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  • SEM modeling for a latent variable with panel data

    Hi everyone,

    I am attempting to estimate the shadow economy (a latent variable) for 150 countries from 2000-2016. I am basing my data off several papers which follow the following general framework:


    [ATTACH=CONFIG]n1447263[/ATTACH]

    Here is my code:

    sem (burden goveff unemp bfree reg -> Shadow)(Shadow -> realgdppercap@1 laborforcepartrate broadmoneypercentgdp), standardized

    where my causal variables tax burden, government effectiveness, unemployment, business freedom index, and regulatory burden and my indicator variables are real GDP per capita, broad money as a percent of GDP, and labor force participation rate. I fixed the real GDP per capita to 1 because I am unable to fix it to -1 without error.

    I then predict the latent variable:

    predict x, latent(Shadow)

    I am hoping to use the predict latent variables by indexing the changes for each country from 2000 to 2016 to understand the relative change in the latent variable over time.

    The issue that I am most concerned with is that I have this data for 150 countries from 2000-2016, how do I indicate these fixed effects to STATA using the SEM model? It seems that my predicted latent variable will not be correct if I do not somehow control for the fact that there are specific countries in the data but I do not see that option anywhere.

    Should I be using something like GLLAMM instead? Any help would be greatly appreciated!

    Thanks,
    Beckie

  • #2
    There are several papers on doing panel estimates using SEM and GSEM. Google them. Generally, you have to move the data to wide layout.

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