# Tagged Questions

A method of parameter estimation by equating sample and population moments then solving the equations for the unknown parameters.

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### What is parameter identification in the context of OLS?

Can someone explain what identification means in the context of an OLS model? I have a fair grasp of the derivation using either the method of moments or by minimizing the squares, but am failing to ...
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### Fully identified moment matching with simulated method of moments

I am struggling with some results regarding the model fit in this paper (p. 49). The authors set up a simulated method of moment estimator, using 8 different moments to match and 8 different ...
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### Second Moment of Beta distribution

I am practicing the Method of Moments and in this problem, I am a little bit stuck on the algebra in my calculation of the second moment. I would sincerely appreciate any advice on what went wrong: ...
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### Variance of estimator(exponential distribution)

I have exponential distributed data $Exp(\lambda)$ with sample n = 50. Also, The sample mean = 2.17. I need to find the estimator of parameter $\lambda$ by the method of moments and to build 95% ...
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### Two stage GMM estimator in Matlab

I am trying to create a simple GMM estimator for the mean of a normally distributed random variable using the first three odd central moments of a normal distribution (all of which should be zero ...
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### Required: Method of moments fitting routine for the two-parameter generalized Pareto

I am currently using the evd package which fits a two-parameter GPD by maximum likelihood. Since in small samples the MOM is superior to the ML estimation I'd like ...
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### How to theoretically fit and test for t-distribution [duplicate]

I'm trying to test some data for the best distribution fit, and am looking to try the t-dist (all theoretically, no R computations) I think the best way to do this is to assume the distribution is ...
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### Types of moments used in the method of moments?

In Wikipedia, the method of moments uses only ordinary moments: One starts with deriving equations that related the population moments (i.e., the expected values of powers of the random variable ...
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### How would I assign the optimal weight matrix to combine the following moment conditions in MM?

The following information is given: Based on a sample x(n), with n = 1,..., 100 from the exponential distribution, we want to estimate lambda but the original data was lost; we only know that 38 ...
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### GMM weight matrix $W_n$ — what does the index $n$ signify? [closed]

The question is as in the title. The GMM estimator (in my Econometrics notes) is as follows: $\hat \delta_{GMM}(W_n) = (S'_{xz} W_nS'_{xz})^{-1}(S'_{xz}W_ns_{xy})$
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### Explaining generalized method of moments to a non-statistician

How do I explain Generalized Methods of moments and how it is used to a non statistician? So far I am going with: it is something we use to estimate conditions such as averages and variation based ...
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### Examples where method of moments can beat maximum likelihood in small samples?

Maximum likelihood estimators (MLE) are asymptotically efficient; we see the practical upshot in that they often do better than method of moments (MoM) estimates (when they differ), even at small ...
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### Is MLE more efficient than Moment method?

I have got some small data sets (about 8 to 11 data points for each set), following Normal distribution. I would like to find out the 95% confidence interval of the 0.005 and 0.995 percentile of each ...
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### Hard thresholding a covariance matrix

I am new to the concept of thresholding a variance-covariance matrix and am having trouble understanding the exact process. I am following Bickel and Levina (2008) in choosing a hard threshold. What ...
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### Derivation of the Satterthwaite appproximation

Using the method of moments, one can try to approximate the sum of $\chi_{r}^{2}$ variables as $\sum a_{i}Y_{i}$ by equating the $n$-th movements of the sample with the $n$-th movement of the ...
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### Generalized method of moments versus standard least squares estimation

I was thinking that in a very standard case such as a simple linear model with iid errors and no endogeneity, I would get the same results using the a simple least square estimate (such as provided by ...
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### What is the correct process of finding the point estimators for the following situation?

Let $X_1,X_2,...,X_n$ be a random sample from a uniform distribution on $(\mu-\sqrt 3\sigma,\mu+\sqrt3\sigma)$. Here the unknown parameters are two, namely $\mu$ and $\sigma$, which are the ...
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