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Dane panelowe Model
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Dane panelowe

Mar 19, 2016

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Dane panelowe. Model. Model grawitacyjny. Deklaracja danych panelowych. Wymiar czasu określa się komendą tis a wymiar obiektu komendą iis tis year iis pairid. Braki obserwacji w panelu. xtdes pairid : 1, 2, ..., 10193 n = 10194 - PowerPoint PPT Presentation
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  • Dane paneloweModel

  • Model grawitacyjny

  • Deklaracja danych panelowychWymiar czasu okrela si komend tis a wymiar obiektu komend iis

    tis year iis pairid

  • Braki obserwacji w paneluxtdes

    pairid: 1, 2, ..., 10193 n = 10194 year: 70, 75, ..., 95 T = 6 Delta(year) = 5; (95-70)/5 + 1 = 6 (pairid*year does not uniquely identify observations)

    Distribution of T_i: min 5% 25% 50% 75% 95% max 1 1 2 4 6 6 616

    Freq. Percent Cum. | Pattern ---------------------------+--------- 3823 37.50 37.50 | 111111Dla 37,50% jednostek, ktre 596 5.85 43.35 | ..1111pojawiy si w panelu 572 5.61 48.96 | .....1dysponujemy kompletem 6 obs. 434 4.26 53.22 | .11111 412 4.04 57.26 | 11111. 291 2.85 60.11 | ....1. 258 2.53 62.64 | ..1... 247 2.42 65.07 | ....11 244 2.39 67.46 | 11.... 3317 32.54 100.00 | (other patterns) ---------------------------+--------- 10194 100.00 | XXXXXX

  • Macierz przejciaxttrans nazwa_zmiennej

  • Estymator efektw losowychModel efektw losowych stosujemy, gdy:

    efekty indywidualne nie s a skorelowane z regresorami xit

    za., e efekt indywidualny jest realizacj pewnej zmiennej losowej o okrelonym rozkadzie.

  • xtreg ltrade lrgdp lpop ldist cu comcol comctry comlang colonial regional ll island

    Random-effects GLS regression Number of obs = 31226Group variable (i): pairid Number of groups = 7961R-sq: within = 0.0308 Obs per group: min = 1 between = 0.6298 avg = 3.9 overall = 0.5761 max = 6Random effects u_i ~ Gaussian Wald chi2(11) = 12000.23corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000------------------------------------------------------------------------------ ltrade | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- lrgdp | .8973097 .0128265 69.96 0.000 .8721703 .9224491 lpop | -.2821761 .0157241 -17.95 0.000 -.3129948 -.2513574 ldist | -1.189486 .029738 -40.00 0.000 -1.247771 -1.1312 cu | .4350876 .2064135 2.11 0.035 .0305245 .8396508 comcol | -.1916404 .0858374 -2.23 0.026 -.3598786 -.0234022 comctry | .8194063 .2761104 2.97 0.003 .2782399 1.360573 comlang | .4530111 .0722408 6.27 0.000 .3114217 .5946004 colonial | 2.587744 .2190961 11.81 0.000 2.158324 3.017165 regional | .1496601 .0897927 1.67 0.096 -.0263305 .3256506 ll | -.6674142 .0552592 -12.08 0.000 -.7757203 -.5591082 island | .0216517 .0431965 0.50 0.616 -.063012 .1063153 _cons | -6.97683 .3468105 -20.12 0.000 -7.656566 -6.297094-------------+---------------------------------------------------------------- sigma_u | 1.7722969 sigma_e | 1.2885209 rho | .65420233 (fraction of variance due to u_i)------------------------------------------------------------------------------

  • Zapisywanie wynikwest store random

  • xtsumR2 between jest duo wyszy od R2 within, co jest spowodowano tym, e dla wikszoci zmiennych zrnicowanie midzy jednostkami jest znacznie wysze ni dla poszczeglnych jednostek.xtsum ltrade

    Variable | Mean Std. Dev. Min Max | Observations-----------------+-----------------------------------------------+----------------ltrade overall | 9.060543 3.348193 .1322796 19.39635 | N = 41061 between | 3.290938 .1322796 19.26711 | n = 10193 within | 1.213239 .0896298 16.43797 | T-bar = 4.02835

    UWAGA! Dla tych zmiennych mona by byo obliczy rednie po pairid i odchylenia po tych zmiennych.

  • Graficzna prezentacja zalenoci midzy zmiennymi graph matrix ltrade cu ldist lrgdp

  • Test Breuscha-Pagana na wystpowanie efektw indywidualnych.

    xttest0

    Breusch and Pagan Lagrangian multiplier test for random effects:

    ltrade[pairid,t] = Xb + u[pairid] + e[pairid,t]

    Estimated results: | Var sd = sqrt(Var) ---------+----------------------------- ltrade | 10.87267 3.297373 e | 1.660286 1.288521 u | 3.141036 1.772297

    Test: Var(u) = 0 chi2(1) = 11779.01 Prob > chi2 = 0.0000Testowanie hipotezy o istnieniu efektw indywidualnych sprowadza si do testowania hipotezy parametrycznej, e H0 : 2u= 0. Jeli hipoteza zerowa jest prawdziwa, to ui = 0 i liniowy model efektw nieobserwowalnych sprowadza si do modelu speniajcego zaoenia KMRL. W takim przypadku lepiej jest zamiast estymatora efektw losowych uy zwykego estymatora MNK. Jeli hipoteza zerowa zostanie odrzucona, wnioskujemy, e efekty indywidualne s istotne w modelu i w konsekwencji efektywniejszym estymatorem jest estymator efektw losowych.

  • Estymator efektw staych

    Zakadamy: efekty indywidualne mog by skorelowane ze zmiennymi objaniajcymi.

    Formujc model z czynnikami staymi zakada si, e rnice midzy jednostkami mog by uchwycone poprzez rnice w wyrazie wolnym. Uwzgldniony zostanie w ten sposb wpyw wszystkich zmiennych w czasie czynnikw, specyficznych dla kadej jednostki.

  • xtreg ltrade lrgdp lpop ldist cu comcol comctry comlang colonial regional ll island, fe

    Fixed-effects (within) regression Number of obs = 31226Group variable (i): pairid Number of groups = 7961R-sq: within = 0.0500 Obs per group: min = 1 between = 0.0376 avg = 3.9 overall = 0.0387 max = 6

    F(7,23258) = 175.01corr(u_i, Xb) = -1.0000 Prob > F = 0.0000------------------------------------------------------------------------------ ltrade | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- lrgdp | .773943 .0229327 33.75 0.000 .7289934 .8188926 lpop | -1.156388 .0484071 -23.89 0.000 -1.25127 -1.061507 ldist | 57609.99 86240.24 0.67 0.504 -111426.6 226646.6 cu | -.8935846 .5480874 -1.63 0.103 -1.967872 .1807029 comcol | (dropped) comctry | .6481806 .3340995 1.94 0.052 -.0066765 1.303038 comlang | -.1906378 .4885926 -0.39 0.696 -1.148312 .767036 colonial | (dropped) regional | -.0303829 .09633 -0.32 0.752 -.2191961 .1584303 ll | (dropped) island | (dropped) _cons | -472221.8 706906.1 -0.67 0.504 -1857804 913360.8-------------+---------------------------------------------------------------- sigma_u | 46551.107 sigma_e | 1.2885209 rho | 1 (fraction of variance due to u_i)------------------------------------------------------------------------------F test that all u_i=0: F(7960, 23258) = 7.54 Prob > F = 0.0000

  • Test Hausmana

  • Test Hausmanahausman . random

    ---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | . random Difference S.E.-------------+---------------------------------------------------------------- lrgdp | .773943 .8973097 -.1233667 .0190103 lpop | -1.156388 -.2821761 -.8742123 .0457821 ldist | 57609.99 -1.189486 57611.18 86240.24 cu | -.8935846 .4350876 -1.328672 .5077335 comctry | .6481806 .8194063 -.1712257 .1881105 comlang | -.1906378 .4530111 -.6436489 .4832225 regional | -.0303829 .1496601 -.1800429 .0348817------------------------------------------------------------------------------ b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg

    Test: Ho: difference in coefficients not systematic

    chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.45 Prob>chi2 = 0.5041

  • Stay i specyficzny efekt zwizany z momentem obserwacjixi:xtreg ltrade cu ldist lrgdp lpop comlang regional comcol comctry colonial ll island i.yearRandom-effects GLS regression Number of obs = 31226Group variable (i): pairid Number of groups = 7961R-sq: within = 0.0976 Obs per group: min = 1 between = 0.6625 avg = 3.9 overall = 0.6306 max = 6

    Random effects u_i ~ Gaussian Wald chi2(16) = 18430.59corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000------------------------------------------------------------------------------ ltrade | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- cu | 1.112545 .1928022 5.77 0.000 .7346598 1.490431 ldist | -1.244146 .0277457 -44.84 0.000 -1.298526 -1.189765 lrgdp | 1.326247 .0139995 94.74 0.000 1.298808 1.353685 lpop | -.5045464 .0152204 -33.15 0.000 -.5343778 -.474715 comlang | .4946473 .0673684 7.34 0.000 .3626077 .6266869 regional | .4599003 .0839523 5.48 0.000 .2953569 .6244437 comcol | .3828931 .0806516 4.75 0.000 .2248189 .5409672 comctry | .8659752 .2577852 3.36 0.001 .3607254 1.371225 colonial | 2.1163 .2044209 10.35 0.000 1.715643 2.516958 ll | -.332329 .0517973 -6.42 0.000 -.4338499 -.2308081 island | .3248042 .0405341 8.01 0.000 .2453589 .4042495 _Iyear_75 | -.1748795 .0263709 -6.63 0.000 -.2265656 -.1231935 _Iyear_80 | -.5083348 .027008 -18.82 0.000 -.5612696 -.4554 _Iyear_85 | -1.26094 .0275015 -45.85 0.000 -1.314842 -1.207038 _Iyear_90 | -1.435306 .0301363 -47.63 0.000 -1.494372 -1.376239 _Iyear_95 | -1.619971 .0328153 -49.37 0.000 -1.684288 -1.555654 _cons | -16.5935 .3599419 -46.10 0.000 -17.29897 -15.88803-------------+---------------------------------------------------------------- sigma_u | 1.7204213 sigma_e | 1.2527262 rho | .65350779 (fraction of variance due to u_i)------------------------------------------------------------------------------

  • Test Resetxtreg ltrade lrgdp lpop ldist cu comcol comctry comlang colonial regional ll island fit2 fit3Random-effects GLS regression Number of obs = 31226Group variable (i): pairid Number of groups = 7961R-sq: within = 0.0317 Obs per group: min = 1 between = 0.6496 avg = 3.9 overall = 0.5859 max = 6

    Random effects u_i ~ Gaussian Wald chi2(13) = 12864.82corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000------------------------------------------------------------------------------ ltrade | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- lrgdp | -1.244214 .1579944 -7.88 0.000 -1.553877 -.9345503 lpop | .4002097 .052442 7.63 0.000 .2974252 .5029942 ldist | 1.661203 .2100177 7.91 0.000 1.249576 2.07283 cu | -.518542 .2159019 -2.40 0.016 -.9417019 -.0953821 comcol | .25018 .089645 2.79 0.005 .0744789 .425881 comctry | -1.167509 .3103585 -3.76 0.000 -1.7758 -.5592171 comlang | -.6391032 .106948 -5.98 0.000 -.8487174 -.429489 colonial | -3.703678 .4993154 -7.42 0.000 -4.682318 -2.725038 regional | -.1262502 .0926495 -1.36 0.173 -.3078399 .0553395 ll | .9430459 .1295693 7.28 0.000 .6890947 1.196997 island | -.0523915 .0425347 -1.23 0.218 -.135758 .030975 fit2 | 70.97246 5.468811 12.98 0.000 60.25379 81.69113 fit3 | -39.28324 3.270625 -12.01 0.000 -45.69355 -32.87294 _cons | 16.43129 1.755942 9.36 0.000 12.9897 19.87287-------------+---------------------------------------------------------------- sigma_u | 1.7079146 sigma_e | 1.2866771 rho | .63793676 (fraction of variance due to u_i)------------------------------------------------------------------------------

  • Badanie resztSkewness -0.3893672Kurtosis 4.927893

  • Reszty musz by symetryczne wzgldem wartoci dopasowanych

  • Zbilansowanie paneluxtreg ltrade lrgdp lpop ldist cu comcol comctry comlang colonial regional ll island Random-effects GLS regression Number of obs = 14424Group variable (i): pairid Number of groups = 2404R-sq: within = 0.0513 Obs per group: min = 6 between = 0.7276 avg = 6.0 overall = 0.6180 max = 6Random effects u_i ~ Gaussian Wald chi2(11) = 5862.81corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000------------------------------------------------------------------------------ ltrade | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- lrgdp | .8404294 .0167743 50.10 0.000 .8075524 .8733064 lpop | -.3204637 .0215761 -14.85 0.000 -.3627519 -.2781754 ldist | -1.063617 .0364111 -29.21 0.000 -1.134982 -.9922529 cu | .7274824 .6068931 1.20 0.231 -.4620062 1.916971 comcol | -.2370708 .1360379 -1.74 0.081 -.5037002 .0295586 comctry | .1838613 .6467118 0.28 0.776 -1.083671 1.451393 comlang | .2525556 .0800819 3.15 0.002 .095598 .4095132 colonial | 2.041308 .2103445 9.70 0.000 1.62904 2.453575 regional | .1451494 .0833236 1.74 0.082 -.0181618 .3084607 ll | -.3362375 .0781497 -4.30 0.000 -.489408 -.183067 island | .015717 .0529004 0.30 0.766 -.0879659 .1193998 _cons | -4.275765 .459591 -9.30 0.000 -5.176546 -3.374983-------------+---------------------------------------------------------------- sigma_u | 1.1760032 sigma_e | 1.0466633 rho | .55799494 (fraction of variance due to u_i)------------------------------------------------------------------------------

  • Grupowa heteroskedastycznoDla danych panelowych przyjmuje si GROUPWISE HETEROSCEDATICITY czyli za. wariancja skadnika losowego jest staa dla danej jednostki, natomiast moe si rni si pomidzy jednostkami.

  • TEST LM nie jest odporny na brak normalnoci resztsum LM p_value

    Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- LM | 14424 12412.06 0 12412.06 12412.06 p_value | 14424 0 0 0 0

  • TEST skorygowany WALDA jest odporny na brak normalnoci resztsum W p_value1

    Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- W | 14424 33.10704 166.5529 3.26e-07 3038.012 p_value1 | 14424 .9981855 .038677 8.81e-19 1

  • Test na czn istotno efektw losowych i istnienie autokorelacji 1 rzdu dla modelu REXttest1Joint Test:LM(Var(u)=0,rho=0) = 8562.63 Pr>chi2(2) = 0.0000

    W modelu wystpuj efekty losowe i autokorelacja 1 rzdu.