A Nonparametric Simulated Maximum Likelihood Estimation Method

Fermanian, Jean‐David; Salanié, Bernard

doi:10.1017/s0266466604204054

Cited by 57 publications

(65 citation statements)

References 23 publications

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“…For example, there are many simulation based methods, such as efficient method of moments (see Tauchen (1996,1997), Chernov and Ghysels (2000), and the references cited therein) and nonparametric simulated maximum likelihood (see e.g. Fermanian and Salanié (2004) and CS (2011)). For further discussion, see Aït-Sahalia (2007) which provides a survey on estimating continuous models using discrete observations.…”

Section: Smooth Transition Autoregression Model (Star)mentioning

confidence: 99%

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

Cai

Swanson

2011

Journal of Empirical Finance

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We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, Benzoni and Lund (2004). Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

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Section: Smooth Transition Autoregression Model (Star)mentioning

confidence: 99%

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

Cai

Swanson

2011

Journal of Empirical Finance

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“…In this chapter we develop a recursive version of the nonparametric simulated (quasi) maximum likelihood (NPSQML) estimator of Fermanian and Salanié (2004) and outline conditions under which asymptotic equivalence between NPSQML and the corresponding recursive QMLE obtains, hence ensuring that A4 and A4' hold. Analogous results are also established for the bootstrap counterpart of the recursive NPSQML estimators.…”

Section: Recursive Nonparametric Simulated Quasi Maximum Likelihood Ementioning

confidence: 99%

“…Section 3 outlines the testing procedure for choosing between m > 2 models, and establishes the asymptotic properties thereof. In Section 4, we develop a recursive version of the nonparametric simulated (quasi) maximum likelihood (NPSQML) estimator of Fermanian and Salanié (2004) and outline conditions under which asymptotic equivalence between NPSQML and the corresponding recursive QMLE obtains. An empirical illustration is provided in Section 5, in which various models of the effective federal funds rate are compared.…”

Section: Introductionmentioning

confidence: 99%

“…Then, we construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of our tests, we also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.…”

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confidence: 99%

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Predictive Density Construction and Accuracy Testing with Multiple Possibly Misspecified Diffusion Models

Corradi

Swanson

2009

SSRN Journal

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This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, we construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of our tests, we also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.JEL classification: C22, C51.

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“…Our approach is related to a method suggested in Diggle and Gratton (1984) (see also Fermanian and Salanié, 2004). There, an approximate maximum likelihood estimate is obtained using a stochastic version of the Nelder-Mead algorithm.…”

Section: Introductionmentioning

confidence: 99%

Approximate maximum likelihood estimation for population genetic inference

Bertl

Ewing

Kosiol

et al. 2017

Statistical Applications in Genetics and Molecular Biology

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Abstract:In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.

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A Nonparametric Simulated Maximum Likelihood Estimation Method

Cited by 57 publications

References 23 publications

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982–2008

Predictive Density Construction and Accuracy Testing with Multiple Possibly Misspecified Diffusion Models

Approximate maximum likelihood estimation for population genetic inference

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