Estimation of time-invariant effects in static panel data models

Abstract

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Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity

“…The interested reader is referred to Abowd et al (1999, Section 3.3) for more details. 14 It is worth noting that by assuming (a) the random sorting of patients into physicians, and (b) physician "pure" unobserved heterogeneity ( j ) is uncorrelated with patient unobserved heterogeneity ( i ), the two sources of heterogeneity can be disentangled following the procedure suggested by Pesaran and Zhou (2017). In any case, the postestimation of the physician fixed-effects under the aforementioned assumptions offers no further insights with respect to the analysis of the overall error variance reported in Section 6.…”

Untangling Fixed Effects and Constant Regressors

“…This is simpler. Finally, in FEVD it is not obvious what to include on the left-and right-hand sides in the second-step regression, as Pesaran & Zhou (2014) emphasize. Hausman & Taylor (1981) propose an instrumental variables method to estimate the impact of constant regressors.…”

Double-Question Survey Measures for the Analysis of Financial Bubbles and Crashes

“…For the elements of z i = (z i1 ; z i2 ; :::; z i7 ) 0 , we consider z i1 = 1 if the respondent identi…es as female, and 0 otherwise, z i2 = ln age i , z i3 measures the education level of respondent i, z i4 = ln income i , and z i5 to z i7 are dummy variables that take and x it to be independently distributed, is to employ the two-stage approach proposed recently in Pesaran and Zhou (2016), whereby in the …rst stage FE (or FE-TE) estimates of (h) are used to …lter out the e¤ects of x it , and in the second stage a pure cross section regression ofû i is run on an intercept and z i , for i = 1; 2; :::; N , whereû…”

“…13 A similar result is also reported in Bover (2015) who shows that most of the observed heterogeneity in house price expectations can be explained by a location dummy at the postal code level. FEF -estimator of Pesaran and Zhou (2016). Standard errors are robust to heteroskedasticity and serial correlation.…”

“…-The FE estimates of (h) reported in the table are the same as those summarized in Table 6. 31 FEF -estimator of Pesaran and Zhou (2016). Standard errors are robust to heteroskedasticity and serial correlation.…”