.. est table TwoSLS1 TwoSLS2 GMM het,b(%9.5f) se Variable TwoSLS1 TwoSLS2 GMM_het hi_empunion totchr
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1 3,. Cameron and Trivedi (2010) Microeconometrics Using Stata, Revised Edition, Stata Press 6 Linear instrumentalvariables regression 9 Linear panel-data models: Extensions.. GMM xtabond., GMM(Generalized Method of Moments). GMM TSLS( ), GMM TSLS, TSLS, TwoSLS1.. use mus06data.dta, clear. global x2list totchr age female blhisp linc. ivregress 2sls ldrugexp (hi empunion=ssiratio ) $x2list,vce(robust) ( ). estimates store TwoSLS ( ) z. E {z i (y i x iβ)} = 0 (14) 2. x2list, ivmodel,.. global ivmodel ldrugexp (hi empunion=ssiratio multlc) $x2list. quietly ivregress 2sls $ivmodel,vce(robust). est store TwoSLS2 GMM.. quietly ivregress gmm $ivmodel,wmatrix(robust). est store GMM het 4 GMM 23
2 .. est table TwoSLS1 TwoSLS2 GMM het,b(%9.5f) se Variable TwoSLS1 TwoSLS2 GMM_het hi_empunion totchr age female blhisp linc _cons legend: b/se, TwoSLS1 TwoSLS2 GMM het 10%., TwoSLS2 GMM het,,.. 2, ( ). gmm wmatrix(robust), 2sls vce(robust) ,. (test of overientifying restriction) 6.,,. 2 gmm.. quietly ivregress gmm $ivmodel,wmatrix(robust). estat overid Test of overidentifying restriction: Hansen s J chi2(1) = (p = ) 5 Microeconometrics Using Stata, Revised Edition gmm 6 Hansen Sargan Hansen-Sargan 24
3 5%, ivregress gmm ldrugexp (hi empunion=ssiratio lowincome multlc firmsz) $x2list, >wmatrix(robust) Instrumental variables (GMM) regression Number of obs = 10,089 Wald chi2(6) = R-squared = GMM weight matrix: Robust Root MSE = Robust ldrugexp Coef. Std. Err. z P> z [95% Conf. Interval] hi_empunion totchr age female blhisp linc _cons Instrumented: Instruments: hi_empunion totchr age female blhisp linc ssiratio lowincome multlc firmsz. estat overid Test of overidentifying restriction: Hansen s J chi2(3) = (p = ) 1%,., hi empunion ,. GMM, TSLS,. 3.3 GMM,. Microeconometrics Using Stata, Revised Edition, Stata Press 9 Linear panel-data models: Extensions.,. y it = γ 1 y i,t γ p y i,t p + x itβ + α i + ϵ it, t = p + 1,..., T (15) α i. x it ϵ it., γ 1,, γ p β 7. AR(1) y it = γ 1 y i,t 1 + α i + ϵ it 7 25
4 ,. 1 ϵ i1. y,., 2, γ 1 1., γ 1 0, α i.,,. 15 within 8., within y i,t 1 ȳ i ϵ it ϵ i., within p = 1,. y it = γ 1 y i,t 1 + x itβ + α i + ϵ it ȳ i = γ 1 ȳ i + x iβ + α i + ϵ i, γ, within. y it ȳ i = γ 1 (y i,t 1 ȳ i ) + (x it x i) β+ (ϵ it ϵ i ) 15 ϵ it, y i,t 1 ȳ i ϵ it ϵ i., y i,t 1 ϵ i,t 1., ϵ i, ϵ i,t 1, y i,t 1 ϵ i. (y i,t 1 ȳ i ),,., within,,,. FD within FD(first difference).,. y it = γ 1 y i,t γ p y i,t p + x itβ + ϵ it, t = p + 1,..., T (16) 15. within,., ϵ it., FD, y i,t 1 ϵ it, OLS. y it = γ 1 y i,t 1 + γ 2 y i,t 2 + ϵ it, t = p + 1,..., T, y i,t 1 = y i,t 1 y i,t 2 ϵ it = ϵ it ϵ i,t 1., y i,t 1 ϵ i,t 1,., y i,t 2. (y i,t 2 y i,t 3 ) (ϵ it ϵ i,t 1 ),, y i,t 2 ϵ it 8 within 26
5 ., FD p 2. Anderson and Hsiao (1981) ϵ it y i,t 2 y i,t 1., x it,., Arellano and Bond (1991),, ϵ it,. Arellano and Bond. 3.4 Arellano-Bond : AR(2).,. 16 AR,,. GMM, Arellano-Bond. y it = α + γ 1 y i,t 1 + γ 2 y i,t 2 + ϵ it, t = 4, 5, 6, 7 7.,, 4. t y y y ( 1) y ( 2) ϵ ϵ 1 y i1 ϵ i1 2 y i2 y i2 y i1 ϵ i2 ϵ i2 ϵ i1 3 y i3 y i3 y i2 y i2 y i1 ϵ i3 ϵ i3 ϵ i2 4 y i4 y i4 y i3 y i3 y i2 y i2 y i1 ϵ i4 ϵ i4 ϵ i3 5 y i5 y i5 y i4 y i4 y i3 y i3 y i2 ϵ i5 ϵ i5 ϵ i4 6 y i6 y i6 y i5 y i5 y i4 y i4 y i3 ϵ i6 ϵ i6 ϵ i5 7 y i7 y i7 y i6 y i6 y i5 y i5 y i4 ϵ i7 ϵ i7 ϵ i6, t = 4 ϵ i4 y i1 y i2, t = 5 ϵ i5 y i1, y i2, y i3, t = 6 4, t = 7 5., = 14,,
6 mus08psidextract.dta, AR(2)... use mus08psidextract.dta, clear. xtabond lwage,lags(2) vce(robust) Arellano-Bond dynamic panel-data estimation Number of obs = 2,380 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 4 avg = 4 max = 4 Number of instruments = 15 Wald chi2(2) = One-step results (Std. Err. adjusted for clustering on id) Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L _cons Instruments for differenced equation GMM-type: L(2/.).lwage Instruments for level equation Standard: _cons 4,165,, ( ) = 2, 380. lwage L1. L2. y i,t 1 y i,t 2., FD. L(2/.), t y i,t 2, y i,t 3,..., y i,1., = 0.84,. GMM 1, (S.E ) 2. 28
7 . xtabond lwage,lags(2) twostep vce(robust) Arellano-Bond dynamic panel-data estimation Number of obs = 2,380 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 4 avg = 4 max = 4 Number of instruments = 15 Wald chi2(2) = Two-step results (Std. Err. adjusted for clustering on id) WC-Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L _cons Instruments for differenced equation GMM-type: L(2/.).lwage Instruments for level equation Standard: _cons 1 2, S.E. 2. T, Arellano-Bond,,. maxldep()., t y i,t 2.. xtabond lwage,lags(2) vce(robust) maxldep(1) Arellano-Bond dynamic panel-data estimation Number of obs = 2,380 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 4 avg = 4 max = 4 Number of instruments = 5 Wald chi2(2) = One-step results (Std. Err. adjusted for clustering on id) Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L _cons Instruments for differenced equation GMM-type: L(2/2).lwage 29
8 Instruments for level equation Standard: _cons t = 4 7 y i2, y i3, y i4, y i5 5.,, 6., maxldep(2), S.E., xtabond xtabond Arellano-Bond.,,, fem( ),blk( ),ed( ). occ( 1), south( 1), smsa( 1),ind( 1)... xtabond lwage occ south smsa ind,lags(2) maxldep(3) pre(wks,lag(1,2)) > endogenous(ms,lag(0,2)) endogenous(union,lag(0,2)) twostep vce(robust) > artests(3) Arellano-Bond dynamic panel-data estimation Number of obs = 2,380 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 4 avg = 4 max = 4 Number of instruments = 40 Wald chi2(10) = Two-step results (Std. Err. adjusted for clustering on id) WC-Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L wks L ms union occ south smsa ind _cons Instruments for differenced equation GMM-type: L(2/4).lwage L(1/2).L.wks L(2/3).ms L(2/3).union Standard: D.occ D.south D.smsa D.ind Instruments for level equation Standard: _cons 30
9 , occ, endogenous() ms union., lag(0,2), 0., ms union 1., L1.., 2., ms ms t 1 ms t 2., pre(wks,lag(1,2)) wks 9. endogenous().,, 5%. ϵ it., AR(2), 2 ϵ it ϵ i,t k (k 2)., COV ( ϵ it, ϵ i,t 1 ) = COV (ϵ it ϵ i,t 1, ϵ i,t 1 ϵ i,t 2 ) = COV (ϵ i,t 1, ϵ i,t 1 ) 0,, k 2. COV ( ϵ it, ϵ i,t k ) = COV (ϵ it ϵ i,t 1, ϵ i,t k ϵ i,t k 1 ) = 0. artests(). artests(3)., artests(2),, 3.. estat abond Arellano-Bond test for zero autocorrelation in first-differenced errors Order z Prob > z H0: no autocorrelation. 2,.,. estat sargan, vce(robust),,. 9 31
10 . xtabond lwage occ south smsa ind,lags(2) maxldep(3) pre(wks,lag(1,2)) > endogenous(ms,lag(0,2)) endogenous(union,lag(0,2)) twostep artests(3). estat sargan. estat sargan Sargan test of overidentifying restrictions H0: overidentifying restrictions are valid chi2(29) = Prob > chi2 = %. 3.6 xtdpdsys Arellano-Bond E (y is ϵ it ) = 0 (s t 2), FD y i,t 2, y i,t 3,.... Arellano and Bover (1995) Blundell and Bond (1998)., E (y i,y 1 ϵ it ) = 0, Stata xtdpdsys xtdpdsys lwage occ south smsa ind,lags(2) maxldep(3) pre(wks,lag(1,2)) >endogenous(ms,lag(0,2)) endogenous(union,lag(0,2)) twostep vce(robust) >artests(3) 10 ado xtabond2 xtabond 32
11 System dynamic panel-data estimation Number of obs = 2,975 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 5 avg = 5 max = 5 Number of instruments = 60 Wald chi2(10) = Two-step results WC-Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L wks L ms union occ south smsa ind _cons Instruments for differenced equation GMM-type: L(2/4).lwage L(1/2).L.wks L(2/3).ms L(2/3).union Standard: D.occ D.south D.smsa D.ind Instruments for level equation GMM-type: LD.lwage LD.wks LD.ms LD.union Standard: _cons ,., 10-60%. xtabond, estat abond,, vce(robust), estat sargan. 3.7 xtdpd ϵ it., estat abond?. ϵ it xtdpd Stata,.. xtdpd L(0/2).lwage L(0/1).wks occ south smsa ind ms union, >div(occ south smsa ind) dgmmiv(lwage,lagrange(2 4)) >dgmmiv(ms union,lagrange(2 3)) dgmmiv(l.wks,lagrange(1 2)) >lgmmiv(lwage wks ms union) twostep vce(robust) artests(3) 33
12 Dynamic panel-data estimation Number of obs = 2,975 Group variable: id Number of groups = 595 Time variable: t Obs per group: min = 5 avg = 5 max = 5 Number of instruments = 60 Wald chi2(10) = Two-step results (Std. Err. adjusted for clustering on id) WC-Robust lwage Coef. Std. Err. z P> z [95% Conf. Interval] lwage L L wks L occ south smsa ind ms union _cons Instruments for differenced equation GMM-type: L(2/4).lwage L(2/3).ms L(2/3).union L(1/2).L.wks Standard: D.occ D.south D.smsa D.ind Instruments for level equation GMM-type: LD.lwage LD.wks LD.ms LD.union Standard: _cons 15 ϵ it MA(1),, ϵ it = η it + δη i,t 1. η it i.i.id.. GMM GMM. GMM TSLS,. ( ),, GMM,.. 34
13 ,... Y i = β 0 + β 1 X 1i + + β k X ki + β k+1 W 1i + + β k+r W ri + u i, i = 1,..., n, Y i β 0, β 1,..., β k+r X 1i,..., X ki k, u i. W 1i...., W ri r u i. u i,. Z 1i..., Z mi m. (m > k),. m < k, ( ), m = k.,,,
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1 1. 2014 6 2014 6 10 10% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti 1029 0.35 0.40 One-sample test of proportion x: Number of obs = 1029 Variable Mean Std.
卒業論文
Y = ax 1 b1 X 2 b2...x k bk e u InY = Ina + b 1 InX 1 + b 2 InX 2 +...+ b k InX k + u X 1 Y b = ab 1 X 1 1 b 1 X 2 2...X bk k e u = b 1 (ax b1 1 X b2 2...X bk k e u ) / X 1 = b 1 Y / X 1 X 1 X 1 q YX1
Stata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(
mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual
AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = i=1 i=1 n λ x i e λ i=1 x i! = λ n i=1 x i e nλ n i=1 x
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,
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Analysis of Variance 2 two sample t test analysis of variance (ANOVA) CO 3 3 1 EFV1 µ 1 µ 2 µ 3 H 0 H 0 : µ 1 = µ 2 = µ 3 H A : Group 1 Group 2.. Group k population mean µ 1 µ µ κ SD σ 1 σ σ κ sample mean
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
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第11回：線形回帰モデルのOLS推定
11 OLS 2018 7 13 1 / 45 1. 2. 3. 2 / 45 n 2 ((y 1, x 1 ), (y 2, x 2 ),, (y n, x n )) linear regression model y i = β 0 + β 1 x i + u i, E(u i x i ) = 0, E(u i u j x i ) = 0 (i j), V(u i x i ) = σ 2, i
Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim
TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls
Hsiao (2003, 6 ) Maddala, Li, Trost and Joutz (1997) Hsiao and Pesaran (2004) 4.2 y it = γy it 1 + x itβ + ε it i = 1, 2,..., N t = 1, 2,...T (
2004 1 4 4.1 Balestra and Nerlove (1966) 1960 1980 (GMM) Arellano and Bond (1991) Arellano (2003) N T N T Smith and Fuerter (2004) 1 (the random coefficient model) 1 1995 2001 Singer and Willett (2003
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BR001 Stata 11 Stata Stata11 whitepaper mwp 3 mwp-027 22 mwp-028 / 40 mwp-001 logistic/logit 50 mwp-039 logistic/logit postestimation 60 mwp-040 margins 74 mwp-029 regress 90 mwp-037 regress postestimation
Stata User Group Meeting in Kyoto / ( / ) Stata User Group Meeting in Kyoto / 21
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ECCS. ECCS,. ( 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file e
1 1 2015 4 6 1. ECCS. ECCS,. (https://ras.ecc.u-tokyo.ac.jp/guacamole/) 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file editor, Do View Do-file Editor Execute(do). 3. Mac System
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BR003 Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 sampsi 47 mwp-044 sdtest 54 mwp-043 signrank/signtest
Microsoft Word - 計量研修テキスト_第5版）.doc
Q9-1 テキスト P166 2)VAR の推定 注 ) 各変数について ADF 検定を行った結果 和文の次数はすべて 1 である 作業手順 4 情報量基準 (AIC) によるラグ次数の選択 VAR Lag Order Selection Criteria Endogenous variables: D(IG9S) D(IP9S) D(CP9S) Exogenous variables: C Date:
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1 2 . sum Variable Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- var1 13.4923077.3545926.05 1.1 3 3 3 0.71 3 x 3 C 3 = 0.3579 2 1 0.71 2 x 0.29 x 3 C 2 = 0.4386
2 Tobin (1958) 2 limited dependent variables: LDV 2 corner solution 2 truncated censored x top coding censor from above censor from below 2 Heck
10 2 1 2007 4 6 25-44 57% 2017 71% 2 Heckit 6 1 2 Tobin (1958) 2 limited dependent variables: LDV 2 corner solution 2 truncated 50 50 censored x top coding censor from above censor from below 2 Heckit
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1 2 λ 3 λ λ. correlate father mother first second (obs=20) father mother first second ---------+------------------------------------ father 1.0000 mother 0.2254 1.0000 first 0.7919 0.5841 1.0000 second
1 Stata SEM LightStone 1 5 SEM Stata Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. Introduc
1 Stata SEM LightStone 1 5 SEM Stata Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. Introduction to confirmatory factor analysis 9 Stata14 2 1
エクセルに出力します 推定結果の表は r(table) という行列で保存されますので matlist r(table) コマンドで 得られたの一覧を表示させます. use clear. regress
Stata+α putexcel を使って推定結果をエクセルに出力する putexcel には様々な機能がありますが 今回は Stata の回帰分析の推定結果をエクセルに 出力します Stata での回帰分析の結果 sizplace Coef. age sex _cons.0067795 -.0872963 4.976162 Std. Err. t.0015178.0523214.1103018 4.47-1.67
Stata 11 whitepaper mwp 4 mwp mwp-028 / 41 mwp mwp mwp-079 functions 72 mwp-076 insheet 89 mwp-030 recode 94 mwp-033 reshape wide
PS001 Stata 11 whitepaper mwp 4 mwp-027 23 mwp-028 / 41 mwp-001 51 mwp-078 62 mwp-079 functions 72 mwp-076 insheet 89 mwp-030 recode 94 mwp-033 reshape wide/long 100 mwp-036 ivregress 110 mwp-082 logistic/logit
s = 1.15 (s = 1.07), R = 0.786, R = 0.679, DW =.03 5 Y = 0.3 (0.095) (.708) X, R = 0.786, R = 0.679, s = 1.07, DW =.03, t û Y = 0.3 (3.163) + 0
7 DW 7.1 DW u 1, u,, u (DW ) u u 1 = u 1, u,, u + + + - - - - + + - - - + + u 1, u,, u + - + - + - + - + u 1, u,, u u 1, u,, u u +1 = u 1, u,, u Y = α + βx + u, u = ρu 1 + ɛ, H 0 : ρ = 0, H 1 : ρ 0 ɛ 1,
28
y i = Z i δ i +ε i ε i δ X y i = X Z i δ i + X ε i [ ] 1 δ ˆ i = Z i X( X X) 1 X Z i [ ] 1 σ ˆ 2 Z i X( X X) 1 X Z i Z i X( X X) 1 X y i σ ˆ 2 ˆ σ 2 = [ ] y i Z ˆ [ i δ i ] 1 y N p i Z i δ ˆ i i RSTAT
第13回：交差項を含む回帰・弾力性の推定
13 2018 7 27 1 / 31 1. 2. 2 / 31 y i = β 0 + β X x i + β Z z i + β XZ x i z i + u i, E(u i x i, z i ) = 0, E(u i u j x i, z i ) = 0 (i j), V(u i x i, z i ) = σ 2, i = 1, 2,, n x i z i 1 3 / 31 y i = β
5 : 1 1
5 : 1 1 2 2 1 y = β 0 + β 1 x + u x u Cov(x, u) 0 β 0 β 1 x x u z Cov(z, u) = 0 Cov(z, x) 0 z x (1) z u (2)z x (3)z x Cov(z, u) = 0 Cov(z, x) 0 1 = 0 x = π 0 + π 1 z + v 1 Bowden and Turkington (1984)
10
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
4.9 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
1 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
Microsoft Word - 計量研修テキスト_第5版）.doc
Q10-2 テキスト P191 1. 記述統計量 ( 変数 :YY95) 表示変数として 平均 中央値 最大値 最小値 標準偏差 観測値 を選択 A. 都道府県別 Descriptive Statistics for YY95 Categorized by values of PREFNUM Date: 05/11/06 Time: 14:36 Sample: 1990 2002 Included
9 1 (1) (2) (3) (4) (5) (1)-(5) (i) (i + 1) 4 (1) (2) (3) (4) (5) (1)-(2) (1)-(5) (5) 1
9 1 (1) (2) (3) (4) (5) (1)-(5) (i) (i + 1) 4 (1) (2) (3) (4) (5) (1)-(2) (1)-(5) (5) 1 2 2 y i = 1, 2, 3,...J (1 < 2 < 3
: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
4 OLS 4 OLS 4.1 nurseries dual c dual i = c + βnurseries i + ε i (1) 1. OLS Workfile Quick - Estimate Equation OK Equation specification dual c nurser
1 EViews 2 2007/5/17 2007/5/21 4 OLS 2 4.1.............................................. 2 4.2................................................ 9 4.3.............................................. 11 4.4
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