Ensemble Kalman Filters (EnKF) have been shown to be more accurate than 3D-Var in data assimilation simulations under the assumption of a perfect model. However, in reality, the forecast model has deficiencies and does not represent the atmospheric behavior precisely due to lack of resolution, approximate parameterizations of subgrid scale physical processes, and numerical dispersion. For assim...

The formulation of the National Centers for Environmental Prediction four-dimensional variational dataassimilation (4D-Var) system is described. Results of applying 4D-Var over a one-week assimilation period, with a full set of physical parametrizations, are presented and compared with those of 3D-Var. The linearization has been performed without simplifications and, therefore, the tangent-line...

This paper investigates limit theory for the likelihood analysis of an I(2) cointegrated vector autoregressive (VAR) model in the presence of deterministic shifts. A log likelihood ratio (logLR) test statistic for integration indices is considered, and it is demonstrated that the asymptotic distribution of the statistic is given in the form of a generalised Dicky-Fuller type distribution. A log...

6 Data assimilation obtains improved estimates of the state of a physical system by com7 bining imperfect model results with sparse and noisy observations of reality. In the four 8 dimensional variational (4D-Var) framework data assimilation is formulated as an opti9 mization problem, which is solved using gradient based optimization methods.The 4D10 Var gradient is obtained by forcing the adjo...

In this paper, we investigate cross-correlations between nonferrousmetal spot and futures markets using detrended cross-correlation analysis (DCCA). We find the existence of significant cross-correlations for both return and volatility series. The DCCA-based crosscorrelation coefficients are very high and decrease with the futures maturity increases. Using the multifractal extension of DCCA, th...

We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz (2014) (DYCI). We show that the connectedness index from the TVP-VAR model captures abrupt turning points better than the one obtained...

VAR analysis is a widespread method of quantitatively analyzing macro-economic issues. In this paper we examine the use of "hybrid" VAR models that retain the short-run features of a VAR but are designed to reproduce selected characteristics of calibrated models that are frequently used for the simulation of policy actions. The calibrated model we use is the McKibbin Sachs Global (MSG2) model o...

VAR models [13] are a type of multi-equation model that linearly describe the simultaneous interactions and behaviour among a group of variables using only their own past. More specifically, a VAR is a model of simultaneous equations formed by a system of equations in which the contemporary values of model variables do not appear in any explanatory variable in the equations. The set of explanat...

This chapter reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: 1) from DSGE to statespace model; 2) from state-space model to VAR(1); 3) from VAR(1) to nite order VAR. The focus is on discussing what can go wrong at each step of th...

Four-dimensional variational data assimilation (4D-Var) combines the information from a time-sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrieva...