One aspect of this approach is that it allows time-varying coefficients of independent variables, in contrast to a traditional fixed effects model using, for example xtreg, which would estimate coefficients spanning all waves, for example:
Code:
xtset id year xtreg score treat year, fe ------------------------------------------------------------------------------ score | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- treat | -,0240636 ,0065614 -3,67 0,000 -,0369239 -,0112033 year | -,0982931 ,0023614 -41,62 0,000 -,1029215 -,0936646 _cons | ,152554 ,0016699 91,36 0,000 ,149281 ,155827 -------------+---------------------------------------------------------------- (...)
First, when I compare time-varying and time-constant coefficients, the time-constant coefficient are roughly similar to, and sometimes a bit like averages of, the time-varying coefficients. This does not entirely explain the relationship between the coefficients, though (as illustrated below). Is there a more accurate way to interpret the difference between the time-varying and time-constant coefficients?
T1 | T2 | Time-constant | |
var1 | -0.050 | -0.040 | -0.024 |
var2 | -0.052 | 0.065 | 0.002 |
Second, are there situations in which time-constant or time-varying coefficients should be more trusted?
Best regards,
Oscar
Two references regarding time-varying coefficients in SEM approaches are:
- The results discussed in Figure 4 in the article by Bollen & Brand (2010), A General Panel Model with Random and Fixed Effects: A Structural Equations Approach.
- The xfree and xfree2 options in the user-developed Stata command xtdpml by Williams, Allison & Moral-Benito.
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