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State-space methods permit a flexible treatment of unobserved components models. Furthermore, data irregularities such as missing observations are easily handled. For example, irregularly spaced observations can be dealt with since, as discussed in [3, Chap. 3], unobserved components models can be set up in continuous time, and the implied discrete-time state-space form derived. Current theoretical and empirical research in time series econometrics focuses on non-Gaussian and nonlinear models that follow functional forms suggested by economic and finance theory. For example, many central banks are developing dynamic stochastic general equilibrium models using state-space methods. These models are based on unobserved components, and estimation is by maximum likelihood or Bayesian methods.