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  • FE estimation

    Hey everyone...
    I'm trying to make some estimations with panel data...
    Command xtpcse is the same as an estimation FE? may explain to me the difference between those commands?

  • #2
    Welcome to the Stata Forum/Statalist,

    Maybe this text from the Stata Manual will clarify your doubts:

    xtpcse is an alternative to feasible generalized least squares (FGLS)—see [XT] xtgls—for fitting linear cross-sectional time-series models when the disturbances are not assumed to be independent and identically distributed (i.i.d.). Instead, the disturbances are assumed to be either heteroskedastic across panels or heteroskedastic and contemporaneously correlated across panels. The disturbances may also be assumed to be autocorrelated within panel, and the autocorrelation parameter may be constant across panels or different for each panel.
    [...] xtpcse and xtgls follow two different estimation schemes for this family of models. xtpcse produces OLS estimates of the parameters when no autocorrelation is specified, or Prais–Winsten (see [TS] prais) estimates when autocorrelation is specified. If autocorrelation is specified, the estimates of the parameters are conditional on the estimates of the autocorrelation parameter(s). The estimate of the variance–covariance matrix of the parameters is asymptotically efficient under the assumed covariance structure of the disturbances and uses the FGLS estimate of the disturbance covariance matrix; see Kmenta (1997, 121). xtgls produces full FGLS parameter and variance–covariance estimates. These estimates are conditional on the estimates of the disturbance covariance matrix and are conditional on any autocorrelation parameters that are estimated; see Kmenta (1997), Greene (2012), Davidson and MacKinnon (1993), or Judge et al. (1985). Both estimators are consistent, as long as the conditional mean (xitβ) is correctly specified. If the assumed covariance structure is correct, FGLS estimates produced by xtgls are more efficient. Beck and Katz (1995) have shown, however, that the full FGLS variance–covariance estimates are typically unacceptably optimistic (anticonservative) when used with the type of data analyzed by most social scientists—10–20 panels with 10–40 periods per panel. They show that the OLS or Prais–Winsten estimates with PCSEs have coverage probabilities that are closer to nominal. Because the covariance matrix elements, σij , are estimated from panels i and j, using those observations that have common time periods, estimators for this model achieve their asymptotic behavior as the Tis approach infinity. In contrast, the random- and fixed-effects estimators assume a different model and are asymptotic in the number of panels m; see [XT] xtreg for details of the random- and fixed-effects estimators. Although xtpcse allows other disturbance covariance structures, the term PCSE, as used in the literature, refers specifically to models that are bo
    Best regards,

    Marcos

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    • #3
      Valentina:
      as Marcos'helpful reply by quoting the -xtpcse- help file (by the way: probably the most useful feature of Marcos' reply rests on his recommendation, that is: every lister interested in a given command or function available in Stata should take a look at both Stata .pdf manual related entries and related help files, before posting), this command differs from a "simple" -fe- estimator (say -xtreg,fe-).
      Moreover, -xtpcse- was devised for long panels (T>N), whereas -xtreg- works well for short panels (N>T).
      In sum, to get the most from this forum you should provide interested listers with enough details concerning your problem, so that your chances of getting positive replied get exponentiated.
      I do hope you will enjoy staying with Statalist.
      Kind regards,
      Carlo
      (Stata 15.1 SE)

      Comment


      • #4
        Thanks a lot, Marcos and Carlo.

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