By Karl Schmedders (editor), Kenneth L. Judd (editor)

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1983. Solution and maximum likelihood estimation of dynamic rational expectations models. Econometrica 51, 1169–1185. , 1986. Efficient solution techniques for dynamic non-linear rational expectations models. Journal of Economic Dynamics and Control 10, 139–145. , 2004. Monte Carlo Methods in Financial Engineering. Springer, New York. , 1977. A bayesian approach to the production of information and learning by doing. Review of Economic Studies 44, 533–547. , 2008. Robustness. Princeton University Press, Princeton, NJ, USA.

Et xT ). (28) Once we have applied these controls, we can go back to the augmented system and compute the expected future state {Et xt+1 , Et xt+2 . } and estimate the parameters θˆt|t when new information on the state xt+1 becomes available. Hence, like with the controls {ut , ut+1 . } the same holds for the estimation of the parameter that is a function of expected future states. θˆt|t = θ (xt , Et xt+1 , . , Et xT ). (29) More detail about the corresponding algorithm is presented in Amman and Kendrick (1999a).

So the DC method proves to be the best of the three when the comparison is done in this way. Then a comparison of the second and third rows in Table 3 to each other and to the first row shows that the percentage of the Monte Carlo runs in which each method had the lowest criterion value was not affected much by the number of outliers that were included. Again the outliers do not seem to affect the relative performance of OF, EOF, and DC. The second way we compared the results was by examining the average criterion value.

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