Speed of Adjustment in Cointegrated Systems

This paper considers the speed of adjustment to long-run equilibria, in the context of cointegrated Vector Autoregressive Processes (VAR). We discuss the definition of multivariate p-lives for any indicator of predictive ability, concentrating on cumulated interim multipliers which converge to impact factor for increasing forecasting horizon. Interim multipliers are related to autoregressive Granger-causality coefficients, structural or generalized cumulative impulse responses. We discuss the relation of the present definition of multivariate p-lives with existing definitions for univariate time series and for nonlinear multivariate stationary processes. For multivariate (possibly cointegrated) VAR systems, p-lives are functions of the dynamics of the system only,and do not depend on the history path on which the forecast is based. Hence one can discuss inference on p-lives as (discrete) functions of parameters in the VAR model. We discuss a likelihood-based approach, both for point estimation and for confidence regions. An illustrative application to adjustment to purchasing-power parity (PPP) is presented.p-life, speed of adjustment, impact factors, vector equilibrium correction, shock absorption

Estimation of extended mixed models using latent classes and latent processes: the R package lcmm

The R package lcmm provides a series of functions to estimate statistical models based on linear mixed model theory. It includes the estimation of mixed models and latent class mixed models for Gaussian longitudinal outcomes (hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear multivariate outcomes (multlcmm), as well as joint latent class mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a time-to-event that can be possibly left-truncated right-censored and defined in a competing setting. Maximum likelihood esimators are obtained using a modified Marquardt algorithm with strict convergence criteria based on the parameters and likelihood stability, and on the negativity of the second derivatives. The package also provides various post-fit functions including goodness-of-fit analyses, classification, plots, predicted trajectories, individual dynamic prediction of the event and predictive accuracy assessment. This paper constitutes a companion paper to the package by introducing each family of models, the estimation technique, some implementation details and giving examples through a dataset on cognitive aging

Copula-based dynamic conditional correlation multiplicative error processes : [Version 18 April 2013]

We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks