Announcement

Collapse
No announcement yet.
X
  • Filter
  • Time
  • Show
Clear All
new posts

  • Please Help Me...

    I use "Qregpd" and run the equation follow:
    equation 1:
    qregpd e_ta_bk mblag lagtang proflag lagsize lagblev, id(id) fix(year) optimize(mcmc) noisy draws(1000) burn(100) arate(0.5) quantile(.1)
    Result:

    qregpd e_ta_bk mblag lagtang proflag lagsize lagblev, id(id) fix(year) optimize(mcmc) noisy draws(1000) burn(100) arate(0.5) quantile(.1)
    Adaptive MCMC optimization
    ................................................. 50: f(x) = -491.682055
    ................................................. 100: f(x) = -341.294361
    ................................................. 150: f(x) = -329.841774
    ................................................. 200: f(x) = -325.344571
    ................................................. 250: f(x) = -310.753205
    ................................................. 300: f(x) = -304.755536
    ................................................. 350: f(x) = -267.094713
    ................................................. 400: f(x) = -258.626104
    ................................................. 450: f(x) = -256.360916
    ................................................. 500: f(x) = -247.309495
    ................................................. 550: f(x) = -232.739689
    ................................................. 600: f(x) = -229.698686
    ................................................. 650: f(x) = -229.698686
    ................................................. 700: f(x) = -227.903506
    ................................................. 750: f(x) = -227.903506
    ................................................. 800: f(x) = -230.524269
    ................................................. 850: f(x) = -226.760101
    ................................................. 900: f(x) = -228.97304
    ................................................. 950: f(x) = -228.97304
    ................................................. 1000: f(x) = -228.899609


    Quantile Regression for Panel Data (QRPD)
    Number of obs: 36108
    Number of groups: 3454
    Min obs per group: 1
    Max obs per group: 17

    e_ta_bk Coef. Std. Err. z P>z [95% Conf. Interval]

    mblag .0003999 .000106 3.77 0.000 .0001921 .0006078
    lagtang .0235918 .0030777 7.67 0.000 .0175597 .0296239
    proflag .0347651 .0027136 12.81 0.000 .0294465 .0400836
    lagsize -.0026297 .000184 -14.29 0.000 -.0029904 -.002269
    lagblev .0023893 .0011837 2.02 0.044 .0000693 .0047093

    No excluded instruments - standard QRPD estimation.


    MCMC diagonstics:
    Mean acceptance rate: 0.074
    Total draws: 1000
    Burn-in draws: 100
    Draws retained: 900
    Value of objective function:
    Mean: -258.4611
    Min: -344.4691
    Max: -226.7601
    MCMC notes:
    *Point estimates correspond to mean of draws.
    *Standard errors are derived from variance of draws.

    and equation 2
    qregpd c1bk mblag lagtang proflag lagsize lagblev, id(id) fix(year) optimize(mcmc) noisy draws(1000) burn(100) arate(0.5) quantile(.1)
    note that -(e_ta_bk) = c1bk
    qregpd c1bk mblag lagtang proflag lagsize lagblev, id(id) fix(year) optimize(mcmc) noisy draws(1000) burn(100) arate(0.5) quantile(.1)
    Adaptive MCMC optimization
    ................................................. 50: f(x) = -1485.30362
    ................................................. 100: f(x) = -910.460328
    ................................................. 150: f(x) = -774.740706
    ................................................. 200: f(x) = -636.863033
    ................................................. 250: f(x) = -562.688539
    ................................................. 300: f(x) = -416.551356
    ................................................. 350: f(x) = -393.861994
    ................................................. 400: f(x) = -394.787211
    ................................................. 450: f(x) = -383.245927
    ................................................. 500: f(x) = -383.245927
    ................................................. 550: f(x) = -383.245927
    ................................................. 600: f(x) = -381.003299
    ................................................. 650: f(x) = -370.73573
    ................................................. 700: f(x) = -366.675186
    ................................................. 750: f(x) = -350.241635
    ................................................. 800: f(x) = -308.686663
    ................................................. 850: f(x) = -279.434892
    ................................................. 900: f(x) = -266.704798
    ................................................. 950: f(x) = -237.271468
    ................................................. 1000: f(x) = -234.880424


    Quantile Regression for Panel Data (QRPD)
    Number of obs: 36108
    Number of groups: 3454
    Min obs per group: 1
    Max obs per group: 17
    ------------------------------------------------------------------------------
    c1bk | Coef. Std. Err. z P>|z| [95% Conf. Interval]
    -------------+----------------------------------------------------------------
    mblag | .0179996 .0018409 9.78 0.000 .0143914 .0216077
    lagtang | -.3846238 .0184168 -20.88 0.000 -.4207201 -.3485275
    proflag | -.7282647 .0483179 -15.07 0.000 -.822966 -.6335633
    lagsize | .0768478 .0073139 10.51 0.000 .0625128 .0911827
    lagblev | -.4521848 .0147003 -30.76 0.000 -.4809969 -.4233727
    ------------------------------------------------------------------------------
    No excluded instruments - standard QRPD estimation.


    MCMC diagonstics:
    Mean acceptance rate: 0.067
    Total draws: 1000
    Burn-in draws: 100
    Draws retained: 900
    Value of objective function:
    Mean: -411.2478
    Min: -910.4603
    Max: -234.2407
    MCMC notes:
    *Point estimates correspond to mean of draws.
    *Standard errors are derived from variance of draws.


    But I get the results very different. While I check the result in reg pool, xtreg (fixed effect, random effect) it just changes the sign of independent variables.

    Another thing, after I run quantile regression again (qreg, xtqreg, qregpd), I'll get the other results...

    I really don't understand why. Does anyone can help me, please?
    Thanks so much for any your reply.

  • #2
    if you don't get a response, do

    * start a new thread making qregpd as part of the title

    * close this one with a link to the new thread.

    * explain where qregpd comes from.

    (The question title is not good: almost all threads imply the same request.)

    Comment


    • #3
      Dear Professor Nick Cox,
      Thank so much for your response.
      Best Wishes,

      Comment

      Working...
      X