000	02039nam##2200325za#4500
001	9.612490
003	CaOODSP
005	20211126112846
007	ta
008	150406|1995||||xxc|||||     f|0| 0 eng|d
020	|a0-662-23685-8
022	|a1192-5434
040	|aCaOODSP|beng
043	|an-cn---
086	1 |aFB3-2/95-7E
110	2 |aBank of Canada.
245	10|aAnalytical derivatives for Markov switching models / |cby Jeff Gable et al.
260	|aOttawa - Ontario : |bBank of Canada |c1995.
300	|a24p. : |bgraphs, references, tables ; |c28 cm.
490	1 |aWorking paper|x1192-5434|v95-7
500	|a"This paper derives analytical gradients for a broad class of regime-switching models with Markovian state-transition probabilities. Such models are usually estimated by maximum likelihood methods, which require the derivatives of the likelihood function with respect to the parameter vector. These gradients are usually calculated by means of numerical techniques. The paper shows that analytical gradients considerably speed up maximum-likelihood estimation with no loss in accuracy. A sample program listing is included."--Abstract.
520	3 |aThis paper derives analytical gradients for a broad class of regime-switching models with Markovian state-transition probabilities. Such models are usually estimated by maximum likelihood methods, which require the derivatives of the likelihood function with respect to the parameter vector. These gradients are usually calculated by means of numerical techniques. The paper shows that analytical gradients considerably speed up maximum-likelihood estimation with no loss in accuracy. A sample program listing is included.--Abstract
546	|aRésumés en français
563	|aSoftcover
590	|a95-36|b1995-09-08
690	07|aRates|2gcpds
690	07|aCurrency|2gcpds
720	1 |aGable, Jeff
776	0#|tAnalytical derivatives for Markov switching models / |w(CaOODSP)9.571638
830	#0|aWorking paper,|x1192-5434|v95-7|w(CaOODSP)9.514622