Dear Stata users, I have a question to ask concerning latent class analysis (LCA). I am working with a sample of 57 American cities. I am using a total of 83 city-level variables to place my 57 cities into a set of latent classes (I am considering first using exploratory factor analysis to aggregate my 83 city-level variables into 7 or so factors/indexes). My goal is to study how different latent classes of American cities are associated with the overall happiness of city residents. I am concerned that my sample size of 57 cities might be inadequate to develop a stable set of latent classes. I would very much appreciate it if anyone could offer me advice concerning whether an LCA can be done with a sample of 57. Might it be the case that I should limit the number of latent classes I develop to a certain number?

I am aware of some of the techniques (like BIC) used to establish what is the best number of latent classes to develop with LCA. I’m wondering if there is any way to get a sense of whether the outcomes of these techniques are stable and accurate.

I plan on doing LCA with Stata 15's 'gsem lca.'

I would very much appreciate your help, thank you in advance!

Yours sincerely,
Jason Settels

Hello everyone,

I am working with the following model -

_Yi = β₁ + β₂ Xi + β₃ Z_i + β₄ P_i + β₅ Q_i + β₆ S_i + ε_i

where Y is a binary dependent variable and among the independent variables - only X is continuous and the rest are dummies (Z, P, Q and S). I am working with Linear Probability Model, Logit and Probit model.

What I am trying to do is

a) plot the fitted values of X using its coefficients from LPM model. Here all of the dummies will be at their respective averages. Next in the same plane, I want to plot X again using its coefficient from LPM but here only one of the of the dummies will take the value of 1 (i.e. only Z) and rest will be at their averages (P, Q and S).

b) Next in a similar manner, I would like to plot the fitted values of X using its marginal effects from Logit model. Here again all of the dummies will be at their respective averages. Next in the same plane, I want to plot X using its marginal effects from Logit but similarly here, only one of the of the dummies will take the value of 1 (i.e. only Z) and rest will be at their respective averages (P, Q and S).

Can anybody please help me with the commands? I am using STATA 14.2.

Thank you very much.

Hi all, I want to apply the seasonal adjusted method on some time series from fred data. Is there any material about it?

I know there are some useful method as the link below.

http://www.abs.gov.au/websitedbs/d33...8!OpenDocument.

But how to apply the same method as that used by Fred? And how to realize it use stata?

Thanks.

Dear Statalist Community

This should be my very last questions for statalist community. Thank you every experts who have helped me this far. I appreciate it a lot.

I am trying to explain the reason why the correlation between my dependent variable (government expenditure as a % of GDP) and independent variable (immigration) is negatively correlated, while the estimated coefficients are positive (in all cases when I include or exclude each control variables.)

1. I try to investigate by using and found no outliers. I wonder whether there are other ways in which I can find the reason for this?
2. I also experiment with another regression that use log on all of my variables. I would like to know whether I can interpret that in terms of one percentage change in standard deviation? eg. one standard deviation in independent variable contributes to ___% of one standard deviation increase increase in dependent variable.




+------------------------+
| obs: govex_~p immi |
|------------------------|
| 901. 10.20876 . |
| 586. 10.2687 . |
| 585. 10.3964 . |
| 902. 10.46668 . |
| 722. 10.53066 . |
+------------------------+

+-------------------------+
| 879. 27.4907 61872 |
| 871. 27.63227 32272 |
| 369. 27.68548 . |
| 182. 27.69099 28223 |
| 205. 27.93502 51800 |
+-------------------------+

regression without log

Fixed-effects (within) regression Number of obs = 657
Group variable: country Number of groups = 33

R-sq: within = 0.2989 Obs per group: min = 2
between = 0.0395 avg = 19.9
overall = 0.0627 max = 29

F(32,32) = .
corr(u_i, Xb) = -0.7913 Prob > F = .

(Std. Err. adjusted for 33 clusters in country)
----------------------------------------------------------------------------------
| Robust
govex_gdp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
immi | 1.14e-06 5.01e-07 2.28 0.029 1.24e-07 2.16e-06
depratio | .0329021 .0603795 0.54 0.590 -.0900868 .1558911
unem_perlab | .1200443 .0409341 2.93 0.006 .0366643 .2034244
urbanpop_pertot | .2526613 .0645373 3.91 0.000 .1212032 .3841194
pop | 7.90e-08 1.57e-07 0.50 0.618 -2.40e-07 3.98e-07
femalepop_pertot | -.9128621 1.047256 -0.87 0.390 -3.046053 1.220329
|
year |
1982 | -.0275117 .1441005 -0.19 0.850 -.3210348 .2660114
1983 | .0287356 .3412937 0.08 0.933 -.666457 .7239282
1984 | -.672402 .4074508 -1.65 0.109 -1.502352 .1575481
1989 | -1.164444 .5968829 -1.95 0.060 -2.380255 .0513666
1990 | -.7825131 .6641094 -1.18 0.247 -2.13526 .5702335
1991 | -.3335672 .7901333 -0.42 0.676 -1.943016 1.275882
1992 | -.4937047 .800642 -0.62 0.542 -2.124559 1.13715
1993 | -.1212492 .7635012 -0.16 0.875 -1.67645 1.433952
1994 | -.8561286 .7242583 -1.18 0.246 -2.331394 .6191373
1995 | -.5426904 .8170002 -0.66 0.511 -2.206865 1.121485
1996 | -.8923355 .8015554 -1.11 0.274 -2.52505 .7403794
1997 | -1.156174 .8138863 -1.42 0.165 -2.814007 .5016577
1998 | -1.249869 .8070755 -1.55 0.131 -2.893828 .3940899
1999 | -1.177472 .8301466 -1.42 0.166 -2.868425 .5134814
2000 | -1.617467 .8595532 -1.88 0.069 -3.36832 .1333854
2001 | -1.411849 .8676217 -1.63 0.113 -3.179137 .3554382
2002 | -1.143768 .8905438 -1.28 0.208 -2.957747 .6702102
2003 | -.8068445 .9449567 -0.85 0.400 -2.731658 1.117969
2004 | -1.323295 .8930714 -1.48 0.148 -3.142422 .4958319
2005 | -1.32398 .9220267 -1.44 0.161 -3.202087 .5541268
2006 | -1.543219 .9286263 -1.66 0.106 -3.434768 .3483313
2007 | -1.860672 .9506458 -1.96 0.059 -3.797074 .0757304
2008 | -1.130692 .9602998 -1.18 0.248 -3.086759 .8253748
2009 | .1310738 .9863769 0.13 0.895 -1.87811 2.140258
2010 | -.3822114 .9845546 -0.39 0.700 -2.387684 1.623261
2011 | -1.07942 1.007688 -1.07 0.292 -3.132013 .9731725
2012 | -1.391096 1.023083 -1.36 0.183 -3.475048 .6928561
2013 | -1.530977 1.035502 -1.48 0.149 -3.640225 .5782712
|
_cons | 44.80689 54.43654 0.82 0.417 -66.0767 155.6905
-----------------+----------------------------------------------------------------
sigma_u | 4.7367037
sigma_e | 1.38224
rho | .92152662 (fraction of variance due to u_i)
----------------------------------------------------------------------------------

regression with log



Thank you
Plai

[CODE]
* Example generated by -dataex-. To install: ssc install dataex
clear
input str10 RegionName str16(B C) str17(D E F) str16(G H I J K L)
"City" "San Francisco" "Pittsburg" "Alameda" "San Francisco" "Fremont" "Hayward" "Antioch" "Fremont" "Hayward" "Union City" "Brentwood"
"State" "CA" "CA" "CA" "CA" "CA" "CA" "CA" "CA" "CA" "CA" "CA"
"CountyName" "San Francisco" "Contra Costa" "Alameda" "San Francisco" "Alameda" "Alameda" "Contra Costa" "Alameda" "Alameda" "Alameda" "Contra Costa"
"2010-01" "" "" "" "" "" "" "" "" "" "" ""
"2010-02" "" "" "" "" "" "" "" "" "" "" ""
"2010-03" "" "" "" "" "" "" "" "" "" "" ""
"2010-04" "" "" "" "" "" "" "" "" "" "" ""
"2010-05" "" "" "" "" "" "" "" "" "" "" ""
"2010-06" "" "" "" "" "" "" "" "" "" "" ""
"2010-07" "" "" "" "" "" "" "" "" "" "" ""
"2010-08" "" "" "" "" "" "" "" "" "" "" ""
"2010-09" "" "" "" "" "" "" "" "" "" "" ""
"2010-10" "" "" "" "" "" "" "" "" "" "" ""
"2010-11" "" "" "" "" "" "" "" "" "" "" ""
"2010-12" "" "" "" "" "" "" "" "" "" "" ""
"2011-01" "" "" "" "" "" "" "" "" "" "" ""
"2011-02" "" "" "" "" "" "" "" "" "" "" ""
"2011-03" "" "" "" "" "" "" "" "" "" "" ""
"2011-04" "" "" "" "" "" "" "" "" "" "" ""
"2011-05" "" "" "" "" "" "" "" "" "" "" ""
"2011-06" "" "" "" "" "" "" "" "" "" "" ""
"2011-07" "" "" "" "" "" "" "" "" "" "" ""
"2011-08" "" "" "" "" "" "" "" "" "" "" ""
"2011-09" "" "" "" "" "" "" "" "" "" "" ""
"2011-10" "" "" "" "" "" "" "" "" "" "" ""
"2011-11" "" "" "" "" "" "" "" "" "" "" ""
"2011-12" "" "" "" "" "" "" "" "" "" "" ""
"2012-01" "" "" "" "" "" "" "" "" "" "" ""
"2012-02" "" "" "" "" "" "" "" "" "" "" ""
"2012-03" "" "" "" "" "" "" "" "" "" "" ""
"2012-04" "" "" "" "" "" "" "" "" "" "" ""
"2012-05" "" "" "" "" "" "" "" "" "" "" ""
"2012-06" "" "" "" "" "" "" "" "" "" "" ""
"2012-07" "" "" "" "" "" "" "" "" "" "" ""
"2012-08" "" "" "" "" "" "" "" "" "" "" ""
"2012-09" "" "" "" "" "" "" "" "" "" "" ""
"2012-10" "" "" "" "" "" "" "" "" "" "" ""
"2012-11" "" "" "" "" "" "" "" "" "" "" ""
"2012-12" "" "" "" "" "" "" "" "" "" "" ""
"2013-01" "" "" "" "" "" "" "" "" "" "" ""
"2013-02" "" "" "" "" "" "" "" "" "" "" ""
"2013-03" "" "" "" "" "" "" "" "" "" "" ""
"2013-04" "" "" "" "" "" "" "" "" "" "" ""
"2013-05" "" "" "" "" "" "" "" "" "" "" ""
"2013-06" "" "" "" "" "" "" "" "" "" "" ""
"2013-07" "" "" "" "" "" "" "" "" "" "" ""
"2013-08" "" "" "" "" "371.6906292702" "" "" "" "" "" ""
"2013-09" "" "" "" "" "373.953695458593" "" "" "" "" "" ""
"2013-10" "" "" "386.804657179819" "730.366492146597" "378.995578016425" "" "" "" "" "" ""
"2013-11" "" "" "383.534074276216" "699.85" "378.872145332032" "" "" "386.597257209507" "263.189448441247" "310.735346358792" "189.298334174659"
"2013-12" "" "176.784523015344" "386.029411764706" "714.285714285714" "377.06228956229" "" "164.961496149615" "390.354454810399" "263.617196130736" "322.060353798127" "191.140278917145"
"2014-01" "" "171.554959785523" "397.631790670588" "707.674101782581" "372.614512238174" "" "155.732087227414" "391.236306729264" "271.340009537434" "331.618334892423" "190.831426392067"
"2014-02" "" "169.648365206663" "404.293533734589" "718.171296296296" "387.972356935015" "" "155.805806920079" "385.990338164251" "276.992936427851" "333.333333333333" "193.467336683417"
"2014-03" "" "174.539282250242" "378.936847466259" "729.115218115218" "387.859676783603" "" "160.48951048951" "385.759107397665" "268.853367277789" "338.898305084746" "194.083859850661"
"2014-04" "" "184.390547263682" "422.223914530671" "754.570333880679" "402.41935483871" "" "168.531468531469" "411.089513810986" "279.500880559636" "342.175170775171" "198.400210423242"
"2014-05" "" "189.420921997551" "413.393483709273" "801.0241404535479" "418.795180722892" "" "169.092772936955" "422.419179874241" "286.170212765957" "345.493827160494" "199.066689584049"
"2014-06" "" "197.202797202797" "404.24388600355" "783.871954619143" "428.703803839763" "" "167.806417674908" "416.411512553582" "288.288288288288" "348.37752668635" "200.389865374804"
"2014-07" "" "193.097781429745" "429.736486692324" "789.126853377265" "424.671385237614" "" "172.413793103448" "432.318145814913" "278.079303384603" "359.810658271937" "202.242227040808"
"2014-08" "" "196.365767878077" "408.176100628931" "843.253588516747" "422.958397534669" "" "166.945373467113" "435.344585145163" "282.347900599829" "365.378573479349" "197.359735973597"
"2014-09" "" "194.791252485089" "402.576489533011" "785.227867993714" "431.100033380242" "" "169.617863228926" "448.860497237569" "297.783933518006" "369.451697127937" "199.716048485037"
"2014-10" "" "192.81122150789" "402.220493313746" "854.80093676815" "435.940432962279" "" "168.970814132104" "445.790816326531" "295.880149812734" "364.927642322203" "203.959208158368"
"2014-11" "" "185.945273631841" "419.023821411134" "805.790960451977" "427.297008547009" "" "164.808531265148" "438.50138121547" "292.071846589863" "357.322176583025" "201.927489674162"
"2014-12" "" "186.656671664168" "406.577375810341" "837.8746594005451" "425.415663937602" "307.484915844771" "162.818327380908" "437.57292882147" "312.306709620084" "367.705662056305" "200.653158997376"
"2015-01" "" "193.535514764565" "423.558897243108" "776.595744680851" "431.912451690024" "309.980161190329" "172.79012345679" "442.225392296719" "298.181818181818" "368.889097486658" "201.346210125841"
"2015-02" "906.297513256819" "194.843827466534" "427.254171186289" "848.809888874687" "464.674789390549" "322.840847736794" "174.492385786802" "450.762829403606" "303.843807199512" "371.71506612996" "205.022060731897"
"2015-03" "959.023539668701" "194.843827466534" "446.208340503642" "863.321799307958" "484.76213592233" "331.269349845201" "178.965688712084" "458.894638598229" "313.401253918495" "372.843229763429" "212.486840718335"
"2015-04" "957.002457002457" "202.411714039621" "430.94944512947" "870.047164164811" "500" "343.381836945304" "175.636586806239" "476.472891445656" "323.897659227001" "382.424590332357" "206.877729257642"
"2015-05" "1016.94915254237" "203.729281767956" "462.365591397849" "856.470588235294" "503.46565847511" "339.7365532382" "180.684104627767" "490.181268882175" "325.885452509695" "385.740888534185" "213.810284898028"
"2015-06" "1017.998385795" "207.166388162177" "466.810333057227" "812.244897959184" "493.738334932121" "334.545454545455" "184.977081760334" "487.350104275287" "324.976787372331" "394.112005088999" "215.606788344541"
"2015-07" "1029.92311507937" "206.737400530504" "468.936384342391" "796.688660801564" "482.264218395367" "343.51106870229" "184.229918938836" "491.690382288352" "321.766848816029" "400.235849056604" "216.852207293666"
"2015-08" "1004.02252614642" "206.068329718004" "469.89247311828" "794.318181818182" "484.675577156744" "349.201596806387" "187.16577540107" "490.526315789474" "319.256756756757" "401.206636500754" "217.707736389685"
"2015-09" "998.688311688312" "205.273364485981" "470.028544243578" "828.8218267581479" "497.301587301587" "347.380410022779" "187.16577540107" "489.653943906717" "331.095725466586" "394.153791847953" "218.340611353712"
"2015-10" "1033.91254897005" "209.252286175363" "481.768087058274" "891.950565492655" "502.515371716042" "350.145350949868" "187.713310580205" "495.600991325898" "328.518744714836" "400" "217.468967777983"
"2015-11" "1031.58929976397" "209.425349087003" "480.540697105016" "915.857605177993" "508.861183386902" "359.078048780488" "186.519458332505" "492.700729927007" "329.153605015674" "405.130946018172" "220.487179487179"
"2015-12" "1061.87929717341" "208.180466038671" "472.706480304956" "915.857605177993" "509.668508287293" "362.662018746006" "184.234791500305" "496.614569536424" "322.041601323253" "405.873493975904" "220.522052205221"
"2016-01" "1046.69887278583" "211.231441165675" "472.706480304956" "915.857605177993" "486.551063066203" "369.061414989994" "185.542168674699" "487.630117163796" "319.444444444444" "403.097947214076" "214.993537268419"
"2016-02" "1061.87929717341" "221.609937758796" "472.706480304956" "934.511434511434" "495.763221153846" "373.788995112133" "188.118279569892" "509.425036390102" "329.275280898876" "411.37752703338" "223.720782791515"
"2016-03" "1080.2895495447" "222.42374278648" "490.171568627451" "939.985568693098" "507.894736842105" "386.42825607064" "193.342541436464" "530.503978779841" "343.234323432343" "414.746176720476" "222.417295424526"
"2016-04" "1034.90601193931" "224.032520325203" "486.425666516042" "960.802386875148" "513.265157919433" "386.189845474614" "193.342541436464" "535.140934519158" "342.372165112336" "422.261359633674" "224.692717996289"
"2016-05" "1048.75567269659" "222.589315525876" "486.140350877193" "942.349137931035" "521.048501417578" "385.507246376812" "196.477018964324" "537.5" "343.804233242266" "423.718220338983" "227.678571428571"
"2016-06" "1080.75040783034" "229.310344827586" "486.619997502653" "962.439732950726" "522.904871017814" "387.043189368771" "200.680272108844" "533.992861156913" "352.77945538662" "425.749545515057" "229.206963249516"
"2016-07" "1064.64123272746" "228.699551569507" "491.175072025957" "966.684578183772" "515.277108433735" "382.022471910112" "206.278095443593" "544.122448979592" "354.325913472183" "423.718220338983" "231.75355450237"
"2016-08" "1130.18867924528" "229.681978798587" "493.555568734356" "934.195064629847" "516.173434273916" "391.240310077519" "207.614523721709" "555.681653293468" "359.73597359736" "424.430641821946" "233.60462223467"
"2016-09" "1099.65237543453" "230.066445182724" "499.926470588235" "914.547304170905" "521.016920481897" "386.297376093294" "208.333333333333" "530.453257790368" "362.020275162925" "435.150310559006" "233.583076245042"
"2016-10" "1078.72823618471" "233.606557377049" "490.196078431373" "888.956494134319" "514.955141820725" "401.702370100273" "212.36689861533" "537.634408602151" "360.547645391153" "427.563942307692" "236.909202503888"
"2016-11" "1115.24163568773" "236.71160517187" "493.243853505217" "893.538675312645" "513.148343734998" "405.003470615456" "214.626865671642" "538.692026491687" "363.12984496124" "429.875608014297" "238.121296813863"
"2016-12" "1160.86235489221" "235.810810810811" "506.012065637066" "908.964237516869" "521.0173913043481" "409.429280397022" "211.86895810956" "554.112554112554" "363.12984496124" "427.062534038631" "237.137857589941"
"2017-01" "1151.05521198953" "233.59375" "526.77570549751" "910.521955260978" "520.5985014175779" "403.299398426654" "216.493594394031" "563.61316246742" "362.622036262204" "431.989366415596" "234.370855806788"
"2017-02" "1133.1775108949" "239.427860696517" "522.137486573577" "916.3720887416" "519.677002413869" "410.606200832948" "219.61252446184" "549.539170506912" "369.02530183727" "436.437902684685" "231.824610076971"
"2017-03" "1113.00688282611" "245.429977898332" "522.5840336134449" "984.001704692105" "536.950570117235" "393.143280266568" "227.272727272727" "551.283987915408" "372.334834834835" "435.391887349367" "234.220135628586"
"2017-04" "1116.95632218685" "248.607657881651" "549.215406562054" "1036.37413394919" "550.22392834293" "407.349665924276" "235.940660822657" "570.698466780239" "371.25340599455" "445.745271938207" "238.397965670693"
"2017-05" "1157.14285714286" "248.756218905473" "524.449911543565" "1034.48275862069" "555.1260977925469" "407.663316582915" "237.268396402263" "579.72027972028" "372.83017707821" "452.505966587112" "239.068549540235"
"2017-06" "1157.14285714286" "249.44099378882" "547.852495772991" "1011.24984979188" "555.1260977925469" "410.5743201256" "236.418918918919" "569.337216738352" "381.528776791873" "453.703703703704" "236.014917421417"
"2017-07" "1130.27862660216" "251.384888212653" "557.182705718271" "1014.6898146523" "556.082648113009" "386.735982750627" "236.742424242424" "591.232227488152" "390.004228281814" "465.502905266255" "237.187043493034"
"2017-08" "1092.71555538763" "251.831501831502" "549.903870498133" "1020.10644589001" "560.729716754681" "386.840202458424" "241.619585687382" "586.492890995261" "383.087765957447" "475.805041876571" "242.389515219842"
"2017-09" "1077.44107744108" "254.237288135593" "565.723270440252" "1000.9765625" "558.3850931677021" "391.509037503649" "241.619585687382" "593.791281373844" "382.671129846977" "476.417910447761" "249.508709245199"
"2017-10" "1139.59731543624" "261.343804537522" "571.739130434783" "1025.02662406816" "570.807453416149" "402.106821787414" "241.964827167981" "596.198156682028" "390.791428571429" "476.417910447761" "251.747387523745"
"2017-11" "1250.41868206168" "261.953204476094" "562.781456953642" "1050.8875739645" "578.918995130222" "398.204653622422" "238.095238095238" "629.62962962963" "404.368358913813" "482.955414012739" "252.96788842037"
"2017-12" "1233.18744339931" "265.432098765432" "565.595873163629" "1033.77425044092" "580.8080808080809" "391.791044776119" "250.446630888491" "614.285714285714" "405.672823218997" "489.038792922288" "251.817775792765"
"2018-01" "1231.63606010017" "265.939597315436" "567.6421412981" "1065.08875739645" "592.429123301946" "398.204653622422" "255.567928730512" "592.384519350812" "417.145137681292" "500.779626834864" "250.883179446006"
end



Here's the data that might help the explanation of the question. I'm looking to rename the variables B with San Francisco and C Pittsburgh - while keeping value label the same (zip code). Also of equal importance I'd need to have the City Name dropped as an observation. Or is there any way to run a time series but set TSSET to exclude observations (1/5) for instance? This way each additional column cell 1/5 that is string information regarding the City/Metropolitan within the column -- isn't instead interpreted as a (delta t) time series.

As it stands I have to drop all the observations that are words except for the single one I wan't to make the variable name.

PS: Is there a command for making the 1st observations of each column the varname directly? This workaround would at least be one solution.

Thanks for the time and any help.

Hi statalists!
I have a question for the syntax for random coefficient model. The data is one year data.
I would like to see the returns to education by age using random coefficient model.

I have variables: ttlwages (total wages during last 30 days), edu (education level), age (age), age_squ (the square of age).

I have tried

code: mixed ttlwagesedu ||age: edu

But I could not get the result by age. How can I get the returns to education by age?
I would appreciate if you could any comments on this. Thank you in advance.

Hi guys,
i'm using STATA 13 to estimate my dyamic panel model. The aim of my thesis is to understand if globalization affects economic growth. I start from a Fixed effects model and I want to spve the endogeneity problem. Since i use a growth model, someone suggests me to use a dynamic one. I use a 5-years panel and the variables are the average over the 5 years. My dependent variable is the GDP growth rate and the regressors are different globalization indices that I used one by one, plus several macroeconomic variables used like controls, I show you an example with one of them. I add time dummies in order to take into account time effects (I add them one by one as suggested in this Forum).

GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob are the endogenous variables
newlsc lnLife_5 are the predetermined variables

I have the following questions:

1 - I tried to avoid the proliferation of instruments using only two lags but I obtain a warning and I don't know if it is related to this problem.
2 - I tried to change globalization indices but the GDP lagged is never significant. This may suggest that a static model using instrumental variables like xtivreg2 (but in this case I have to fins instruments) may better fit to my data to solve the endogeneity problem? Or alternatively, it might make sense to run an AB model without the dependent lagged variable to make it a static model?

I would appreciate some feedbacks (also to my AB model, I'm not 100% sure I've used the AB options well)
Thank you!

Dear All,
I have a few queries with regard to my dataset which is unbalanced and contains data for T=54 years andN=41 countries.
My Pooled OLS and FE regressions are:

reg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum)

xtreg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum) fe

I have undertaken a fisher unit root test for panel data and have found that all variables are non stationary.

My questions are as follows:

1. What will the effect be of non-stationary variables included? Will my coefficients be biased or is it that I am simply worried about spurious relationships?
2. Is T=54 too small for me to worry about non-stationarity/ Has my ADF falsely found non-stationarity as a result of this small T? The reason I worry about this is because looking at the panel graphs for my main variables [ln(suicide) and l.RelativeGDP], most panels look stationary. Are my results driven by certain countries? Should I believe my ADF results?

1. However, when I take the first differences of these variables I do agree that they become more stationary so perhaps taking the first differences is the solution to this issue?

1. Can Fixed effects overcome the issue of non-stationarity? If so, how?

Thank you for your guidance,

Is it possible to query stata from another process, preferably over a well known protocol (http, odbc, etc.); that is, pretty much using stata as a database and query it remotely.

Is there a Mata version of Stata's commands -stsplit- and -stjoin-?
I need to split survival data within Mata.

Hi,

I'm trying to regress with time fixed effects, so I first used the command "xtset statename year" but it went red saying, "repeated time values within panel". I have no idea how to fix this. I tried using the command duplicates which showed that there are a lot of duplicates, but I don't know how to fix this without removing any of the observations.

duplicates report statename year

Duplicates in terms of statename year

--------------------------------------
copies | observations surplus
----------+---------------------------
1 | 75 0
2 | 118 59
3 | 81 54
4 | 136 102
5 | 135 108
6 | 72 60
7 | 154 132
8 | 152 133
9 | 135 120
10 | 220 198
11 | 176 160
12 | 204 187
13 | 247 228
14 | 336 312
15 | 240 224
16 | 256 240
17 | 221 208
18 | 216 204
19 | 323 306
20 | 300 285
21 | 357 340
22 | 154 147
23 | 253 242
24 | 264 253
25 | 225 216
26 | 286 275
27 | 135 130
28 | 140 135
29 | 290 280
30 | 300 290
31 | 372 360
32 | 192 186
33 | 297 288
34 | 272 264
35 | 140 136
36 | 144 140
37 | 222 216
38 | 228 222
39 | 195 190
40 | 400 390
41 | 369 360
42 | 252 246
43 | 172 168
44 | 264 258
45 | 225 220
46 | 46 45
47 | 141 138
48 | 144 141
49 | 147 144
50 | 250 245
51 | 153 150
52 | 104 102
53 | 212 208
54 | 162 159
55 | 165 162
56 | 112 110
57 | 114 112
58 | 232 228
59 | 177 174
60 | 120 118
61 | 427 420
62 | 186 183
63 | 63 62
65 | 65 64
66 | 198 195
67 | 134 132
68 | 136 134
69 | 138 136
70 | 70 69
71 | 142 140
73 | 219 216
74 | 148 146
75 | 150 148
76 | 76 75
77 | 77 76
78 | 234 231
81 | 162 160
82 | 328 324
83 | 166 164
85 | 85 84
93 | 186 184
96 | 96 95
97 | 97 96
99 | 99 98
100 | 200 198
103 | 103 102
104 | 208 206
105 | 105 104
108 | 108 107
111 | 111 110
119 | 119 118
121 | 121 120
128 | 128 127
--------------------------------------

So I am almost finished with my research between Premiership Parties and Governing Parties without Premiership, and at my final command I get an error message called "invalid 'replace' "

use MPDataset_MPDS2017b_stata14, clear
gen ptype = 0
replace ptype = 0 if party == 11320 | party == 11420 ///
| party == 11620 | party == 11810 | party == 13320 | party == 13410 ///
| party == 13420 | party == 13620 | party == 14320 | party == 14620 ///
| party == 14810 | party == 21320 | party == 21322 | party == 21421 ///
| party == 21422 | party == 21423 | party == 21425 | party == 21426 ///
| party == 21521 | party == 22320 | party == 22521 | party == 22522 ///
| party == 22722 | party == 23420 | party == 23520 | party == 33210 ///
| party == 33610 | party == 34020 | party == 34212 | party == 34313 ///
| party == 34511 | party == 35311 | party == 35313 | party == 35520 ///
| party == 41320 | party == 41521 | party == 42320 | party == 42520 ///
| party == 51320 | party == 51620 | party == 53520 | party == 53620 ///
replace ptype = 1 if party == 11110 | party == 11520 ///
| party == 13001 | party == 13230 | party == 14110 | party == 14222 ///
| party == 14223 | party == 14520 | party == 14820 | party == 14901 ///
| party == 21111 | party == 21112 | party == 21321 | party == 21522 ///
| party == 21913 | party == 21916 | party == 22330 | party == 22526 ///
| party == 22528 | party == 23113 | party == 23320 | party == 34213 ///
| party == 34710 | party == 34730 | party == 41111 | party == 41112 ///
| party == 41113 | party == 41420 | party == 42420 | party == 42710 ///
| party == 51420 | party == 51421 | party == 53110 | party == 53320 ///
label variable ptype "party type"
label define ptype 0 "Premiership Parties" 1 "Governing Non-Premiership Parties"
label values ptype ptype

here, the "replace" is 'invalid'.

However, the following command I did earlier works without any problems:

keep if date > 194000
twoway (scatter niche57 herfinN if party == 11320 | party == 11420 ///
| party == 11620 | party == 11810 | party == 13320 | party == 13410 ///
| party == 13420 | party == 13620 | party == 14320 | party == 14620 ///
| party == 14810 | party == 21320 | party == 21322 | party == 21421 ///
| party == 21422 | party == 21423 | party == 21425 | party == 21426 ///
| party == 21521 | party == 22320 | party == 22521 | party == 22522 ///
| party == 22722 | party == 23420 | party == 23520 | party == 33210 ///
| party == 33610 | party == 34020 | party == 34212 | party == 34313 ///
| party == 34511 | party == 35311 | party == 35313 | party == 35520 ///
| party == 41320 | party == 41521 | party == 42320 | party == 42520 ///
| party == 51320 | party == 51620 | party == 53520 | party == 53620, ///
msymbol(circle)) ///
(scatter niche57 herfinN if party == 11110 | party == 11520 | ///
party == 13001 | party == 13230 | party == 14110 | party == 14222 ///
| party == 14223 | party == 14520 | party == 14820 | party == 14901 ///
| party == 21111 | party == 21112 | party == 21321 | party == 21522 ///
| party == 21913 | party == 21916 | party == 22330 | party == 22526 ///
| party == 22528 | party == 23113 | party == 23320 | party == 34213 ///
| party == 34710 | party == 34730 | party == 41111 | party == 41112 ///
| party == 41113 | party == 41420 | party == 42420 | party == 42710 ///
| party == 51420 | party == 51421 | party == 53110 | party == 53320, ///
msymbol(X)), ///
title("Nicheness/Concentration") ///
subtitle("Premiership Parties vs Governing Non-Premiership Parties") ///
legend(label(1 "PP") label(2 "GNPP"))

Dear all,

I have monthly data on stock market and when I tried to use Newey-West standard errors to deal with heteroskedasticity and autocorrelation, Stata says that there is no observation. Below is my -dataex- :
Here are the problematic results:
Why is there 'no observations'? Below is my 'normal' regression.

I am working on a multivariate regression model that examines the relationship between an index of state consolidation as the dependent variable and a number of predictor variables. I have conducted some basic tests of the predictor variables and transformed them, etc.
However, the correlation matrix and other tests seem to show multicollinearity between some of the predictors. Theoretically, I do not see how they are related to each other. Accordingly, I tried to center and standardize the predictors. I used the commands beta and another called stdbeta. I also created some modified predictors the old-fashioned way subtracting from the mean and dividing by the standard deviation. The stdbeta command did not work and the multicollinearity persisted using the modified predictors.
I am at an impasse and work greatly appreciate some insight into this.
I have attached the do file.
Thank you,
J. David Granger
Array

I am trying to perform the Heckman selection model on a Panel dataset, using fixed effects.

When using cross-sectional data, the problem is standardly implemented by the two-step routine of the command I have been looking into past threads on Statalist (as well as on the related literature), but unfortunately I have not been able to find an exhaustive practical explanation for the code to be used.

To relate to past threads, the most useful comments about the literature seem to choose the estimator proposed in Wooldridge (1995), while some useful comments about the practical implementation can be found at the following links:

1) 2) 3)
Could you please provide a practical example to deal with this problem?
Any piece of advice on more advanced techniques than the one in Wooldridge (1995) would be appreciated.

Thanks in advance for the help.



Wooldridge, Jeffrey M. "Selection corrections for panel data models under conditional mean independence assumptions." Journal of econometrics 68.1 (1995): 115-132.

Dear all,

I am trying to use the Newey-West Standard Errors to carry out my regression, however, the following popped up:

Below is my -dataex-:

I then tried to do it with other lags specification, unfortunately, the same problem persists:
I have tried searching the error phrases both on Google and Statalist, I have come across NOTHING at all, does it mean that my case is extremely rare? Most importantly, what actually went wrong?

Dear Statalisters,

I have a dataset with variables var_1:var_200 as well as a variable id that takes values 1:200 as well, so that the total dataset has 200 rows and 201 columns including the "id" variable.

Now I would like to sum first across all rows, and secondly across all but the first column (I could drop the first if necessary). How can I do that?

I've tried summing across the columns with "egen example = rowtotal()", but would need to type each of the 200 variables there? Or can I at least tell Stata more succintly to sum all of var_1 through var_200?

Thank you so much and best regards,
PM

This is a follow-up to my earlier question about the behaviour of graph export in the Mac version of Stata.

In an assignment for my intro biostats class, students are expected to export some graphs to png files. I was expecting them to use graph export, like this:

But some of them are using graph save instead, like this:

As noted in the previous thread, I use the Windows version of Stata, but many of my students use the Mac version. In a meeting with one of the Mac users today, I observed that when we used graph export with a file name that included two periods (A.Birthday_Problem.png), Stata treated everything after the first period as a file extension, and execution stopped. When I became aware of that, I advised Mac users to change the first period in the file name to an underscore (A_Birthday_Problem.png), and that seemed to fix things. I concluded that the Mac OS does not allow periods in file names apart from the final period before a file extension.

But then today, with graph save (and the same file name), everything was fine. So now I suspect some inconsistency between graph export and graph save.

Can any Mac users duplicate what I have described? Here's a silly little example you can try.

Thanks,
Bruce

Hi All,
I have created a dot graph but I want to improve the appearance of the variable labels o n the x-axis. Any help will be most appreciated! Below is the code that I used and the table I created

Array

Hi everyone,

I have panel of Chinese provinces over time. I am using the xtline command in Stata for my panel data to construct graphical representations of my data. My time identifier is Year and the ID variable is Province. Province has 31 cases, representing 31 provinces in China. Year has 14 time points, covering the timeframe 2004 to 2014.
My DV is pollution, an average measure of multiple variables.

I have grouped the 31 provinces within five geographical regions. This variable is called Regions. I would like to plot a single graph plotting variation in pollution for the five regions over time - in that one graph contains the five regions' variation in pollution over time. Is that possible?
I can create one single graph containing all provinces by using:
xtline pollution, overlay t(Year) i(Province) legend(off)

However I have not found a way to get the region-level plots together one one graph.
I have tried creating a new variable but i am not sure that this is the right way:
egen regions2 = group(regions)
sum regions2, meanonly

Would someone be as kind as to advice me whether this is the right way to go? If so, what command should I use?
Thanks!