A new multivariate random walk model with slowly changing parameters is introduced and investigated in detail. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases, variances and covariances of the proposed estimators are...

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that...

This paper has a twofold purpose; the first is to present a small macroeconomic model in state space form, the second is to demonstrate that it produces accurate forecasts. The first of these objectives is achieved by fitting two forms of a structural state space macroeconomic model to Australian...

Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data. In addition new multivariate specifications are outlined and...

(Re)insurance companies need to model their liabilities' portfolio to compute the risk-adjusted capital (RAC) needed to support their business. The RAC depends on both the distribution and the dependence functions that are applied among the risks in a portfolio. We investigate the impact of...

Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the...

When dealing with nonlinear econometric models, resort is often made to simulation techniques for the investigation of their dynamic properties. A spectral analysis using stochastic and analytic simulation is carried out on a nonlinear model of the Italian economy. The two approaces are...

This paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIML-estimation in nonlinear econometric models. The performance of different techniques of Hessian approximation in trust-region algorithms is compared regarding their "robustness" against "bad"...

The stochastic simulation of an econometric model is an application of Monte Carlo methods. Deterministic simulation is performed setting error terms to zero. Stochastic simulation, on the contrary, takes into account the disturbance terms, solving the model after adding a vector of...