Finite-Sample Properties of Some Alternative GMM Estimators

Hansen, Lars Peter; Heaton, J.B.; Yaron, Amir

doi:10.2307/1392442

Cited by 268 publications

(82 citation statements)

References 12 publications

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“…There are three main ways of implementing the gmm estimator: the two-stage, the iterative and the continuous updating gmm estimators (Hansen et al 1996). According to Newey and Smith (2004) and Anatolyev (2005), the three methods are asymptotically equivalent, but the second-order bias of the continuous updating estimator is smaller, and the iterations increase the estimator's efficiency.…”

Section: Estimation By the Generalized Methods Of Momentsmentioning

confidence: 99%

Generalized moment estimation of stochastic differential equations

Laurini

Hotta²

2015

Comput Stat

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We study the semiparametric estimation of stochastic differential equations employing methods based on moment conditions, comparing the finite sample and robustness properties of generalized method of moments, empirical likelihood and minimum contrast methods using unconditional and conditional formulations of moment conditions. The results obtained indicate that the estimators proposed, particularly, the estimators based on exponential tilting, obtain better results than those of the generalized methods of moments normally used to estimate stochastic differential equations. This conclusion is mainly derived from the robustness properties of this method in the presence of problems of incorrect specification.

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Section: Estimation By the Generalized Methods Of Momentsmentioning

confidence: 99%

Generalized moment estimation of stochastic differential equations

Laurini

Hotta²

2015

Comput Stat

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“…046114-4 20, so that in each bin a probability mass of F λ,σ 0 (x j ) − F λ,σ 0 (x j −1 ) ≈ 0.05 is obtained. λ 2 q was calculated using q = 0.5 after the series was filtered by the sample estimate σ .…”

Section: Estimation Methodologymentioning

confidence: 99%

“…. , 20. At a significance level of 0.05, the J-test statistics would allow one to reject the multifractal cascade as the data-generating process only for the Swiss francs (CHF), on the basis of our chosen moment functions.…”

Section: Empirical Evidencementioning

confidence: 99%

Parameter estimation and forecasting for multiplicative log-normal cascades

Leövey

Lux

2012

Phys. Rev. E

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We study the well-known multiplicative log-normal cascade process in which the multiplication of Gaussian and log normally distributed random variables yields time series with intermittent bursts of activity. Due to the nonstationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian probability density function to empirical data, cf. Castaing et al. [Physica D 46, 177 (1990)]. More recently, alternative estimators based upon various moments have been proposed by Beck [Physica D 193, 195 (2004)] and Kiyono et al. [Phys. Rev. E 76, 041113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous generalized method of moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono et al.'s procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets.

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“…Hansen et al (1996) found evidence that GMM-type estimators behave differently in the presence of weak identification. According to them, the continuous updating estimator is less biased, and its confidence intervals have better coverage rates than the two-step GMM.…”

Section: Joint Estimation By Gmmmentioning

confidence: 99%

“…The iterated GMM estimator continues this process, evaluating ̂ ̂ at the previous estimate of until reaching convergence, obtaining ̂ (Ferson & Foerster, 1994). Hansen, Heaton and Yaron (1996) suggested minimizing the following quadratic form of the sample moments:…”

Section: Joint Estimation By Gmmmentioning

confidence: 99%

The equity premium puzzle: analysis in Brazil after the real plan

Gomes

Costa

Pupo

2012

BAR, Braz. Adm. Rev.

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Our paper investigates whether there is evidence of an Equity Premium Puzzle (EPP) in Brazil, applying two different methodologies. The EPP was identified by Mehra and Prescott (1985) since the Consumption Capital Asset Pricing Model (CCAPM), when calibrated with reasonable preference parameters, could not explain high historical average risk premiums in the United States. In our first approach, we consider Mehra's (2003) model and calibrate the coefficient of risk aversion, using 1995:2-2012:1 quarterly data. The Ibovespa index was used as a measure of the market return, whereas the risk-free rate was proxied by the Selic interbank rate and by the savings account rate. In our second approach, we propose a new method to test the puzzle. We jointly estimate, via generalized method of moments, the parameters of interest using a moment condition that has not been previously explored, as far as we are aware of. The two approaches produced a high risk aversion coefficient, however the second approach indicated that we cannot reject the hypothesis of the risk aversion coefficient being statistically equal to zero. A possible explanation for this result might be that in Brazil the equity premium is not statistically different from zero. Therefore there is no evidence of EPP in Brazil for the studied period.

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Finite-Sample Properties of Some Alternative GMM Estimators

Cited by 268 publications

References 12 publications

Generalized moment estimation of stochastic differential equations

Generalized moment estimation of stochastic differential equations

Parameter estimation and forecasting for multiplicative log-normal cascades

The equity premium puzzle: analysis in Brazil after the real plan

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