SSRN Author: Grey GordonGrey Gordon SSRN Content
http://www.ssrn.com/author=1186214
http://www.ssrn.com/rss/en-usFri, 05 Jan 2018 01:17:13 GMTeditor@ssrn.com (Editor)Fri, 05 Jan 2018 01:17:13 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: A Practical Approach to Testing Calibration StrategiesA calibration strategy tries to match target moments using a model’s parameters. We propose tests for determining whether this is possible. The tests use moments at random parameter draws to assess whether the target moments are similar to the computed ones (evidence of existence) or appear to be outliers (evidence of non-existence). Our experiments show the tests are eﬀective at detecting both existence and non-existence in a non-linear model. Multiple calibration strategies can be quickly tested using just one set of simulated data. Applying our approach to indirect inference allows for the testing of many auxiliary model speciﬁcations simultaneously. Code is provided.
http://www.ssrn.com/abstract=2843814
http://www.ssrn.com/1656107.htmlThu, 04 Jan 2018 07:58:17 GMTREVISION: A Divide and Conquer Algorithm for Exploiting Policy Function MonotonicityA divide and conquer algorithm for exploiting policy function monotonicity is proposed and analyzed. To solve a discrete problem with n states and n choices, the algorithm requires at most n log2(n) 5n objective function evaluations. In contrast, existing methods for non-concave problems require n^2 evaluations in the worst case. For concave problems, the solution technique can be combined with a method exploiting concavity to reduce evaluations to 14n 2 log2(n). A version of the algorithm exploiting monotonicity in two state variables allows for even more efficient solutions. The algorithm can also be efficiently employed in a common class of problems that do not have monotone policies, including problems with many state and choice variables. In the sovereign default model of Arellano (2008) and the real business cycle model, the algorithm reduces run times by an order of magnitude for moderate grid sizes and orders of magnitude for larger ones. Sufficient conditions for ...
http://www.ssrn.com/abstract=2995636
http://www.ssrn.com/1611568.htmlTue, 25 Jul 2017 03:47:45 GMTREVISION: A Divide and Conquer Algorithm for Exploiting Policy Function MonotonicityA divide-and-conquer algorithm for exploiting policy function monotonicity is proposed and analyzed. To compute a discrete problem with n states and n choices, the algorithm requires at most 5n log2(n)n function evaluations and so is O(n log2 n). In contrast, existing methods for non-concave problems require n^2 evaluations in the worst-case and so are O(n^2). The algorithm holds great promise for discrete choice models where non-concavities naturally arise. In one such example, the sovereign default model of Arellano (2008), the algorithm is six times faster than the best existing method when n=100 and 50 times faster when n=1000. Moreover, if concavity is assumed, the algorithm combined with Heer and Maußner (2005)'s method requires fewer than 18n evaluations and so is O(n).
http://www.ssrn.com/abstract=2556345
http://www.ssrn.com/1605568.htmlThu, 06 Jul 2017 04:53:52 GMTREVISION: A Divide and Conquer Algorithm for Exploiting Policy Function MonotonicityA divide and conquer algorithm for exploiting policy function monotonicity is proposed and analyzed. To solve a discrete problem with n states and n choices, the algorithm requires at most n log2(n) 5n objective function evaluations. In contrast, existing methods for non-concave problems require n2 evaluations in the worst case. For concave problems, the solution technique can be combined with a method exploiting concavity to reduce evaluations to 14n 2 log2(n). A version of the algorithm exploiting monotonicity in two state variables allows for even more efficient solutions. The algorithm can also be efficiently employed in a common class of problems that do not have monotone policies, including problems with many state and choice variables. In the sovereign default model of Arellano (2008) and the real business cycle model, the algorithm reduces run times by an order of magnitude for moderate grid sizes and orders of magnitude for larger ones. Sufficient conditions for monotonicity ...
http://www.ssrn.com/abstract=2995636
http://www.ssrn.com/1605278.htmlWed, 05 Jul 2017 06:25:40 GMTREVISION: A Divide and Conquer Algorithm for Exploiting Policy Function MonotonicityA divide-and-conquer algorithm for exploiting policy function monotonicity is proposed and analyzed. To compute a discrete problem with n states and n choices, the algorithm requires at most 5n log2(n)n function evaluations and so is O(n log2 n). In contrast, existing methods for non-concave problems require n^2 evaluations in the worst-case and so are O(n^2). The algorithm holds great promise for discrete choice models where non-concavities naturally arise. In one such example, the sovereign default model of Arellano (2008), the algorithm is six times faster than the best existing method when n=100 and 50 times faster when n=1000. Moreover, if concavity is assumed, the algorithm combined with Heer and Maußner (2005)'s method requires fewer than 18n evaluations and so is O(n).
http://www.ssrn.com/abstract=2556345
http://www.ssrn.com/1604705.htmlSat, 01 Jul 2017 15:27:46 GMT