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2005年12月28日
substantial writing
xquarter̖lJapanese LawɃGg[ẅlCsustantial writing credit~CƌĂB
́CUofC LSJDɓL̐xŁCJD炤߂ɂ́CƂ܂łɁC2{i3{Hjsubstantial writingƂ̂Ȃƃ_Ȃ̂BAJf~bNȐlԂ炷ƁCu_ɁwCmxƂwmxƂ𖼏JD/LLM/MBAƂāCǁ[vƂ̂͂܂ƂȊoƎv̂ǁCUofĆCx̃y[p[邱ƂŁC̓_̗̌߂}ĂƌB
Ƃ킯Ȃ̂łCOK͂̂́CʂāCǂ̂炢̃xȂC"substantial writing"ƌ̂CƂ悭ȂBŁCAssociate Dean of StudentEleanor̂Ƃɋ삯őkBcritiquełȂāCƎŃT[`ďCcredit悤CƂƂɁB
���e�� hatsuru : 12:13 | �g���b�N�o�b�N
2005年12月27日
two roasts
NX}XCɂ́C肪Ƀ[Xg`LĂ̂ǁC`Lƃr[tł͖ʔȂiVailł́C҂r[tĂĂĵŁC[Xg|[NĂĂ݂܂B
3-4lbsi1.5kg)炢̌ł܂C450F20->360F20->20x->360F10->10x݂ŏĂĂ݂CC傤ǂĂオɁBĂŌłȂ炸ɁCłƉʂĂiȂ̂ŁjB
AJɁCĂ`gravy sauceĊ\B
ɂĂCI[u͊yiɁC2ڂ̃Ap[g̃I[úCxE^C}[ł߂ΑSIɂĂŐV^Ȃ̂ŁCɊyjBĕ荞łBAJɗwĂ^搶uAJł͗Ă̂ǁC{ɋAĂĂI[uȂ̂ŎȂȂvƂCȂłȂC炵B
���e�� hatsuru : 10:39 | �R�����g (1) | �g���b�N�o�b�N
2005年12月24日
TOB & industrial regulation
lieƕ̂łƂjC[F
@،@̊JtɂAƋK@Kɓ݂i@AƎ҂銔Ђ̂snaɂđb̋v̂Ƃ铙jƂ@_ɂāA_AzB
@ivsn[\Ƃ邪j
WTO[͂悭mȂ̂ő[ĂƂāCЖ@́Cǂ[ɂ悤ƂłȂ́CƂ̂u@v̉낤B{vetoꍇ͂Ό邵i̋ɒ[ȗႪuLƁvjCЂȂĖ@F߂ŏ߂Đ̂Ȃ̂il搶IzjCǂ[ɂ悤Ɗ{IɎRȂ͂i@29n̖͂ƂjB
ƁĆ̖CK݂ƂāCǂ̒iKŋKyڂCƂ_BƋƂ̏ꍇCŏ̔F݂̂Ȃ炸Cchange of control̏ꍇɂCF̍ۂƓl̐RȂȂȂCƂȂĂ̂ʂ̂悤ȋCiȂƂClOɒׂUS̐MƋKsK͂jBƓx̐Rub̋v̔vłȂCljIɂ̂悤ȓ݂IӖ͂ȂBƂƁCɌuv́CF̐RƓ̃`FbNł͂ȂC傫ȃt[nh^CƂӖȂ̂H
ǂCꂪuIɁv]܂ǂ́CʖB
ɁCǂœႪ̂ɂ邯ǁC[́CƎ҂ɂāCt[nhbetc̈ӌɏ]CZeB@[ɂȂ肩˂ȂBႦCɒ[ȘbCSĂ̕Ǝ҂NHKiƂCIHjɂȂȂďꂵ낤B
ɁCTOBɂĂ苭݂ƂƂ́CTOBʂVKQɂĂ̂ݍbarrier݂邱ƂɂȂCƎ҈ێstatus quoւ̋wށBꂾω̑ЉŁCꂪ]܂ǂ́CȂBTOBɂĂ̂݁CƋʂĈ悭ȂB
PS. ɂĂC@ẃCɁCudg͗L...vƂĂ̂낤B
���e�� hatsuru : 00:37 | �R�����g (3) | �g���b�N�o�b�N
2005年12月22日
follow-ups
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1B
Winter Q̗\肾ǁC
35000 EMPIRICAL MICROECONOMICS (Heckman)
This class focuses on using economic theory and econometric methods to evaluate and forecast economic policies. In 2004-05, the course will focus on (a) evaluating education, job training and skill formation policies including policies directed toward young children; (b) evaluating labor market and product distortions and market reforms; (c) cost benefit analysis in partial and general equilibrium settings. The course emphasizes heterogeneity among agents and grounding macromodels in microdata.
ƂBVoXN(2004-05)̂܂܂I
2B
self-selection]X`FbN邽߂ɁCOLSĂ݂ʂ͎̒ʂBƂĂCꂾĂCƂquestionnaireȂƕȂǁijBq34, q38, citysizesign͒ɂĂǁBȂ݂ɁCOLSȂāCordered probit/logitĂ݂CconvergeȂB
Source | SS df MS Number of obs = 1398
-------------+------------------------------ F( 29, 1368) = 6.54
Model | 194.988987 29 6.72375817 Prob > F = 0.0000
Residual | 1407.35794 1368 1.02877042 R-squared = 0.1217
-------------+------------------------------ Adj R-squared = 0.1031
Total | 1602.34692 1397 1.14699136 Root MSE = 1.0143------------------------------------------------------------------------------
q28 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q33 | -.0355979 .0655119 -0.54 0.587 -.1641126 .0929168
q34 | -.0135354 .0023235 -5.83 0.000 -.0180935 -.0089774
q35 | -.0686305 .0406351 -1.69 0.091 -.1483444 .0110834
q36 | -.0045693 .0035037 -1.30 0.192 -.0114425 .002304
q38 | .0797487 .0322043 2.48 0.013 .0165735 .1429239
q41 | .0029098 .0088181 0.33 0.741 -.0143886 .0202082
occupation1 | .1768633 .7285164 0.24 0.808 -1.252267 1.605994
occupation2 | .351741 .7238314 0.49 0.627 -1.068199 1.771681
occupation3 | .9412269 .7563023 1.24 0.214 -.5424111 2.424865
occupation4 | .3378645 .733857 0.46 0.645 -1.101742 1.777471
occupation5 | .3682377 .7309054 0.50 0.614 -1.065579 1.802054
occupation6 | .5518859 .723846 0.76 0.446 -.8680824 1.971854
occupation7 | .2375824 .7260803 0.33 0.744 -1.186769 1.661934
occupation8 | .4556109 .7253166 0.63 0.530 -.9672424 1.878464
occupation9 | .0253105 .7708943 0.03 0.974 -1.486953 1.537574
occupation10 | .4671936 .7227552 0.65 0.518 -.950635 1.885022
occupation11 | .2784078 .72294 0.39 0.700 -1.139783 1.696599
occupation12 | (dropped)
occupation13 | 2.241044 1.244881 1.80 0.072 -.201039 4.683126
citysize | -.0478251 .0210287 -2.27 0.023 -.0890772 -.0065731
iregion1 | (dropped)
iregion2 | .1384803 .1578938 0.88 0.381 -.1712598 .4482204
iregion3 | .0929231 .1413621 0.66 0.511 -.1843869 .370233
iregion4 | -.0927823 .1737972 -0.53 0.594 -.4337201 .2481556
iregion5 | -.1511561 .1736426 -0.87 0.384 -.4917906 .1894785
iregion6 | -.0873185 .1543214 -0.57 0.572 -.3900508 .2154138
iregion7 | .0132282 .1486268 0.09 0.929 -.2783329 .3047893
iregion8 | .0373025 .1632893 0.23 0.819 -.283022 .357627
iregion9 | .0088165 .2007419 0.04 0.965 -.3849787 .4026118
iregion10 | -.0485014 .1653433 -0.29 0.769 -.3728553 .2758525
iregion11 | -.2587781 .1747372 -1.48 0.139 -.60156 .0840038
_cons | 2.358717 .7645185 3.09 0.002 .8589613 3.858473
------------------------------------------------------------------------------
���e�� hatsuru : 22:37 | �R�����g (2) | �g���b�N�o�b�N
2005年12月21日
allowing for self-selection bias
kłĂCjQCOẼT[xCT[`Ă̂ŁCςςƌĂ݂B
܂questionnaire̍肪낵ȂBRachinski̎ƂłûႾ߂[vƂn݂܂Ă邷܂[B쐬҂̐lƎv̂ǁCЉwSw̐l牞Ǝčǂ̂ł͂Ȃł傤BĂĂꂾƁC~悤ȂǁBނB
ƁCf[^StataʼnĂ݂̂ǁC͂Ȃself selectionĂ܂ˁB͂Clی@́Csuvey questionnaireɑāCbias傳Ƃׂ낤Bself selectionƂCeconometricsIɂ́CHeckman's two-step (Heckit)̏oԂȂ킯ǁiCinverse Mill's ratio...jCfirst stepprobitŎginstrument邩CƂ̂Bquestionnaire̎⍀ڂC쐬҂̂̐l̎wfĂCvĂ̂BɁCHeckitāCparticipation ratiofirst stepprobitŐ肷킯ŁCꂪƂۏ͕KȂB
܂CCECONIɂ́CuӌvƂ͕̂sŁCusv͂̕₷BȂƁCSOCI̎R搶ł́Codds-ratio͂ōsȂ̂ȂB
���e�� hatsuru : 19:40 | �g���b�N�o�b�N
Next Quarter
VailAĂ̂ŁCn܂闈w͉Ƃ낤ƎvB
ȂC
ECON 31100 Empirical Analysis II by Hansen
PPHA 34600 Program Evaluation by LaLonde
SɃobeBOiMW 9:00-10:20)Ă邱ƂB[BVoXǂތł́CLaLonde͋NApplied Econometrics IIƂȂ苤ʂĂ銴Ȃ̂Łiǂރy[p[\dȂĂjCHansenB~Midwayzĉ̂͂炢̂ǁijBɂĂCo^ƁCςLaLonde͐lCȂBHCPhDłȂāCMPP?
ꂩCu~[n[Nobel LaureateHvijŁC
ECON 32300 Guide to business ethics by Fogel (identical with GSB 38114)
łC܂Ȃ瑬UŐ邩ȂBGSB MBAƋʂCʂ邢Ɨ\zB
ƁCSOCI, PSYC, STATςςƌĂ݂ǁC܂HwÛȂBSOCI/LAW
SOCI 30156 Sociology of Law by Lancaster
... Survey researchńC̑OHarrisRachinskîƂĂîɑςĵŁC1ƂKv͂ȂBSTAT̒BayesianCɔ`Ă݂ĂȂƂC͂̂ǁCǂꂩȁH
STATISTICS 25300=STAT 31700. Introduction to Probability Models.
Instructor: Mei WangTime: TTh, 10:30-11:50 AM
Location: Eckhart 133
PQ: Consent of instructor
Reading: Ross, R. (2003). Introduction to Probability Models, 8th ed.This course introduces stochastic processes as models for a variety of phenomena in the physical and biological sciences. Following a brief review of basic concepts in probability the course will introduce stochastic processes that are popular in applications in sciences, such as discrete time Markov chain, the Poisson process, continuous time Markov process, renewal process and Brownian motion.
STATISTICS 31200. Introduction to Stochastic Processes I. Instructor: Per A. Mykland
Time: TTh, 10:30-11:50 AM
Location: Eckhart 117
PQ: STAT 25100 or consent of instructor
Reading: Ross, S. (1996). Stochastic Processes, 2nd ed., Wiley.Stochastic processes provide models for random events that evolve in time which may include substantial dependence among observations at different times. The goal of this course is to present a variety of useful models including Markov chain, renewal theory, Brownian motions etc.
肩BƂ́C]ʂCECOÑ[NVbvɊo܂邭炢B
���e�� hatsuru : 19:07 | �g���b�N�o�b�N
2005年12月19日
Look before you leap
Ƃ킯ŁC6ԊςȂiłSẴR[XĂȂjVailAĂ܂BƍŏI͈ړ̂ŁC8Ԃ̗Błi1V[YH
ŁCVailɂĂ܂Ƃ߂ƁF
- {̃XL[ƉႤƂƁCpE_[Xm[groomĂȂVE[ႪCBack Bowls/Blue Sky Basinӂɂ́CԂقƂǓȂ̂ŁCȃR[XBuntouchedslopeŐቌ[ƂȂ犊~Ă̂́CC
- ߂iBlue Sky BasinɎĂ9炢j㋉҃R[XȂ̂ŁCChicagoߕӂ̂ւXL[Ƃ͈ĊyBBlue Sky BasinLover's LeapiLepicierɓÔ悤ȁj́CR`̉q̃Jxi38xj炢āC{ɁuSvȂƎv킹BĆCK͂̃RuAło[̉qƂ͈āCӂӂ̃pE_[Xm[̐[BCrɁCR̐炪āCȂXOBŏIɃ`WĂ݂C̐l"You're crazy!"ƌꂽB
- Ƃ킯ŁCVł̃XL[ɂԏKn܂i...j
- BĂԂ͑vǁCtgʁBȂ̂ŔɂĂ̂̂ȂǁCIo͓܂B-10C͗]TŁC̊xł-20CBłKissD삵̂͊SB
- ɂ֘A邯ǁCtgǂBꂾ肪y߂̂ǁBV[Yn܂ƂƂĂCǂ҂ԃ[B
- {̃XL[ƈāC܂GAłlȂBłVvɔԂ̂قƂǂŁC6ԂŌB̋ŹCwRv^[B{i{鑠R`jňԗǂ̂RTbNȂ̂ƈႤBmɃwRv^[̕hŌh邯...
- look before you leap. KissDꂽobNpbNԂŃGA͔тȂinɎsĔw痎ꍇCKissDʁĵǁCBlue Sky Basin1JC"Caution Cliff"̐悵ȂCdȂт܂BCnłjump͕ʂɊy݂܂B
- tg`PbǵC[ł`FbNďł̓m[`FbNBȂȂIB
- ^ŁC܂ď߂hitchhikěBCUŜĂ̏Bł͈@Ȃ̂ǁiIllinoisjCColorado͂ǂ낤Billegal낤Ȃ...
- ЂƂłɁCDenver Airportɂ́CXL[pturntableBB
���e�� hatsuru : 23:31 | �R�����g (1) | �g���b�N�o�b�N
2005年12月15日
during the flight
During the flight from O'Hare to Denver, I was making a power point file, which I am going to use at an lunch presentation of the Japanese Law Association (UofC LS). I seemed to be weird because the people around me, who are GSB studnets, had just finished their finals (I'm coming Vail with the GSB ski club and the group is almost 100 people). As I did in last year, I am asked to make one presentation, which comes in January.
This is what I made tentatively. It is a another version of my "public comment" empirical study -- I changed the regression framework from probit to OLS (LPM), because OLS is easy to interpret. Although LPM has several problems, such as bias, predictions over 1 and under 0, and so on, I have decided that the ease of interepretation comes first when you need to talk before law the law students, who are not familiar with econometrics. Also, I added some regressions of subset of the data, which I believe enhance the understanding of the points. I'd appreciate any comments on the draft. Thanks.
���e�� hatsuru : 19:07 | �g���b�N�o�b�N
Vail Photos
You can completely plunge into snow because Vail has deep fresh powder snow -- even in flat areas!
Some "most difficult" courses have very narrow trails through woods and, of course, not groomed. Take care because you'll "TAIHA" when you loose control of your skis; but that is where skiing is interesting!
Finally, this is an example of "ghost" and "flare". Both happen when you put the sun into the frame. I'm not sure if KissD and EF-S 17-55 USM is strong against ghost and flare, but this ghost is beautiful, isn't it?
���e�� hatsuru : 18:39 | �R�����g (2) | �g���b�N�o�b�N
2005年12月13日
on your own risk
When I rented skiing gears yesterday -- of course, I left my ski gears in Sendai execept for the wears --, I had an interesting experience.
As trying to fit the boots to the ski, the guy at the rental shop set the tightness of the bindings to five. When I ski in Japan, I always set my binding to 7 or 8. Five would let the ski go off too often, especially when you ski agressively. So I told him to set the tightness to seven.
Then, he asked me to sign the document, which specified that the binding is set to seven subject to my request. No rental shop in Japan would require such a document. Because tighter binding raises the risk of injury, the shop seemed to avoid the legal risk caused by the tighter binding. I'm not sure if such waiver is effective or not, but the difference of attitude is interesting.
PS. As expected, Vail is really cold -- the high is 20-25F and the low is often negative. But the snow condition is extremely nice and the area is really huge -- it calls itself as "the largest ski resort in North America". You should definitely come here!
���e�� hatsuru : 22:05 | �R�����g (2) | �g���b�N�o�b�N
2005年12月12日
suffering from altitude sickness?
Actually, I cannot distinguish this headache from those caused by drinking or just the altitude of 11140 feet. In either case, the headache will go away by tomorrow morning, hopefully.
���e�� hatsuru : 20:41 | �R�����g (2) | �g���b�N�o�b�N
2005年12月11日
Tarte aux poire + ...
݁B"Chinese pancake with sweat beans"ƐBȂCłȂpint beanł쐬BƊÂ݂ȂB
mȂ^ǵ̕C2ڂƂƂāC쐬BvXɃp[gEVNōii̓p[gEv[jǁC̐ñTNTN͐lCB
ȏ2_́C[Cg{Xtenure interviewIjƂ牽ƂiHjŊJp[eB̂߂ɍ쐬BCynf̒ŌƂ̂CugUÛ݂ŘAzvƂQ[̌Buf̃^CgvłĂ̂ǁC̈lC"Dr. Strange Love or How I learned to love the atomic bomb"oāC"It's toooooo mean!!"ƔXBʂ̐ĺCC^CgmȂB
���e�� hatsuru : 15:14 | �g���b�N�o�b�N
2005年12月09日
Illumination has begun around the Main Quad
���e�� hatsuru : 15:21 | �g���b�N�o�b�N
preparation of the materials
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Winter Quartercourse materials̏Cdeadlineԍۂł肬Z[tB
ÑXPW[́CNC㔼gݑւāCȊɁBł邾materialsCvgAEgłȂChalkɂԂ̂ŁCcourse packet́C170̔ōςBNɔׂƁCCfinanceeconometricsdɂȂBempiricaĺCtableׂČ̂ŊyijB
���e�� hatsuru : 11:22 | �g���b�N�o�b�N
2005年12月07日
Estimating tax avoidance
LSƂGSB͐TŎƂIōTexam weekłCPhDseminar͍TBƂ킯ŁCTLevitt̃NX́Cstudent presentationɓ˓B̒̈ɊŷB
VAlC"Estimating Tax Avoidance in Russia"CƔ\BdʼnȂāCǂestimateHƎvǁCނideǎBCȃoCAXĂ̂ŁCdecompose邱Ƃ܂܂KvǁCideâ͖ʔiɁC͂łpublicɂ͏ȂjB
ACfA͂ƂCf[^͂ǂCƂƁCuMoscowɋAƂ̂łvƂČĂꂽdata set͂܂BMoscowśCOEZEpX|[gԍE[ŊzEEƓSĂĂf[^x[XI@Ȃ̂ǂœłCƂƁC~~h炢CMoscowł́C"data stolen from the authority"ȒPɎɓCƁBƂChicago炵research\\VenkateschiԂ肠ĂHjɓT^IɌ悤ɁCSoci/Econ/SSAȂǁC̈Ƃɂ荞ށ\\BC͔̉QB
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���e�� hatsuru : 21:11 | �g���b�N�o�b�N
2005年12月04日
UofC orchestra's first concert of the season 2005-06
UNIVERSITY SYMPHONY ORCHESTRA
Saturday, December 3, 2005
8:00 pm
Mandel HallTwo unusual music treats of the season Leo Sowerby's Comes Autumn Time and Arnold Bax's November Woods are balanced by Tchaikovsky's richly colored symphonic masterpiece, full of yearning melodies, passionate climaxes, and poignant colorations: the Symphony No. 5 in E Minor. Barbara Schubert conducts.
Admission: Donations requested at the door - $8 adults, $5 students.
academic year 2005-06ŏUofC orchestraconcertB̂悤ɁCuLȁ{AJ̍ȉƂ̃}Ci[ȁvƂgݍ킹Bx̃}Ci[Ȃ͂܂Bׂ̐l"NovemberƂDecemberł́H"ƊzĂ̂ɂ͓Bށ[B
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UofC orchestrão[\ƁCÑo[炿傱傱ςĂ悤Ɍ邯ǁCȂƂ̎ʐ^vlñgbv3l͕s\\concert master, principal of the vln 2, concert masteȑׂ̗BŃC̏vln̉̐l⋭ĕĂǁi傤ǍĂʒuޏ̐^ʂ炩jCN͂Ƃ͂ĂB
Ȃ݂ɁCj̖ɌCMarch of the Penguinsreally moving and cuteBquarterłno. 1̋q̓肾B܂ĂȂl͍sׂBMorgan Freemañi[VC̎ƂȂNHKhL^[Ȃ̂ǁijCׂĂe̓AJȂ̂ł悵BJglԂƂẮCủf͂ǂĂƂ낤HvƂ̂CɂȂǁĆCŌcredit̂Ƃŏ炩ɂȂBȂق...
���e�� hatsuru : 10:27 | �R�����g (1) | �g���b�N�o�b�N
2005年12月01日
finance
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iǂłǁCGSBɂ́CfinancéuӂȂvł͂Ȃāuӂȁ`vBLatinȔȂCƎv̂...j
ScienceDirectȂl͂߂B
���e�� hatsuru : 07:42 | �g���b�N�o�b�N
Why individual investors want dividends
Why individual investors want dividends
Ming Donga, Chris Robinsonb and Chris Veld
(Journal of Corporate Finance Volume 12, Issue 1 , December 2005, Pages 121-158)
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���e�� hatsuru : 07:27 | �R�����g (1) | �g���b�N�o�b�N