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Observations may be clustered by "group" "time" to account for serial cross-sectional correlation. All types assume no intragroup serial correlation between errors and allow for heteroskedasticity across groups time periods. ARELplm allows a fully general structure w. The main use of vcovHC is to be an argument to other functions, e. Notice that the vcov and vcov. An object of class "matrix" containing the estimate of the asymptotic covariance matrix of coefficients.

The function pvcovHC is deprecated. Use vcovHC for the same functionality. For more information on customizing the embed code, read Embedding Snippets.

Functions Source code Man pages R Description Robust covariance matrix estimators a la White for panel models. S3 method for class 'pgmm' vcovHC xRelated to vcovHC. Package overview Estimation of error components models with the plm function Model components for fitted models with plm Panel data econometrics in R:'. R Package Documentation rdrr. We want your feedback! Note that we can't provide technical support on individual packages.

You should contact the package authors for that. Tweet to rdrrHQ.

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GitHub issue tracker. Personal blog. What can we improve? The page or its content looks wrong. I can't find what I'm looking for. I have a suggestion. Extra info optional. Embedding an R snippet on your website. Add the following code to your website.Observations may be clustered by "group" "time" to account for serial cross-sectional correlation.

All types assume no intragroup serial correlation between errors and allow for heteroskedasticity across groups time periods. As for the error covariance matrix of every single group of observations, "white1" allows for general heteroskedasticity but no serial cross--sectional correlation; "white2" is "white1" restricted to a common variance inside every group time period see GREE, Sec. ARELplm allows a fully general structure w.

The main use of vcovHC is to be an argument to other functions, e. Notice that the vcov and vcov. A special procedure for pgmm objects, proposed by WIND;textualplm, is also provided. An object of class "matrix" containing the estimate of the asymptotic covariance matrix of coefficients.

The function pvcovHC is deprecated. Use vcovHC for the same functionality. Created by DataCamp. Community examples Looks like there are no examples yet. Post a new example: Submit your example. API documentation. Put your R skills to the test Start Now.All HC2 stockholders will have the opportunity to participate in the offering and subscribe for their basic subscription amount of newly issued shares of common stock in proportion to their respective existing ownership amounts.

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Contact: Investor Relations Garrett Edson ir hc2. This week we saw the HC2 Holdings, Inc. But that can't change the reality that Barrington Research Associates, Inc. A total of 4, shares of common stock were issued in the offering. In addition, the Company has granted the underwriters a day option to purchase up to an additionalshares of common stock solely to cover over-allotments, if any, at the public offering price per share, less the underwriting discounts and commissions.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Economics Stack Exchange is a question and answer site for those who study, teach, research and apply economics and econometrics.

It only takes a minute to sign up. I'm trying to figure out the commands necessary to replicate the following table in Stata. This table is taken from Chapter 11, p. Here I'm specifically trying to figure out how to obtain the robust standard errors shown in square brackets in column 2.

I'm trying to do this in Stata. I was able to to get the conventional standard errors using the command. It gives results that are different from the book. Could someone explain how to obtain these standard errors in Stata? Use -areg- in Stata, and the standard errors will come out as in the textbook.

Specifically, the command. The latter seems to be what Wooldridge estimated. Moreover, -xtreg- assumes that the number of -xtset- groups id in your example grows when more data is added to the sample. The point estimates will be identical, but standard errors will be different, sometimes substantially so.

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Old versions of Stata e. Stata 9 did not make the appropriate degrees of freedom adjustment when -xtreg, vce robust - was called, which is why you get a bigger standard error when specifying -version In fact, those standard errors are identical to -areg, absorb id vce cluster id - in newer versions of Stata.

As a side note, it is puzzling that Wooldridge got non-clustered robust standard errors when calling -xtreg, vce robust - in version 9, but perhaps I have a flawed understanding of what the call -version 9- does.

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For more information on -xtreg- vs -areg- see the blogpost and comments here. I recognize that this is a year-old thread, and that the question might have been answered on the Statalist. Consider my answer as "for future reference".

I'm still not sure if I'm doing something wrong. However, it is useful to note that I get the same results in R. The book gives 0. To understand the issue let's review what is the so call robust variance-covariance matrix estimates VCE and the implied "robust" standard errors. The robustness is meant to allow for violations of homoscedasticity in the cross-sectional dimension or heteroscedasticity.

Stata by default uses HC1 which uses the residuals just as HC0, but has a degrees of freedom adjustment. However, one can request HC2 or HC3 through the vce option after a compatible command, e. However, in the case of unobserved effects models such as the one-way error component xtreg one should not use HC estimators and choose an appropriate VCE which allows for dependence of the error term.

Stata xtreg and xtivreg and similar commands are for short-panels one-way error models one can include the temporal intercept for two-way error models manually.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I am trying to update my lm based model to get correct standard errors and tests.

I am really confused which VC matrix to use. While the former only accounts for heteroskedasticity the latter two account for both serial correlation and heteroskedasticity. Yet, the documentation does not tell much about the difference between the latter two at least I don't get it. Empirically the results of coeftest mymodel, vcov. While vcovHAC is somewhat close to the naive lm results, using NeweyWest all coefficients turn insignificant tests even close to 1.

The "sandwich" in question is two pieces of bread defined by the expected information enclosing a meat defined by the observed information. See my comments here and here.

For a linear regression, the estimating equation is:. However, that's rather strict. The "HC0" vcovHC estimator is consistent even when the data are not independent.

So I will not say that we "assume" the residuals are independent, but I will say that we use "a working independent covariance structure". The HC are various finite sample corrections. HC3 is generally the best performing. This is the rationale for the "vcovHAC". Here, very flexible and general methods are produced to estimate the autoregressive effect: the details may be beyond the scope of your question. The "meatHAC" function is the general workhorse: the default method is Andrews'.

Newey-West is a special case of the general autoregressive error estimator. These methods solve one of two problems: 1. These If you have balanced panel data, this covariance estimator is overkill.

You should use gee from the gee package instead specifying the covariance structure to AR-1 or similar. As for which to use, it depends on the nature of the data analysis and the scientific question. I would not advise fitting all the types and picking the one that looks best, as it is a multiple testing issue.Heteroscedasticity-consistent estimation of the covariance matrix of the coefficient estimates in regression models.

For details see below. Should the sandwich estimator be computed? The function meatHC is the real work horse for estimating the meat of HC sandwich estimators -- the default vcovHC method is a wrapper calling sandwich and bread. See Zeileis for more implementation details.

The theoretical background, exemplified for the linear regression model, is described below and in Zeileis Analogous formulas are employed for other types of models. All other methods do not assume constant variances and are suitable in case of heteroscedasticity.

They are all of form.

vcovHC.plm

This is in all cases a diagonal matrix whose elements can be either supplied as a vector omega or as a a function omega of the residuals, the diagonal elements of the hat matrix and the residual degrees of freedom.

For White's estimator. Instead of specifying the diagonal omega or a function for estimating it, the type argument can be used to specify the HC0 to HC5 estimators. If omega is used, type is ignored. All of them are tailored to take into account the effect of leverage points in the design matrix. For more details see the references. Cribari-Neto F. Errata: 37, Long J. MacKinnon J. White H. Zeileis A Heteroskedasticity-consistent estimation of the covariance matrix of the coefficient estimates in regression models.

The function meatHC is the real work horse for estimating the meat of HC sandwich estimators — vcovHC is a wrapper calling sandwich and bread. See Zeileis for more implementation details. The theoretical background, exemplified for the linear regression model, is described below and in Zeileis All other methods do not assume constant variances and are suitable in case of heteroskedasticity. They are all of form.

This is in all cases a diagonal matrix whose elements can be either supplied as a vector omega or as a a function omega of the residuals, the diagonal elements of the hat matrix and the residual degrees of freedom.

For White's estimator. Instead of specifying of providing the diagonal omega or a function for estimating it, the type argument can be used to specify the HC0 to HC4 estimators. If omega is used, type is ignored. Cribari-Neto suggests the HC4 type estimator which is tailored to take into account the effect of leverage points in the design matrix.

For more details see the references. Cribari-Neto F. Long J. The American Statistician54— MacKinnon J. Journal of Econometrics 29— White H.

Econometrica 48— Journal of Statistical Software11 101— For details see below. Should the sandwich estimator be computed?