Estimating Affine Multifactor Term Structure Models Using Closed-Form Likelihood Expansions

Yacine Ait-Sahalia and Robert Kimmel

No 286, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: We develop and implement a technique for closed-form maximum likelihood estimation (MLE) of multifactor affine yield models. We derive closed-form approximations to likelihoods for nine Dai and Singleton (2000) affine models. Simulations show our technique very accurately approximates true (but infeasible) MLE. Using US Treasury data, we estimate nine affine yield models with different market price of risk specifications. MLE allows non-nested model comparison using likelihood ratio tests; the preferred model depends on the market price of risk. Estimation with simulated and real data suggests our technique is much closer to true MLE than Euler and quasi-maximum likelihood (QML) methods.

JEL-codes: G12 G13 (search for similar items in EconPapers)
Date: 2002-12
New Economics Papers: this item is included in nep-cfn and nep-fmk
Note: TWP
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Citations: View citations in EconPapers (13)

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Related works:
Journal Article: Estimating affine multifactor term structure models using closed-form likelihood expansions (2010)
Working Paper: Estimating Affine Multifactor Term Structure Models Using Closed-Form Likelihood Expansions (2008)
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