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## Estimating dynamic equilibrium economies: linear versus nonlinear likelihood (2005)

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Venue: | Journal of Applied Econometrics |

Citations: | 42 - 13 self |

### Citations

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Citation Context ...re. 1 It is important to note that we are presenting here only a basic sequential Monte Carlo filter and that the literature has presented several refinements to improve efficiency (see, for example, =-=Kitagawa, 1996-=-; Pitt and Shephard, 1999). The interested reader can find further details, comparison with alternative schemes and a discussion of convergence in Fernández-Villaverde and Rubio-Ramírez (2004). 2.3. T... |

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Citation Context ... parameters to find the posterior. The advent of Markov chain Monte Carlo algorithms has facilitated this task. In addition, we can compare models by likelihood ratios (Vuong, 1989) or Bayes factors (=-=Geweke, 1998-=-) even if the models are misspecified and nonnested. The previous discussion points out the need to evaluate the likelihood function. The task is conceptually simple, but its implementation is more cu... |

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Citation Context ...ches imply that the optimal decision rules are linear in the states of the economy. The resulting linear system of difference equations can be solved with standard methods (see Anderson et al., 1996; =-=Uhlig, 1999-=- for a detailed explanation). For estimation purposes, Sargent emphasized that the resulting system has a linear representation in a state-space form. If, in addition, we assume that the shocks exogen... |

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Citation Context ...ter. The lack of a closed form for the solution of the model complicates the process of finding the likelihood. The literature shows how to write this likelihood analytically only in a few cases (see =-=Rust, 1994-=- for a survey). Outside those, Sargent (1989) proposed an approach that has become popular. Sargent noticed that a standard procedure for solving dynamic models is to linearize them. This can be done ... |

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Citation Context ...in a state-space form. If, in addition, we assume that the shocks exogenously hitting the economy are normal, we can use the Kalman filter to evaluate the likelihood. It has been argued (for example, =-=Kim et al., 2003-=-) that this linear solution is likely to be accurate enough for fitting the model to the data. However, exploiting the linear approximation to the economy can be misleading. For instance, linearizatio... |

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