製造業における熟練労働への需要シフト:
|
|
- みがね うみのなか
- 5 years ago
- Views:
Transcription
1 * ** No.04-J * **
2 * * e-mal: htosh.sasak@boj.or.j e-mal: kench.sakura@boj.or.j 1
3 skll-based technologcal change SBTC SBTC IT SBTC SBTC SBTC SBTC 1 Freeman and Katz[1994] 2
4 2 Sakura[2001] IT 3 [2000] ead and Res[2000] SBTC Ito and Fukao[2004] 2000 SBTC IT SBTC SBTC SBTC 2 Berman et al.[1994]autor et al.[1998] SBTC Sachs and Shatz[1994] Wood[1994]Bernard and Jensen[1997]Feenstra and anson[1996a, 1996b, 1999] SBTC 3 [2004] Sakura[2001] 3
5 4 5 2 SBTC SBTC 5 2. SBTC L % 10.4% % 50% 4
6 w w L 1(1) y y A 0 0 w L / w / L SBTC 1(2) SBTC y y y y B A B / L< / L w L / w > w L / w B A 7 w w < ( wl L + w ) ( w L L + w ) A B ( / L) ( / L ) < ( wl / w ) + ( / L) ( w L / w ) + ( / L ) SBTC SBTC 6 7 5
7 8 SBTC 3. SBTC SBTC Feenstra and anson[1996a] 6
8 3.1 Mncer[1974] lnw 2 = α + β t + γ 1K + γ 2K (3-1) lnw t K α β γ 1 γ 2 (3-1) (3-2) Weghted Least Squares 11 ln( wage ) = a + = b ( g g kn 2 1 ) g 3 Dh 2 Ej 16 h h D + e j j D + = 1 = 1 k = 1 Fk + d f k D + u (3-2) ln( wage ) / kn ) D Dh Ej Fk D D a ( , [2004] (3-2)
9 bg d h e j f k u 3 Dh D D D D D1 D2 D Ej D D D E1 E Fk D 16 (3-2) (3-2) 2 dˆ ˆ 1 d ˆb 1 ˆb 2 8
10 (1) % 13 (2) (3-3) 13 Autor et al.[1998] % % 14 Berman et al.[1994] 9
11 s = n s = n s = (3-3) = 1,, n : n = 17 s = W / W : s = W / W : = W / W : % 2 W W W W (3-3) SBTC SBTC (3-3) 5(1) % 10% (2) 10
12 SBTC 4. SBTC 4.1 Berman et al.[1994] K L 15 SBTC 15 [2004] IT IT 11
13 Z V C V ln C = α 0 + α ln w + α K ln K + α Y lny + α Z ln Z + α t t + 0.5( α YK KK ln K 2 + α ty YY lny 2 + α YZ ZZ ln Z 2 + α t tt 2 tk + q γ q KZ ln w ln w + α lny ln K + α t lny + α lny ln Z + α t ln K + α ln K ln Z + α t ln Z + Y K Z ρ lny ln w + ρ ln K ln w + ρ ln Z ln w + q ) t tz ρ t ln w (4-1) w t (4-1) w ln lnc ln w V = w d V C = s Y K Z q t = α + ρ lny + ρ ln K + ρ ln Z + γ ln w + ρ t q q (4-2) d w (4-1) w (4-3) 16 Y K Z t = γ q = ρ = ρ = ρ = ρ = γ and q 0 q α =1 (4-3) (4-2) Y ρ ρ = 0 (4-4) + K 16 γ q = γ q 12
14 L (4-1) w w (4-2) (4-3) (4-4) s w K = α + γ ln ( ) + µ ln ( ) + λ ln Z + δ t (4-5) L w Y s L w /( w + w L ) L ln( w / w ) ln( K / Y ) ln α γ µ λ δ Z 2 SBTC (4-5) (4-1) (4-5) Berman et al.[1994]
15 18 (4-5) s t K = α + µ ln ( ) t + λz t + α + φ t + ε t (4-6) Y t s t α µ λ α φ ε t dosyncratc shock (4-6) Zt SBTC M t Ft 19 SBTC IT IT Rt t 18 Ito and Fukao[2004] [2003] JIP (1) 2000 SBTC 14
16 λ (4-6 ) (4-6 ) s s t t K M R = α + µ ln ( ) t + λ M t + λ R t + α + φ t + ε t (4-6 ) Y K F R = α + µ ln ( ) t + λ F t + λ R t + α + φ t + ε t (4-6 ) Y (4-6) µ λ SBTC SBTC 21 (4-6) (4-6) α fxed effects model random 21 Wood[1994] SBTC defensve nnovaton Lawrence[2000] SBTC SBTCautonomous nnovaton 15
17 effects model (4-6) φ t 22 NL (4-6) 23 strct exogenety (4-6) E[ ε α X α φ ] = 0 for all and u ( u = { 1, 2, L, t, L, T } ) t u t t ε t E[ ] X (4-6) ( ) u = ln( K u / Y u Z u X ), u (4-6) X 24 (4-6) t t W t s s X t 22 F 23 (4-6) ad hoc 24 Wooldrdge[2002],
18 s t = α + Φ X + Ψ W + α + φ + ε (4-7) t s t t Φ Ψ (4-7) W s 0 : Ψ = 0 F X t 25 W s t 1 Wt 1 W t + 1 (4-6) (4-5) L ln ( wt / wt ) omtted varable (4-6) εt (4-6) εt 26 AR(1) s < t (4-6) t ε t ( t s) s > t (4-6) t ( s t) 26 effcent Frst Dfferenced Model 27 17
19 st 100 Kt Yt M t Ft 100 Rt 100 NLt
20 L ( wt / wt ) 6 (1) SBTC SBTC 4.3 8(1) M t Ft SBTC Rt (1) (5) NLt (6) (7) 9 SBTC ln( K / Y ) t 8(1) AR(1) 19
21 (3) 30 F (4-7) 1 W t 1 W t (2) (4-6) L ln( w t / wt ) omtted varable 8(1) (8) (9) (4-6) AR(1) SBTC 8(3) 8(1) SBTC Sakura[2001] [2004] ead and Res[2000] Berman et al.[1994] Feenstra and anson[1996a, 1996b, 1999] Bernard and Jensen[1997]Autor et al.[1998] Goldn and Katz[1998] 31 8(2)
22 % 0.31% 0.75% < > 0.75 <0.70.8> % % % SBTC 2 SBTC SBTC Feenstra and anson[1999] % SBTC % (1) 33 Feenstra and anson[1999] SBTC 31.5% 21
23 SBTC 5. SBTC 1985 SBTC 14 SBTC SBTC SBTC SBTC 22
24 [2004] 23
25 M t A) B) A) 100 C) 2000 = D) A) 2000 =
26 E) B) 2000 D) C)
27 Autor, D.., L. F. Katz, and A. B. Krueger[1998], Comutng nequalty: ave comuters changed the labor market?, Quarterly Journal of Economcs, 113, Berman, E., J. Bound, and Z. Grlches[1994], Changes n the demand for sklled labor wthn U.S. manufacturng: Evdence from the Annual Survey of Manufactures, Quarterly Journal of Economcs, 104, Bernard, A. B., and J. B. Jensen[1995], Exorters, skll ugradng, and the wage ga, Journal of Internatonal Economcs, 42, Feenstra, R. C., and G.. anson[1996a], Foregn nvestment, outsourcng and relatve wages, n R. C. Feenstra, G.M. Grossman and D. A. Irwn (ed.) The Poltcal Economy of Trade Polcy: Paers n onor of Jagdsh Bhagwat, Cambrdge, MA: MIT ress, Feenstra, R. C., and G.. anson[1996b], Globalzaton, outsourcng, and wage nequalty, Amercan Economc Revews, 86, Feenstra, R. C., and G.. anson[1999], The mact of outsourcng and hgh-technology catal on wages: Estmates for the U.S., , Quarterly Journal of Economcs, 114, Freeman, R. B., and L. F. Katz[1994], Rsng wage nequalty: the Unted States vs. other advanced countres, n R. B. Freeman (ed.) Workng under Dfferent Rules, New York, NY: Russell Sage Foundaton, Goldn, C., and L. F. Katz[1998], The orgns of technology-skll comlementarty, Quarterly Journal of Economcs, 113, ead, K., and J. Res[2000], Offshore roducton and skll ugradng by Jaanese manufacturng frms, Journal of Internatonal Economcs, 58, Ito, K., and K. Fukao[2004], Physcal and human catal deeenng and new trade atterns n Jaan, NBER Workng Paer Lawrence, R.[2000], Does a kck n the ants get you gong or does t just hurt? The mact of nternatonal cometton on technologcal change n U.S. manufacturng, n R. C. Feenstra (ed.) The Imact of Internatonal Trade on 26
28 Wages, Chcago: Unversty of Chcago ress, Mncer, J.[1974], Schoolng, Exerence, and Earnngs, New York, NBER. Sachs, J.D., and. J. Shatz[1994], Trade and jobs n U.S. manufacturng, Brookngs Paers on Economc Actvty, 1, Sakura, K.[2001], Based technologcal change and Jaanese manufacturng emloyment, Journal of the Jaanese and Internatonal Economcs, 15, Wood, A.[1994], North-South Trade, Emloyment and Inequalty, Changng Fortunes n a Skll-Drven World, Oxford: Clarendon ress. Wooldrdge, J. M.[2002], Econometrc Analyss of Cross Secton and Panel Data, Cambrdge, MA: MIT ress. [2004] 23 1 [2000] Vol.21-2 [2004] Vol.25-1 [2004] [2003]
29 SBTC SBTC y 0 A y 0 O ( / L) ( w L / w ) L SBTC y 0 y 1 A B y 0 y 1 O ( / L) ( / L ) ( w L / w ) ( w L / w ) L
30 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 2.89 ) ( 3.15 ) ( ) ( 5.58 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 2.99 ) ( 0.96 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 0.54 ) ( 2.04 ) ( 0.03 ) ( 1.43 ) ( 2.02 ) ( 5.56 ) ( 9.32 ) ( 7.55 ) ( 5.41 ) ( 6.82 ) ( 6.61 ) ( 9.83 ) ( 9.42 ) ( 6.70 ) ( 5.89 ) ( 6.43 ) ( 5.84 ) ( 0.25 ) ( 0.02 ) ( ) ( 0.87 ) ( 1.49 ) ( 0.61 ) ( 2.57 ) ( 0.93 ) ( 6.01 ) ( 2.98 ) ( ) ( ) ( ) ( 0.71 ) ( 1.69 ) ( ) ( 0.89 ) ( 0.68 ) ( 3.82 ) ( 7.44 ) ( 3.12 ) ( 3.27 ) ( 2.70 ) ( 2.20 ) ( 5.17 ) ( 2.53 ) ( 2.07 ) ( 1.41 ) ( 4.01 ) ( 6.69 ) ( 4.73 ) ( 4.88 ) ( 4.89 ) ( 5.55 ) ( 7.78 ) ( 2.21 ) ( 2.00 ) ( 1.78 ) ( 4.26 ) ( 5.54 ) ( 4.11 ) ( 2.79 ) ( 0.10 ) ( ) ( ) ( ) ( ) ( ) R 2 S.E WLS eteroskedastcty-consstent standard errorscses t 17 16
31 ,105/
32
33 (87.6%) (91.3%) (89.6%) (94.0%) (89.7%) (12.4%) (8.7%) (10.4%) (6.0%) (10.3%)
34 <100.0%> <8.0%> <2.4%> <0.9%> <1.3%> <1.3%> <1.7%> <5.6%> <6.5%> <1.7%> <4.7%> <3.7%> <2.0%> <5.4%> <12.3%> <25.8%> <11.8%> <4.8%> <100.0%> <11.3%> <-32.0%> <-11.5%> <-5.1%> <-8.8%> <-0.1%> <23.9%> <-4.9%> <-3.7%> <-13.7%> <-46.0%> <-6.4%> <-4.8%> <1.1%> <163.6%> <32.9%> <4.3%>
35 L s t ln( K t / Y ) M t F t Rt NL ln( w / ) t t w t t s t ln( K t / Y M t F t R t t ) NL t L ln( w t / w ) t s t L ln( K / Y ) M R NL ln( w t / w ) t t t F t t t t
36 SBTC SBTC
37
38 s t (1) (2) (3) (4) (5) (6) (7) (8) (9) ln( K / Y ) t *** *** *** *** *** *** *** *** *** (0.530) (0.487) (0.454) (0.526) (0.489) (0.490) (0.466) (0.450) (0.475) M *** t *** *** *** (0.052) (0.052) (0.051) (0.044) ln( F t SBTC R t NL t L w / w ) t *** * ** * (0.039) (0.040) (0.040) (0.038) *** *** *** *** *** *** *** (0.289) (0.280) (0.264) (0.250) (0.244) (0.239) (0.250) *** *** (0.060) (0.083) *** *** (4.058) (5.176) const *** *** *** *** *** *** *** (1.854) (1.832) (1.790) (1.993) (1.862) (2.224) (1.957) (2.290) S.E F ausman-test P (0.00) (0.00) (0.00) (0.00) (0.00) (0.30) (0.00) (0.00) (0.00) Number of obs ****** 1510% secfcaton-test FGLSFeasble Generalsed Least Squares (2)(5)(7)(9) 13
39 s t = α + Φ X + Ψ W + α + φ + ε t s t t (4-7) W s 0 : Ψ = 0 F P W s W = t +1 ln( K / Y ) + t 1 ln( K / Y ) + t 1 M t +1 R t + 1 F t +1 R t +1 M t +1 R t +1 F t +1 R t (0.385) (0.525) (0.331) (0.380) W W s = t 1 ln( K / Y ) ln( K / Y ) t 1 t 1 M t 1 R t 1 F t 1 M t 1 F t 1 R t 1 R t 1 R t (0.530) (0.399) (0.344) (0.267) AR(1) (1)' (2)' (3)' (4)' (5)' (6)' (7)' ln( K / Y ) t (0.965) (1.267) (0.975) (0.954) (1.254) (0.753) (0.925) M *** t *** *** (0.055) (0.055) (0.054) F t SBTC R t NLt * (0.048) (0.048) (0.048) *** ** *** *** *** (0.334) (0.328) (0.342) (0.299) (0.334) *** *** (0.093) (0.139) S.E F Number of obs s t ****** 1510% FGLS F Ψ = 0 AR(1) (1)' (5)'
40 (1) (1)(5)
日本経済の情報化と生産性に関する米国との比較分析
RIETI Dscusson Paer Seres 02-J-08 RIETI Dscusson Paer Seres 02-J-08 IT nvesmen and roducvy growh of Jaan economy and comarson o he Uned Saes 2002 0 975 2000 990 990 Jorgenson 990 990 JEL Classfcaon: O30O47O53
More information2016 (8) Variety Expansion Effects by Feenstra (1994) 1 Variety Effects Dixit and Stiglitz (1977) CES n n? n t U t = ( nt i=1 σ > 1, a it > 0,
2016 8 29 1 4 Variety Expansion Effects by Feenstra 1994 1 Variety Effects Dix and Stiglz 1977 CES n n? n t U t σ > 1, a > 0, σ a q σ n a 1 σ a p q U t P t E U t, p t U t P t U t V p, I I t a σ p 1 σ a
More informationMicrosoft Word - 田口君最終報告(修正後).doc
2005 7 1 2005 7 2 1 2 2 4 Data Envelopment Analyss 4 Stochastc Fronter Analyss 5 Non-mnmum Cost Functon 6 7 8 9 9 9 13 13 13 14 14 17 19 20 21 24 24 24 25 25 28 31 36 37 40 3 2 2005 7 1950 1960 2003 1004
More informationˆ CGE ž ž ˆ 2 CGE 2 1 ˆ n = 1,, n n ˆ k f = 1,, k ˆ ˆ ˆ 3
CGE 2 * Date: 2018/07/24, Verson 1.2 1 2 2 2 2.1........................................... 3 2.2.......................................... 3 2.3......................................... 4 2.4..................................
More information432001 4.1 1990 1990 (Information Technology ) 1990 1992 19981998 28
432001 2739 Hisako ISHII 1990 (US Bureau of Labor Statistics, BLS)Monthly Labor Review MLR 25 19831993 20004.6 27 432001 4.1 1990 1990 (Information Technology ) 1990 1992 19981998 28 1990 8 7 6 5 4 3 2
More informationN cos s s cos ψ e e e e 3 3 e e 3 e 3 e
3 3 5 5 5 3 3 7 5 33 5 33 9 5 8 > e > f U f U u u > u ue u e u ue u ue u e u e u u e u u e u N cos s s cos ψ e e e e 3 3 e e 3 e 3 e 3 > A A > A E A f A A f A [ ] f A A e > > A e[ ] > f A E A < < f ; >
More informationuntitled
Cross [1973]French [1980] Rogalsk [1984]Arel [1990]Arel [1987]Rozeff and Knney [1974]seasonaltycalendar structure [2004] 12 Half-Year Effect [2004] [1983] [1990]ChanHamao and Lakonshok [1991] Fama and
More information201711grade1ouyou.pdf
2017 11 26 1 2 52 3 12 13 22 23 32 33 42 3 5 3 4 90 5 6 A 1 2 Web Web 3 4 1 2... 5 6 7 7 44 8 9 1 2 3 1 p p >2 2 A 1 2 0.6 0.4 0.52... (a) 0.6 0.4...... B 1 2 0.8-0.2 0.52..... (b) 0.6 0.52.... 1 A B 2
More informationPublic Pension and Immigration The Effects of Immigration on Welfare Inequality The immigration of unskilled workers has been analyzed by a considerab
Public Pension and Immigration The Effects of Immigration on Welfare Inequality The immigration of unskilled workers has been analyzed by a considerable amount of research, which has noted an ability distribution.
More informationH 0 H = H 0 + V (t), V (t) = gµ B S α qb e e iωt i t Ψ(t) = [H 0 + V (t)]ψ(t) Φ(t) Ψ(t) = e ih0t Φ(t) H 0 e ih0t Φ(t) + ie ih0t t Φ(t) = [
3 3. 3.. H H = H + V (t), V (t) = gµ B α B e e iωt i t Ψ(t) = [H + V (t)]ψ(t) Φ(t) Ψ(t) = e iht Φ(t) H e iht Φ(t) + ie iht t Φ(t) = [H + V (t)]e iht Φ(t) Φ(t) i t Φ(t) = V H(t)Φ(t), V H (t) = e iht V (t)e
More informationy = x x R = 0. 9, R = σ $ = y x w = x y x x w = x y α ε = + β + x x x y α ε = + β + γ x + x x x x' = / x y' = y/ x y' =
y x = α + β + ε =,, ε V( ε) = E( ε ) = σ α $ $ β w ( 0) σ = w σ σ y α x ε = + β + w w w w ε / w ( w y x α β ) = α$ $ W = yw βwxw $β = W ( W) ( W)( W) w x x w x x y y = = x W y W x y x y xw = y W = w w
More informationBvarate Probt Model 0.24% 0.4% 5.%.% %.% Keyword Bvarate Probt Model 6- TEL & FAX: E-mal:
Dscusson Paper No. 508 2000 5 Bvarate Probt Model 0.24% 0.4% 5.%.% 00 0.55%.% Keyword Bvarate Probt Model 6- TEL & FAX: 0727-62-8484 E-mal: suzuk@ser.osaka-u.ac.jp 995 58 8.2% 996 72 334 /3 2 3 996 2 (995)
More informationnsg02-13/ky045059301600033210
φ φ φ φ κ κ α α μ μ α α μ χ et al Neurosci. Res. Trpv J Physiol μ μ α α α β in vivo β β β β β β β β in vitro β γ μ δ μδ δ δ α θ α θ α In Biomechanics at Micro- and Nanoscale Levels, Volume I W W v W
More information研修コーナー
l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
More informationJorgenson F, L : L: Inada lim F =, lim F L = k L lim F =, lim F L = 2 L F >, F L > 3 F <, F LL < 4 λ >, λf, L = F λ, λl 5 Y = Const a L a < α < CES? C
27 nabe@ier.hit-u.ac.jp 27 4 3 Jorgenson Tobin q : Hayashi s Theorem Jordan Saddle Path. GDP % GDP 2. 3. 4.. Tobin q 2 2. Jorgenson F, L : L: Inada lim F =, lim F L = k L lim F =, lim F L = 2 L F >, F
More informationDP
KEIO UNIVERSITY MARKET QUALITY RESEARCH PROJECT (A 21 st Century Center of Excellence Project) DP2004-13 * ** * ** Graduate School of Economics and Graduate School of Business and Commerce, Keio University
More informationp *2 DSGEDynamic Stochastic General Equilibrium New Keynesian *2 2
2013 1 nabe@ier.hit-u.ac.jp 2013 4 11 Jorgenson Tobin q : Hayashi s Theorem : Jordan : 1 investment 1 2 3 4 5 6 7 8 *1 *1 93SNA 1 p.180 1936 100 1970 *2 DSGEDynamic Stochastic General Equilibrium New Keynesian
More informationII III II 1 III ( ) [2] [3] [1] 1 1:
2015 4 16 1. II III II 1 III () [2] [3] 2013 11 18 [1] 1 1: [5] [6] () [7] [1] [1] 1998 4 2008 8 2014 8 6 [1] [1] 2 3 4 5 2. 2.1. t Dt L DF t A t (2.1) A t = Dt L + Dt F (2.1) 3 2 1 2008 9 2008 8 2008
More informationmeiji_resume_1.PDF
β β β (q 1,q,..., q n ; p 1, p,..., p n ) H(q 1,q,..., q n ; p 1, p,..., p n ) Hψ = εψ ε k = k +1/ ε k = k(k 1) (x, y, z; p x, p y, p z ) (r; p r ), (θ; p θ ), (ϕ; p ϕ ) ε k = 1/ k p i dq i E total = E
More information4.9 Hausman Test Time Fixed Effects Model vs Time Random Effects Model Two-way Fixed Effects Model
1 EViews 5 2007 7 11 2010 5 17 1 ( ) 3 1.1........................................... 4 1.2................................... 9 2 11 3 14 3.1 Pooled OLS.............................................. 14
More informationchap9.dvi
9 AR (i) (ii) MA (iii) (iv) (v) 9.1 2 1 AR 1 9.1.1 S S y j = (α i + β i j) D ij + η j, η j = ρ S η j S + ε j (j =1,,T) (1) i=1 {ε j } i.i.d(,σ 2 ) η j (j ) D ij j i S 1 S =1 D ij =1 S>1 S =4 (1) y j =
More information1990年代以降の日本の経済変動
1990 * kenichi.sakura@boj.or.jp ** hitoshi.sasaki@boj.or.jp *** masahiro.higo@boj.or.jp No.05-J-10 2005 12 103-8660 30 * ** *** 1990 2005 12 1990 1990 1990 2005 11 2425 BIS E-mail: kenichi.sakura@boj.or.jp
More information1 12 *1 *2 (1991) (1992) (2002) (1991) (1992) (2002) 13 (1991) (1992) (2002) *1 (2003) *2 (1997) 1
2005 1 1991 1996 5 i 1 12 *1 *2 (1991) (1992) (2002) (1991) (1992) (2002) 13 (1991) (1992) (2002) *1 (2003) *2 (1997) 1 2 13 *3 *4 200 1 14 2 250m :64.3km 457mm :76.4km 200 1 548mm 16 9 12 589 13 8 50m
More information1 (Berry,1975) 2-6 p (S πr 2 )p πr 2 p 2πRγ p p = 2γ R (2.5).1-1 : : : : ( ).2 α, β α, β () X S = X X α X β (.1) 1 2
2005 9/8-11 2 2.2 ( 2-5) γ ( ) γ cos θ 2πr πρhr 2 g h = 2γ cos θ ρgr (2.1) γ = ρgrh (2.2) 2 cos θ θ cos θ = 1 (2.2) γ = 1 ρgrh (2.) 2 2. p p ρgh p ( ) p p = p ρgh (2.) h p p = 2γ r 1 1 (Berry,1975) 2-6
More information(2004 ) 2 (A) (B) (C) 3 (1987) (1988) Shimono and Tachibanaki(1985) (2008) , % 2 (1999) (2005) 3 (2005) (2006) (2008)
,, 23 4 30 (i) (ii) (i) (ii) Negishi (1960) 2010 (2010) ( ) ( ) (2010) E-mail:fujii@econ.kobe-u.ac.jp E-mail:082e527e@stu.kobe-u.ac.jp E-mail:iritani@econ.kobe-u.ac.jp 1 1 16 (2004 ) 2 (A) (B) (C) 3 (1987)
More informationZ: Q: R: C: sin 6 5 ζ a, b
Z: Q: R: C: 3 3 7 4 sin 6 5 ζ 9 6 6............................... 6............................... 6.3......................... 4 7 6 8 8 9 3 33 a, b a bc c b a a b 5 3 5 3 5 5 3 a a a a p > p p p, 3,
More information財政赤字の経済分析:中長期的視点からの考察
1998 1999 1998 1999 10 10 1999 30 (1982, 1996) (1997) (1977) (1990) (1996) (1997) (1996) Ihori, Doi, and Kondo (1999) (1982) (1984) (1987) (1993) (1997) (1998) CAPM 1980 (time inconsistency) Persson, Persson
More informationshuron.dvi
01M3065 1 4 1.1........................... 4 1.2........................ 5 1.3........................ 6 2 8 2.1.......................... 8 2.2....................... 9 3 13 3.1.............................
More information(004)(004) (006)(009a,b)(011) Hashmoto and Cohn(1997) Hashmoto and Cohn(1997) FFCQ(Flexble Fxed Cost Quadratc functon) Hashmoto an
010 11 011 3 *C) 0530186 0 009 6 004 1 (004)(004) (006)(009a,b)(011) Hashmoto and Cohn(1997) Hashmoto and Cohn(1997) 1991 94 FFCQ(Flexble Fxed Cost Quadratc functon) Hashmoto and Cohn(1997) 1 Hashmoto
More informationウェーブレットによる経済分析
E-malmasakazu.nada@boj.or.jp E-malkouchrou.kamada@boj.or.jp wavelet J.J. Morlet D. D. Gabor uncertanty prncple Conway and Frame Schlecher wave let DWT: dscrete wavelet transformcwt: contnuous wavelet
More informationuntitled
2012.08.10 START 3 B(3, 0.5) N =8 3 2 1 0 1 3 3 1 w, =1, 2,...,N w 3 2 1 1 N 0 1 N 1( ) w, =1, 2,...,N w > 0 w = c log N, =1, 2,...,N, c>0 P[ [x, ) ]= N 1 { w x} = 1 N { Ne x/c } e x/c X w x 1 N 2( ) Zpf
More informationNo δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2
No.2 1 2 2 δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i δx j (5) δs 2 = δx i δx i + 2 u i δx i δx j = δs 2 + 2s ij δx i δx j
More information.2 ρ dv dt = ρk grad p + 3 η grad (divv) + η 2 v.3 divh = 0, rote + c H t = 0 dive = ρ, H = 0, E = ρ, roth c E t = c ρv E + H c t = 0 H c E t = c ρv T
NHK 204 2 0 203 2 24 ( ) 7 00 7 50 203 2 25 ( ) 7 00 7 50 203 2 26 ( ) 7 00 7 50 203 2 27 ( ) 7 00 7 50 I. ( ν R n 2 ) m 2 n m, R = e 2 8πε 0 hca B =.09737 0 7 m ( ν = ) λ a B = 4πε 0ħ 2 m e e 2 = 5.2977
More information低費用航空会社による運賃競争の時間効果とスピルオーバー効果の計測:米国内複占市場のケース
1 3 Π Π 1 = = 1 1 1 1 1 ( A q γ q ) q α θ q q 1 1 1 1 ( A q γ q ) q α b θ q q mc ( α >, 0 < 1) 1 = α θ1q1 1 θ1 < ( α b) θ Q ( < θ < α b, < < α mc = 0 0 b ) γ b θ < θ < θ 1 0 < γ < γ1 1 0 1 < γ = 1 1 0
More informationPart () () Γ Part ,
Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35
More information産業・企業レベルデータで見た日本の経済成長.pdf
2003 11 10 IT IT JIP JCER ) 2003 CD-ROM http://www.esri.go.jp/jp/archive/bun/bun170/170index. html 1 JIP Jorgenson, Mun, andstiroh (2002) GDP 2 3 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981
More information2 1 κ c(t) = (x(t), y(t)) ( ) det(c (t), c x (t)) = det (t) x (t) y (t) y = x (t)y (t) x (t)y (t), (t) c (t) = (x (t)) 2 + (y (t)) 2. c (t) =
1 1 1.1 I R 1.1.1 c : I R 2 (i) c C (ii) t I c (t) (0, 0) c (t) c(i) c c(t) 1.1.2 (1) (2) (3) (1) r > 0 c : R R 2 : t (r cos t, r sin t) (2) C f : I R c : I R 2 : t (t, f(t)) (3) y = x c : R R 2 : t (t,
More information1. 2. (Rowthorn, 2014) / 39 1
,, 43 ( ) 2015 7 18 ( ) E-mail: sasaki@econ.kyoto-u.ac.jp 1 / 39 1. 2. (Rowthorn, 2014) 3. 4. 5. 6. 7. 2 / 39 1 ( 1). ( 2). = +. 1. g. r. r > g ( 3).. 3 / 39 2 50% Figure I.1. Income inequality in the
More informationCOE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate Scho
COE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate School of Economics and Institute of Economic Research
More information橡紙目次第1章1
1 1 1 AstA A st t Q + = 0 Ast A st t y = bst ( y) t (1.1) (1.) y 1 Q + = 0 t b st q (1.3) (1.3) b st y Q + = q t (1.4) Abbott(1979) Q Q y + ( β ) + ga + gas f = 0 t A q u (1.5) Q Q y + ( β ) + ga + Sf
More informationAuerbach and Kotlikoff(1987) (1987) (1988) 4 (2004) 5 Diamond(1965) Auerbach and Kotlikoff(1987) 1 ( ) ,
,, 2010 8 24 2010 9 14 A B C A (B Negishi(1960) (C) ( 22 3 27 ) E-mail:fujii@econ.kobe-u.ac.jp E-mail:082e527e@stu.kobe-u.ac.jp E-mail:iritani@econ.kobe-u.ac.jp 1 1 1 2 3 Auerbach and Kotlikoff(1987) (1987)
More information= M + M + M + M M + =.,. f = < ρ, > ρ ρ. ρ f. = ρ = = ± = log 4 = = = ± f = k k ρ. k
7 b f n f} d = b f n f d,. 5,. [ ] ɛ >, n ɛ + + n < ɛ. m. n m log + < n m. n lim sin kπ sin kπ } k π sin = n n n. k= 4 f, y = r + s, y = rs f rs = f + r + sf y + rsf yy + f y. f = f =, f = sin. 5 f f =.
More informationm dv = mg + kv2 dt m dv dt = mg k v v m dv dt = mg + kv2 α = mg k v = α 1 e rt 1 + e rt m dv dt = mg + kv2 dv mg + kv 2 = dt m dv α 2 + v 2 = k m dt d
m v = mg + kv m v = mg k v v m v = mg + kv α = mg k v = α e rt + e rt m v = mg + kv v mg + kv = m v α + v = k m v (v α (v + α = k m ˆ ( v α ˆ αk v = m v + α ln v α v + α = αk m t + C v α v + α = e αk m
More information2010 9 2011 3 *C) 20530186 20 16 20 5 81 1 (1986)(1987) (1989)(1996)(2010)(2002) (1989)(1992)(1998) (1997)NTT(1993) (1997)(1994)JR(2009(2009) (2002)(2000) (1989) 1 2 2004 (2004)(2004)(2006)(2009a,b) (2004)(2004)(2006)(2009a
More informationKEIRIN
KEIRIN KEIRIN PCOSS CIO PC PC OSS OSS 2003 CIO 2003 IT IT 2006 2006 IT IT IT IT 2008 2008 IT IT 2001 2001 5IT IT 5IT IT IT IT (NGN) Web2.0 (NGN) Web2.0 2005 IT CIO 2005 2005 IT CIO 2006 CIOIT IT SE 2006
More informationD = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j
6 6.. [, b] [, d] ij P ij ξ ij, η ij f Sf,, {P ij } Sf,, {P ij } k m i j m fξ ij, η ij i i j j i j i m i j k i i j j m i i j j k i i j j kb d {P ij } lim Sf,, {P ij} kb d f, k [, b] [, d] f, d kb d 6..
More information液晶の物理1:連続体理論(弾性,粘性)
The Physics of Liquid Crystals P. G. de Gennes and J. Prost (Oxford University Press, 1993) Liquid crystals are beautiful and mysterious; I am fond of them for both reasons. My hope is that some readers
More information2. (1) R. A Fsher 4) Maddala(1993) 5) Matyas and Sevestre(1996) 6) Baum- Snow(2007) 7) Baum-Snow Alonso (2004) 8) (2002) 9) (2002) 10)
1 2 3 1 657-8501 1-1 E-mal: koke@lon.kobe-u.ac.jp 2 732-0052 2-10-11 E-mal: kenj.hra@fukken.co.jp 3 101-0032 3-8-15 E-mal: kesuke.sato@fukken.co.jp Key Words : ex-post analyss, fxed effect model, accessblty,
More information24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x
24 I 1.1.. ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 1 (t), x 2 (t),, x n (t)) ( ) ( ), γ : (i) x 1 (t),
More information物価変動の決定要因について ― 需給ギャップと物価変動の関係の国際比較を中心に―
NAIRU NAIRU NAIRU GDPGDP NAIRUNon- Accelerating Inflation Rate of Unemployment GDP GDP NAIRU Lown and RichFisher, Mahadeva and Whitley raw materials G NAIRUTurnerFai WatanabeNAIRU Watanabe nested NAIRU
More information日本の教育経済学:実証分析の展望と課題.pdf
ESRI Discussion Paper Series No69 by October 2003 Economic and Social Research Institute Cabinet Office Tokyo, Japan 2 3 1 2 3 4 5 6 6 4 1 2 3 4 * 2003 8 7 B603 4 1 Schultz 1963 Becker 1964 human capital
More information4.6: 3 sin 5 sin θ θ t θ 2t θ 4t : sin ωt ω sin θ θ ωt sin ωt 1 ω ω [rad/sec] 1 [sec] ω[rad] [rad/sec] 5.3 ω [rad/sec] 5.7: 2t 4t sin 2t sin 4t
1 1.1 sin 2π [rad] 3 ft 3 sin 2t π 4 3.1 2 1.1: sin θ 2.2 sin θ ft t t [sec] t sin 2t π 4 [rad] sin 3.1 3 sin θ θ t θ 2t π 4 3.2 3.1 3.4 3.4: 2.2: sin θ θ θ [rad] 2.3 0 [rad] 4 sin θ sin 2t π 4 sin 1 1
More information(2005a) (2005) (2004-2005) (2004) (2003) 3 (2003) (2003) 2
Offshore Software Outsourcing in Japan 1 2 3 4 5 1 2001 500 3 60 10 MD IT 2003 50 2004 236-7 BPO 1 100 2 (2000) (2001) (2001) (2004) (2005) (2005b) (2005) (2005) 1 (2005a) (2005) (2004-2005) (2004) (2003)
More informationCVMに基づくNi-Al合金の
CV N-A (-' by T.Koyama ennard-jones fcc α, β, γ, δ β α γ δ = or α, β. γ, δ α β γ ( αβγ w = = k k k ( αβγ w = ( αβγ ( αβγ w = w = ( αβγ w = ( αβγ w = ( αβγ w = ( αβγ w = ( αβγ w = ( βγδ w = = k k k ( αγδ
More informationlimit&derivative
- - 7 )................................................................................ 5.................................. 7.. e ).......................... 9 )..........................................
More informationuntitled
A hydrodynamc lmt of move-to-front rules and ts applcaton to web rankngs 2010.07.15 START Move-to-front cf. Move-to-front N N 1( ) (move-to-front ) N =1,,N t 0 t X (N) (t) X (N) (0) = x (N) X (N) (t) =
More informationTERG
Dscusson Paper No. 268 小標本特性に優れたパネル単位根検定 千木良弘朗 山本拓 2011 年 7 月 TOHOKU ECONOMICS RESEARCH GROUP GRADUATE SCHOOL OF ECONOMICS AND MANAGEMENT TOHOKU UNIVERSITY KAWAUCHI, AOBA-KU, SENDAI, 980-8576 JAPAN Λ z
More information通勤混雑と家賃関数*
CIRJE-J-30 CIRJE 000 8 5 9 3 Estmaton of Fatgue Cost of Commutng Congeston and Optmal Congeston Fare Ths paper has three ams. Frst, we estmate a hedonc housng rent functon along the Chuo Lne n Tokyo wth
More information.....Z...^.[.......\..
15 10 16 42 55 55 56 60 62 199310 1995 134 10 8 15 1 13 1311 a s d f 141412 2 g h j 376104 3 104102 232 4 5 51 30 53 27 36 6 Y 7 8 9 10 8686 86 11 1310 15 12 Z 13 14 15 16 102193 23 1712 60 27 17 18 Z
More information30
3 ............................................2 2...........................................2....................................2.2...................................2.3..............................
More informationS I. dy fx x fx y fx + C 3 C vt dy fx 4 x, y dy yt gt + Ct + C dt v e kt xt v e kt + C k x v k + C C xt v k 3 r r + dr e kt S Sr πr dt d v } dt k e kt
S I. x yx y y, y,. F x, y, y, y,, y n http://ayapin.film.s.dendai.ac.jp/~matuda n /TeX/lecture.html PDF PS yx.................................... 3.3.................... 9.4................5..............
More information10
z c j = N 1 N t= j1 [ ( z t z ) ( )] z t j z q 2 1 2 r j /N j=1 1/ N J Q = N(N 2) 1 N j j=1 r j 2 2 χ J B d z t = z t d (1 B) 2 z t = (z t z t 1 ) (z t 1 z t 2 ) (1 B s )z t = z t z t s _ARIMA CONSUME
More information(1.2) T D = 0 T = D = 30 kn 1.2 (1.4) 2F W = 0 F = W/2 = 300 kn/2 = 150 kn 1.3 (1.9) R = W 1 + W 2 = = 1100 N. (1.9) W 2 b W 1 a = 0
1 1 1.1 1.) T D = T = D = kn 1. 1.4) F W = F = W/ = kn/ = 15 kn 1. 1.9) R = W 1 + W = 6 + 5 = 11 N. 1.9) W b W 1 a = a = W /W 1 )b = 5/6) = 5 cm 1.4 AB AC P 1, P x, y x, y y x 1.4.) P sin 6 + P 1 sin 45
More information2 2 1?? 2 1 1, 2 1, 2 1, 2, 3,... 1, 2 1, 3? , 2 2, 3? k, l m, n k, l m, n kn > ml...? 2 m, n n m
2009 IA I 22, 23, 24, 25, 26, 27 4 21 1 1 2 1! 4, 5 1? 50 1 2 1 1 2 1 4 2 2 2 1?? 2 1 1, 2 1, 2 1, 2, 3,... 1, 2 1, 3? 2 1 3 1 2 1 1, 2 2, 3? 2 1 3 2 3 2 k, l m, n k, l m, n kn > ml...? 2 m, n n m 3 2
More information,. Black-Scholes u t t, x c u 0 t, x x u t t, x c u t, x x u t t, x + σ x u t, x + rx ut, x rux, t 0 x x,,.,. Step 3, 7,,, Step 6., Step 4,. Step 5,,.
9 α ν β Ξ ξ Γ γ o δ Π π ε ρ ζ Σ σ η τ Θ θ Υ υ ι Φ φ κ χ Λ λ Ψ ψ µ Ω ω Def, Prop, Th, Lem, Note, Remark, Ex,, Proof, R, N, Q, C [a, b {x R : a x b} : a, b {x R : a < x < b} : [a, b {x R : a x < b} : a,
More information5 5.1 E 1, E 2 N 1, N 2 E tot N tot E tot = E 1 + E 2, N tot = N 1 + N 2 S 1 (E 1, N 1 ), S 2 (E 2, N 2 ) E 1, E 2 S tot = S 1 + S 2 2 S 1 E 1 = S 2 E
5 5.1 E 1, E 2 N 1, N 2 E tot N tot E tot = E 1 + E 2, N tot = N 1 + N 2 S 1 (E 1, N 1 ), S 2 (E 2, N 2 ) E 1, E 2 S tot = S 1 + S 2 2 S 1 E 1 = S 2 E 2, S 1 N 1 = S 2 N 2 2 (chemical potential) µ S N
More information..3. Ω, Ω F, P Ω, F, P ). ) F a) A, A,..., A i,... F A i F. b) A F A c F c) Ω F. ) A F A P A),. a) 0 P A) b) P Ω) c) [ ] A, A,..., A i,... F i j A i A
.. Laplace ). A... i),. ω i i ). {ω,..., ω } Ω,. ii) Ω. Ω. A ) r, A P A) P A) r... ).. Ω {,, 3, 4, 5, 6}. i i 6). A {, 4, 6} P A) P A) 3 6. ).. i, j i, j) ) Ω {i, j) i 6, j 6}., 36. A. A {i, j) i j }.
More informationohpmain.dvi
fujisawa@ism.ac.jp 1 Contents 1. 2. 3. 4. γ- 2 1. 3 10 5.6, 5.7, 5.4, 5.5, 5.8, 5.5, 5.3, 5.6, 5.4, 5.2. 5.5 5.6 +5.7 +5.4 +5.5 +5.8 +5.5 +5.3 +5.6 +5.4 +5.2 =5.5. 10 outlier 5 5.6, 5.7, 5.4, 5.5, 5.8,
More informationd > 2 α B(y) y (5.1) s 2 = c z = x d 1+α dx ln u 1 ] 2u ψ(u) c z y 1 d 2 + α c z y t y y t- s 2 2 s 2 > d > 2 T c y T c y = T t c = T c /T 1 (3.
5 S 2 tot = S 2 T (y, t) + S 2 (y) = const. Z 2 (4.22) σ 2 /4 y = y z y t = T/T 1 2 (3.9) (3.15) s 2 = A(y, t) B(y) (5.1) A(y, t) = x d 1+α dx ln u 1 ] 2u ψ(u), u = x(y + x 2 )/t s 2 T A 3T d S 2 tot S
More information第86回日本感染症学会総会学術集会後抄録(I)
κ κ κ κ κ κ μ μ β β β γ α α β β γ α β α α α γ α β β γ μ β β μ μ α ββ β β β β β β β β β β β β β β β β β β γ β μ μ μ μμ μ μ μ μ β β μ μ μ μ μ μ μ μ μ μ μ μ μ μ β
More informationS I. dy fx x fx y fx + C 3 C dy fx 4 x, y dy v C xt y C v e kt k > xt yt gt [ v dt dt v e kt xt v e kt + C k x v + C C k xt v k 3 r r + dr e kt S dt d
S I.. http://ayapin.film.s.dendai.ac.jp/~matuda /TeX/lecture.html PDF PS.................................... 3.3.................... 9.4................5.............. 3 5. Laplace................. 5....
More information国際学研究 2‐1☆/目次(2‐1)
Economic Development and Structural Changes : A Look through Productivity and Wages Akira Kohsaka Abstract : This paper examines the dynamisms of structural changes in the process of economic development
More information) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8)
4 4 ) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8) a b a b = 6i j 4 b c b c 9) a b = 4 a b) c = 7
More information繰り返しゲームの新展開:
CIRJE-J-65 001 10 001 10 5 New Progress n Repeated Games: Implct Colluson wth Prvate Montorng Htosh Matsushma Faculty of Economcs, Unversty of Tokyo October 5, 001 Abstract The present paper provdes a
More informationO1-1 O1-2 O1-3 O1-4 O1-5 O1-6
O1-1 O1-2 O1-3 O1-4 O1-5 O1-6 O1-7 O1-8 O1-9 O1-10 O1-11 O1-12 O1-13 O1-14 O1-15 O1-16 O1-17 O1-18 O1-19 O1-20 O1-21 O1-22 O1-23 O1-24 O1-25 O1-26 O1-27 O1-28 O1-29 O1-30 O1-31 O1-32 O1-33 O1-34 O1-35
More information[ ] 0.1 lim x 0 e 3x 1 x IC ( 11) ( s114901) 0.2 (1) y = e 2x (x 2 + 1) (2) y = x/(x 2 + 1) 0.3 dx (1) 1 4x 2 (2) e x sin 2xdx (3) sin 2 xdx ( 11) ( s
[ ]. lim e 3 IC ) s49). y = e + ) ) y = / + ).3 d 4 ) e sin d 3) sin d ) s49) s493).4 z = y z z y s494).5 + y = 4 =.6 s495) dy = 3e ) d dy d = y s496).7 lim ) lim e s49).8 y = e sin ) y = sin e 3) y =
More information「国債の金利推定モデルに関する研究会」報告書
: LG 19 7 26 2 LG Quadratic Gaussian 1 30 30 3 4 2,,, E-mail: kijima@center.tmu.ac.jp, E-mail: tanaka-keiichi@tmu.ac.jp 1 L G 2 1 L G r L t),r G t) L r L t) G r G t) r L t) h G t) =r G t) r L t) r L t)
More informationAutumn II III Zon and Muysken 2005 Zon and Muysken 2005 IV II 障害者への所得移転の経済効果 分析に用いるデータ
212 Vol. 44 No. 2 I はじめに 2008 1 2 Autumn 08 213 II III Zon and Muysken 2005 Zon and Muysken 2005 IV II 障害者への所得移転の経済効果 17 18 1 分析に用いるデータ 1 2005 10 12 200 2 2006 9 12 1 1 2 129 35 113 3 1 2 6 1 2 3 4 4 1
More information遺産相続、学歴及び退職金の決定要因に関する実証分析 『家族関係、就労、退職金及び教育・資産の世代間移転に関する世帯アンケート調査』
2-1. (2-1 ) (2-2 ) (2-3 ) (Hayashi [1986]Dekle [1989]Barthold and Ito [1992] [1996]Campbell [1997] [1998]Shimono and Ishikawa [2002]Shimono and Otsuki [2006] [2008]Horioka [2009]) 1 2-1-1 2-1-1-1 8 (1.
More informationO x y z O ( O ) O (O ) 3 x y z O O x v t = t = 0 ( 1 ) O t = 0 c t r = ct P (x, y, z) r 2 = x 2 + y 2 + z 2 (t, x, y, z) (ct) 2 x 2 y 2 z 2 = 0
9 O y O ( O ) O (O ) 3 y O O v t = t = 0 ( ) O t = 0 t r = t P (, y, ) r = + y + (t,, y, ) (t) y = 0 () ( )O O t (t ) y = 0 () (t) y = (t ) y = 0 (3) O O v O O v O O O y y O O v P(, y,, t) t (, y,, t )
More information振動と波動
Report JS0.5 J Simplicity February 4, 2012 1 J Simplicity HOME http://www.jsimplicity.com/ Preface 2 Report 2 Contents I 5 1 6 1.1..................................... 6 1.2 1 1:................ 7 1.3
More information構造と連続体の力学基礎
II 37 Wabash Avenue Bridge, Illinois 州 Winnipeg にある歩道橋 Esplanade Riel 橋6 6 斜張橋である必要は多分無いと思われる すぐ横に道路用桁橋有り しかも塔基部のレストランは 8 年には営業していなかった 9 9. 9.. () 97 [3] [5] k 9. m w(t) f (t) = f (t) + mg k w(t) Newton
More informationall.dvi
38 5 Cauchy.,,,,., σ.,, 3,,. 5.1 Cauchy (a) (b) (a) (b) 5.1: 5.1. Cauchy 39 F Q Newton F F F Q F Q 5.2: n n ds df n ( 5.1). df n n df(n) df n, t n. t n = df n (5.1) ds 40 5 Cauchy t l n mds df n 5.3: t
More information磁性物理学 - 遷移金属化合物磁性のスピンゆらぎ理論
email: takahash@sci.u-hyogo.ac.jp May 14, 2009 Outline 1. 2. 3. 4. 5. 6. 2 / 262 Today s Lecture: Mode-mode Coupling Theory 100 / 262 Part I Effects of Non-linear Mode-Mode Coupling Effects of Non-linear
More information2 1 1 α = a + bi(a, b R) α (conjugate) α = a bi α (absolute value) α = a 2 + b 2 α (norm) N(α) = a 2 + b 2 = αα = α 2 α (spure) (trace) 1 1. a R aα =
1 1 α = a + bi(a, b R) α (conjugate) α = a bi α (absolute value) α = a + b α (norm) N(α) = a + b = αα = α α (spure) (trace) 1 1. a R aα = aα. α = α 3. α + β = α + β 4. αβ = αβ 5. β 0 6. α = α ( ) α = α
More information