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  • increasing obs per group in data panel regression

    Hello I was wondering if the number of obs is 324 and number of groups is 325. Why do I get only 8 or 9 obs per group in my panel data regression?
    Code:
    . xtreg log_price unemployment mortgage_rate log_income ,fe cluster(ID_code)
    
    Fixed-effects (within) regression Number of obs = 2594
    Group variable: ID_code Number of groups = 325
    
    R-sq: Obs per group:
    within = 0.7181 min = 7
    between = 0.6936 avg = 8.0
    overall = 0.2121 max = 8
    
    F(4,324) = 2066.76
    corr(u_i, Xb) = -0.6932 Prob > F = 0.0000
    
    (Std. Err. adjusted for 325 clusters in GM_code)
    -----------------------------------------------------------------------------------
    | Robust
    log_price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
    ------------------+----------------------------------------------------------------
    unemployment | .0962734 .0061217 15.73 0.000 .0842301 .1083167
    mortgage_rate | .0049352 .0011974 4.12 0.000 .0025796 .0072908
    log_income | -.3595322 .0633615 -5.67 0.000 -.4841841 -.2348802
    _cons | 1.266938 .360273 3.52 0.000 .558168 1.975707
    ------------------+----------------------------------------------------------------
    sigma_u | .28079617
    sigma_e | .03753937
    rho | .98244106 (fraction of variance due to u_i)
    -----------------------------------------------------------------------------------

  • #2
    Because 2594/8=324.

    How many observations per group did you think you'd have?

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    • #3
      Originally posted by Jared Greathouse View Post
      Because 2594/8=324.

      How many observations per group did you think you'd have?
      I mean having 8 obs per group for example it doesnt make it biased?
      Also why is unemployment and mortgage rate positive, if I run normal regression they are both negative?
      Last edited by Joe Mckennedy; 03 Feb 2022, 07:15.

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      • #4
        Why would it make it biased?

        And I don't know, different estimators will yield different results, especially when you used fixed effects estimators and additional covariates

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