and evaluates new models for time series count data. The Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of discreteness, overdispersion (variance greater than the mean) and serial correlation. A fully parametric approach is taken and a marginal distribution for the counts is specified, where conditional on past observations the mean is autoregressive. This enables to attain improved inference on coefficients of exogenous regressors relative to static Poisson regression, which is the main concern of the existing literature, while modelling the serial correlation in a flexible way. A variety of models, based on the double Poisson distribution of Efron (1986) is introduced, which in a first step introduce an additional dispersion parameter and in a second step make this dispersion parameter time-varying. All models are estimated using maximum likelihood which makes the usual tests available. In this framework autocorrelation can be tested with a straightforward likelihood ratio test, whose simplicity is in sharp contrast with test procedures in the latent variable time series count model of Zeger (1988). The models are applied to the time series of monthly polio cases in the U.S between 1970 and 1983 as well as to the daily number of price change durations of :75$on the IBM stock. A .75$ price change duration is defined as the time it takes the stock price to move by at least .75$. The variable of interest is the daily number of such durations, which is a measure of intradaily volatility, since the more volatile the stock price is within a day, the larger the counts will be. The ACP models provide good density forecasts of this measure of volatility.Forecast; volatility; transactions data

Nonequilibrium dynamics in scalar hybrid models

We study by numerical simulations the transition from the metastable "false vacuum" to the broken symmetry phase in the preheating stage after cosmic inflation in a scalar hybrid model. We take quantum fluctuations and their back reaction into account by applying a one-loop bubble-resummation.Comment: 5 pages, 4 figures, contribution to the conference Strong and Electroweak Matter (SEWM2004), Helsinki, Finland, 16-19 June 200

Dynamical density functional theory for the diffusion of injected Brownian particles

While the theory of diffusion of a single Brownian particle in confined geometries is well-established by now, we discuss here the theoretical framework necessary to generalize the theory of diffusion to dense suspensions of strongly interacting Brownian particles. Dynamical density functional theory (DDFT) for classical Brownian particles represents an ideal tool for this purpose. After outlining the basic ingredients to DDFT we show that it can be readily applied to flowing suspensions with time-dependent particle sources. Particle interactions lead to considerable layering in the mean density profiles, a feature that is absent in the trivial case of noninteracting, freely diffusing particles. If the particle injection rate varies periodically in time with a suitable frequency, a resonance in the layering of the mean particle density profile is predicted

Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model

We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model does not suffer from the curse of dimensionality. The canonical vine autoregressive (CAVA) captures asymmetries in the dependence structure. The model is applied to 95 S&P500 stocks. For the marginal distributions, we use non-Gaussian GARCH models, that are designed to capture skewness and kurtosis. By conditioning on the market index and on sector indexes, the dependence structure is much simplified and the model can be considered as a non-linear version of the CAPM or of a market model with sector effects. The model is shown to deliver good forecasts of Value-at-Risk.asymmetric dependence, high dimension, multivariate copula, multivariate GARCH, Value-at-Risk

Liquid pair correlations in four spatial dimensions: Theory versus simulation

Using liquid integral equation theory, we calculate the pair correlations of particles that interact via a smooth repulsive pair potential in d = 4 spatial dimensions. We discuss the performance of different closures for the Ornstein-Zernike equation, by comparing the results to computer simulation data. Our results are of relevance to understand crystal and glass formation in high-dimensional systems

Comovements in Trading activity: A Multivariate Autoregressive Model of Time Series Count Data Using Copulas

This paper introduces the Multivariate Autoregressive Conditional Poisson model to deal with issues of discreteness, overdispersion and both auto- and cross-correlation, arising with multivariate counts. We model counts with a double Poisson and assume that conditionally on past observations the means follow a Vector Autoregression. We resort to copulas to introduce contemporaneous correlation. We advocate the use of our model as a feasible alternative to multivariate duration models and apply it to the study of sector and stock specific news related to the comovements in the number of trades per unit of time of the most important US department stores traded on the New York Stock Exchange. We show that the market leaders inside an specific sector, in terms of more sectorial information conveyed by their trades, are related to their size measured by their market capitalization.Continuousation; Factor model; Market microstructure.