Browsing by Subject "Kalman filtering."

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Open Access
GDP nowcasting using high frequency asset price, commodity price and banking data
(2011) Balkan, Binnur
Knowing the current state of the economy is important especially when we consider that GDP information comes with a lag of quarter. From this perspective, employing high frequency variables in GDP nowcasting may contribute to our knowledge of economic conditions, since they are timelier compared to GDP. This paper deals with nowcasting US GDP using an expectation maximization algorithm in a Kalman Ölter estimation, which includes asset prices, commodity prices and banking data as explanatory variables together with real variables and price indices. As a result of the estimations, asset prices and other high frequency variables are found useful in nowcasting US GDP contrary to previous studies. Model predictions beat the traditional methods with the medium size model, which includes Öfteen variables, yielding the best nowcast results. Finally, this paper also proposes a new route for achieving better nowcast results by changing system speciÖcations of the state variables.
Open Access
Robust minimax estimation applied to kalman filtering
(2008) Aybar, Bahadır
Kalman filtering is one of the most essential tools in estimating an unknown state of a dynamic system from measured data, where the measurements and the previous states have a known relation with the present state. It has generally two steps, prediction and update. This filtering method yields the minimum mean-square error when the noise in the system is Gaussian and the best linear estimate when the noise is arbitrary. But, Kalman filtering performance degrades significantly with the model uncertainty in the state dynamics or observations. In this thesis, we consider the problem of estimating an unknown vector x in a statespace model that may be subject to uncertainties. We assume that the model uncertainty has a known bound and we seek a robust linear estimator for x that minimizes the worst case mean-square error across all possible values of x and all possible values of the model matrix. Robust minimax estimation technique is derived and analyzed in this thesis, then applied to the state-space model and simulation results with different noise perturbation models are presented. Also, a radar tracking application assuming a linear state dynamics is also investigated. Modifications to the James-Stein estimator are made according to the scheme we develop in this thesis, so that some of its limitations are dealt with. In our scheme, James-Stein estimation can be applied even if the observation equation is perturbed and the number of observations are less than the number of states, still yielding robust estimations.
Open Access
Spurious regression problem in Kalman Filter estimation of time varying parameter models
(2010) Eroğlu, Burak Alparslan
This thesis provides a simulation based study on Kalman Filter estimation of time varying parameter models when nonstationary series are included in regression equation. In this study, we have performed several simulations in order to present the outcomes and ramifications of Kalman Filter estimation applied to time varying regression models in the presence of random walk series. As a consequence of these simulations, we demonstrate that Kalman Filter estimation cannot prevent the emergence of spurious regression in time varying parameter models. Furthermore, so as to detect the presence of spurious regression, we also propose a new method, which suggests penalizing Kalman Filter recursions with endogenously generated series. These series, which are created endogenously by utilizing Cochrane’s variance ratio statistic, are replaced by state evolution parameter Tt in transition equation of time varying parameter model. Consequently, Penalized Kalman Filter performs well in distinguishing nonsense relation from a true cointegrating regression.