TS002
|
|
|
- きゅうた かいて
- 6 years ago
- Views:
Transcription
1 TS002
2 Stata 12 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 26 mwp-005 svar VAR 33 mwp-007 vec intro VEC 51 mwp-008 vec VEC 80 mwp-063 VAR vargranger Granger 93 mwp-062 varlmar 101 mwp-059 varnorm 107 mwp-060 varsoc 113 mwp-057 varstable 118 mwp-058 varwle 124 mwp-061 VEC veclmar 131 mwp-066 vecnorm 135 mwp-067 vecrank 139 mwp-064 vecstable 144 mwp-065
3 whitepaper mwp irf IRF 149 mwp-006 fcast compute 160 mwp-068 fcast graph 166 mwp-069 StataCorp c 2011 Math c 2011 StataCorp LP Math web: [email protected]
4 mwp-084 Stata var, vec / whitepaper mwp-004, mwp-063, whitepaper arima, arch mwp VAR VAR 4. VEC 1. VAR (1) {y t } 1 y t = (y 1t, y 2t,..., y nt ) {y t } = {..., y 1, y 0, y 1,...} (1) {y t } (i) E(y t ) µ µ t (ii) V (y t ) = E[(y t µ)(y t µ) ] t (iii) Cov(y t, y t s ) = E[(y t µ)(y t s µ) ] t s(s > 0) c Copyright Math c Copyright StataCorp LP (used with permission) 4
5 (2) VAR (VAR: vector autoregression model) AR(p) p y t = A 0 + A i y t i + u t (2) i=1 A 0 n 1 A i (i = 1, 2,..., p) n n u t n 1 u t E(u t ) = 0 (3a) V (u t ) = E(u t u t) = Σ (3b) E(u t u t s ) = 0, for s > 0 (3c) Σ (2) p VAR(p) VAR(p) (2) ( p ) I n A i z i = 0 (4) i=1 1 (3) VAR (2) VAR (SUR: seemingly unrelated regression) SUR VAR OLS (BLUE: best linear unbiased estimator) VAR var *1 [TS] var (mwp- 004 ) var p Stata preestimation varsoc [TS] varsoc (mwp-057 ) *1 VAR varbasic [TS] varbasic (mwp-005 ) 5
6 2. VAR VEC (innovation accounting) (IRF: impulse response function) (FEVD: forecast-error variance decomposition) 3. VAR 4. VEC (1) x t y t 1 I(1) β 1 x t + β 2 y t I(0) x t y t (cointegrated) β = (β 1, β 2 ) (cointegrating vector) (VECM: vector error correction model) (2) n VAR(p) y t = A 0 + p A i y t i + u t (22) i=1 {y t } y 1t, y 2t,..., y nt I(1) 1 (22) p 1 y t = A 0 + Πy t 1 + Γ i y t i + u t (23) Π = p i=1 A i I n, Γ i = p j=i+1 A j (23) Πy t 1 Πy t 1 i=1 Case 1: Π = 0 p i=1 A i = I n (23) VAR { y t } VAR {y t } 6
7 Case 2: Πy t 1 y 1t, y 2t,..., y nt 1 Π αβ (23) p 1 y t = A 0 + αβ y t 1 + Γ i y t i + u t (24) α, β n r r Π β r β y t 1 r (error correction term) α (adjustment coefficient vector) (24) VECM (24) (Granger s representation theorem) i=1 (3) (4) VEC VEC 7
8 mwp-004 var - Stata (VAR: vector autoregressive) whitepaper var VAR IRF FEVD varbasic mwp VAR 2. var 2.1 VAR var 1. VAR VAR [TS] var intro p543 3 VAR(2) x t y 1t y 2t = v a (1) 1 11 a (1) 12 a (1) 13 v 2 + a (1) 21 a (1) 22 a (1) y a (2) 1,t a (2) y 3t v 3 a (1) 31 a (1) 32 a (1) 33 u 1t u 2t W.N.(Σ) u 3t [TS] var intro y 2,t 1 y 3,t 1 a (1) 11 a (1) 12 a (1) a (2) 12 a (2) a (2) 22 a (2) 23 a (2) 31 a (2) 32 a (2) 33 A 1 = a (1) 21 a (1) 22 a (1) 23 A 2 = a (2) 21 a (2) 22 a (2) 23 a (1) 31 a (1) 32 a (1) 33 a (2) 31 a (2) 32 a (2) 33 a (2) 11 a (2) 12 a (2) 13 y 1,t 2 y 2,t 2 + u 1t u 2t y 3,t 2 u 3t (M1) c Copyright Math c Copyright StataCorp LP (used with permission) 8
9 u t (W.N.) Σ (M1) σ 11 σ 12 σ 13 Σ = σ 21 σ 22 σ 23 σ 12 = σ 21, σ 13 = σ 31, σ 23 = σ 32 σ 31 σ 32 σ 33 3 VAR(2) a (1) ij 9 a (2) ij 9 σ ij 6 24 (M1) VAR (SUR: seemingly unrelated regression) SUR VAR OLS (BLUE: best linear unbiased estimator) u t OLS 2. var 2.1 VAR VAR Example lutkepohl2.dta 3 VAR. use * 1 (Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1) (M1) y 1, y 2, y 3 3 dln inv ln(inv) inv dln inc ln(inc) inc dln consump ln(consump) consump inv *1 File Example Datasets Stata 12 manual datasets Time-Series Reference Manual [TS] var 9
10 . list qtr inv ln inv dln inv in 1/8, separator(4) * 2 qtr inv ln_inv dln_inv q q q q q q q q q1 1982q4 1978q4 var VAR(2) varsoc [TS] varsoc (mwp-057 ) Statistics Multivariate time series Vector autoregression (VAR) Model : Dependent variables: dln inv dln inc dln consump Lags: Include lags 1 to: 2 1 var Model *2 Data Describe data List data 10
11 by/if/in : If: qtr <= tq(1978q4) 2 var by/if/in. var dln_inv dln_inc dln_consump if qtr <= tq(1978q4), lags(1/2) Vector autoregression Sample: 1960q4 1978q4 No. of obs = 73 Log likelihood = AIC = FPE = 2.18e 11 HQIC = Det(Sigma_ml) = 1.23e 11 SBIC = Equation Parms RMSE R sq chi2 P>chi2 dln_inv dln_inc dln_consump
12 Coef. Std. Err. z P> z [95% Conf. Interval] dln_inv dln_inv L L dln_inc L L dln_consump L L _cons dln_inc dln_inv L L dln_inc L L dln_consump L L _cons dln_consump dln_inv L L dln_inc L L dln_consump L L _cons
13 (M1) a (1) 11 a (1) 12 a (1) a (1) 21 a (1) 22 a (1) 23 = a (1) 31 a (1) 32 a (1) a (2) 11 a (2) 12 a (2) 13 a (2) 21 a (2) 22 a (2) 23 a (2) 31 a (2) 32 a (2) 33 v 1 v 2 v = = p dln inv a (1) 11 a(2) 11, a(1) 12, a(2) 12, a(1) 13, a(2) 13, v 1 0 VAR 2.3 Σ e(sigma). matrix list e(sigma) * 3. matrix list e(sigma) symmetric e(sigma)[3,3] dln_inv dln_inc dln_consump dln_inv dln_inc dln_consump *3 ereturn list 13
14 3. var var postestimation # 1 varsoc varlmar varstable varnorm Granger vargranger lag-exclusion varwle 2 predict fcast compute/graph 3 IRF IRF/DM/FEVD irf 14
15 mwp-008 vec intro - Stata (VECM: vector error-correction models) whitepaper VECM whitepaper 1. Granger VECM VECM 3.4 Johansen VECM 1. Granger VECM E(y t ) = µ y t t k Cov(y t, y t k ) = E[(y t µ)(y t k µ)] = γ k (weak stationary) (covariance stationary) y t y t = y t y t 1 (unit root process) y t I(1) d 1 d d (integrated process) I(d) I(0) c Copyright Math c Copyright StataCorp LP (used with permission) 15
16 y t I(1) c y t I(0) c y t (cointegration) y t (cointegrated) c (cointegrating vector) Granger (Granger representation theorem) Granger [TS] vec intro p626 (7) K VAR(p) I(1) y t [TS] vec intro p626 (8) VECM y t r Π K K αβ K r β β y t I(0) r α (adjustment parameters) 2. vec vec Model 5 1 vec - Model 16
17 [TS] vec intro p626 (8) p627 (9) (11) µ, ρ, γ, τ 5 [TS] vec intro Trends in the Johansen VECM framework p VECM Example txhprice.dta VEC. use * 1 4 Austin, Dallas, Houston, San Antonio t austin dallas houston sa m m m m m m m m *1 File Example Datasets Stata 12 manual datasets Time-Series Reference Manual [TS] vec intro 17
18 2 4 Dallas Houston
![3.1 var vec varsoc [TS] vec intro p626 (7) (8) VECM VAR p 1 vec VAR p dallas, houston varsoc Statistics Multivariate time series VEC diagnostics](/docs-images/98/138213508/images/19-0.jpg "and tests Lag-order selection statistics (preestimation) Main : Dependent variables: dallas houston Options: Maximum lag order: 4 4 varsoc - Main")
19 3.1 var vec varsoc [TS] vec intro p626 (7) (8) VECM VAR p 1 vec VAR p dallas, houston varsoc Statistics Multivariate time series VEC diagnostics and tests Lag-order selection statistics (preestimation) Main : Dependent variables: dallas houston Options: Maximum lag order: 4 4 varsoc - Main 19
20 . varsoc dallas houston Selection order criteria Sample: 1990m5 2003m12 Number of obs = 164 lag LL LR df p FPE AIC HQIC SBIC e * e 06* * * * e e Endogenous: dallas houston Exogenous: _cons (LR: likelihood ratio) LR FPE * varsoc dallas houston austin sa. varsoc dallas houston austin sa Selection order criteria Sample: 1990m5 2003m12 Number of obs = 164 lag LL LR df p FPE AIC HQIC SBIC e e * e * * e 11* * e Endogenous: dallas houston austin sa Exogenous: _cons 20
21 3.2 VECM vec 1 vecrank Statistics Multivariate time series Cointegrating rank of a VECM Model : Dependent variables: dallas houston 5 vecrank - Model. vecrank dallas houston, trend(constant) Johansen tests for cointegration Trend: constant Number of obs = 166 Sample: 1990m3 2003m12 Lags = 2 5% maximum trace critical rank parms LL eigenvalue statistic value * Johansen * vecrank 2 21
22 . vecrank austin dallas houston sa, lag(3). vecrank austin dallas houston sa, lag(3) Johansen tests for cointegration Trend: constant Number of obs = 165 Sample: 1990m4 2003m12 Lags = 3 5% maximum trace critical rank parms LL eigenvalue statistic value * VECM /2 VEC y t = (dallas, houston) ( ) ( ) y1t v1 = + y 2t v 2 ( α1 α 2 ) (β1 β 2 ) ( ) ( ) ( ) ( ) y1,t 1 γ11 γ + 12 y1,t 1 ε1t + y 2,t 1 γ 21 γ 22 y 2,t 1 ε 2t (M1) Statistics Multivariate time series Vector error-correction model (VECM) Model : Dependent variables: dallas houston Number of cointegrating equations (rank): 1 Maximum lag to be included in underlying VAR model: 2 Trend specification: constant * 2 *2 22
23 6 vec - Model. vec dallas houston, trend(constant) Vector error correction model Sample: 1990m3 2003m12 No. of obs = 166 AIC = Log likelihood = HQIC = Det(Sigma_ml) = 2.50e 06 SBIC = Equation Parms RMSE R sq chi2 P>chi2 D_dallas D_houston
24 Coef. Std. Err. z P> z [95% Conf. Interval] D_dallas _ce1 L dallas LD houston LD _cons D_houston _ce1 L dallas LD houston LD _cons Cointegrating equations Equation Parms chi2 P>chi2 _ce Identification: beta is exactly identified Johansen normalization restriction imposed beta Coef. Std. Err. z P> z [95% Conf. Interval] _ce1 dallas houston _cons
25 vec ce cointegrating equations β 2 VECM(1) ( ) ˆβ1 = ˆβ 2 ( ) y 1t y 2t (M2) I(0) (adjustment coefficients) α ) ( ) ) ( ) ) (ˆα (ˆv (ˆγ11 ˆγ = = 12 = ˆα ˆv ˆγ 21 ˆγ 22 ( ) (M2) VECM y 3. predict ce, ce equation( ce1). twoway (line ce t), yscale(range( )) yline(0) ylabel(-0.4(0.2)0.4) 25
26 3.4 Johansen 3.5 (1) predict VECM predict Statistics Postestimation Predictions, residuals, etc. Main : New variable name: ce1 Produce: Prediction of cointegrating equation: Equation to predict: ce1 8 predict - Main. predict ce1, ce equation(_ce1) ce2. predict ce2, ce equation( ce2). predict ce2, ce equation(_ce2) 26
27 ce1, ce2. twoway (line ce1 t), yscale(range( )) yline(0) ylabel(-0.4(0.2)0.4) > title(" ce1") * 3. twoway (line ce2 t), yscale(range( )) yline(0) ylabel(-0.4(0.2)0.4) > title(" ce2") ce *3 Graphics Twoway graph (scatter, line, etc.) 27
28 (2) vecstable (3) veclmar (4) vecnorm VECM 28
29 mwp-006 irf - IRF irf var, svar, vec irf 10 3 irf set IRF irf create IRF irf graph whitepaper VAR VEC [TS] vecintro (mwp-008 ) 1. IRF 2. Cholesky ordering 3. DM 4. FEVD 1. IRF (IRF: impulse response function) 1 VAR(p) [TS] irf create p180 (1) Σ IRF IRF (orthogonalized IRF) (Cholesky decomposition) Σ [TS] irf create p c Copyright Math c Copyright StataCorp LP (used with permission) 29
30 (1) VAR IRF VAR Example lutkepohl2.dta. use * 1 (Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1) dln inv, dln inc, dln consump 3 VAR(2) mwp-004. var dln inv dln inc dln consump if qtr<=tq(1978q4), lags(1/2) dfk * 2. var dln_inv dln_inc dln_consump if qtr<=tq(1978q4), lags(1/2) dfk Vector autoregression Sample: 1960q4 1978q4 No. of obs = 73 Log likelihood = AIC = FPE = 2.18e 11 HQIC = Det(Sigma_ml) = 1.23e 11 SBIC = Equation Parms RMSE R sq chi2 P>chi2 dln_inv dln_inc dln_consump Coef. Std. Err. z P> z [95% Conf. Interval] dln_inv dln_inv L L dln_inc L L dln_consump L L _cons *1 File Example Datasets Stata 12 manual datasets Time-Series Reference Manual [TS] irf *2 Statistics Multivariate time series Vector autoregression (VAR) 30
31 dln_inc dln_inv L L dln_inc L L dln_consump L L _cons dln_consump dln_inv L L dln_inc L L dln_consump L L _cons (2) IRF IRF FEVD.irf / irf001.irf irf set 31
32 Statistics Multivariate time series Manage IRF results and files Set active IRF file irf set : Set active IRF file: irf001 Replace any existing file with an empty file: 1 irf set. irf set "irf001", replace (file irf001.irf created) (file irf001.irf now active) irf001.irf Stata (3) IRF IRF FEVD irf create order1 order1 2 Statistics Multivariate time series IRF and FEVD analysis Obtain IRFs, dynamic-multiplier functions, and FEVDs Main : Create IRF and store as name: order1 Forecast horizon: 8 32
33 2 irf create - Main. irf create order1 (file irf001.irf updated) irf001.irf order1 (4) IRF order1 OIRF 3 VAR 3 3 = 9 9 dln inc dln consump Statistics Multivariate time series IRF and FEVD analysis Graphs by impulse or response Main : Statistics to graph: Orthogonalized impulse-response functions (oirf) IRF result sets: order1 Impulse variables: dln inc Response variables: dln consump 33
34 3 irf graph - Main. irf graph oirf, irf(order1) impulse(dln_inc) response(dln_consump) 34
35 2. Cholesky ordering 3. DM 4. FEVD 35
Stata 11 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 25 mwp-005 svar VAR 31 mwp-007 vec intro VEC 47 mwp-008 vec VEC 75 mwp
TS002 Stata 11 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 25 mwp-005 svar VAR 31 mwp-007 vec intro VEC 47 mwp-008 vec VEC 75 mwp-063 VAR postestimation vargranger Granger 86
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
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
% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti One-sample test of pr
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.
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:
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 s
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
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
最小2乗法
2 2012 4 ( ) 2 2012 4 1 / 42 X Y Y = f (X ; Z) linear regression model X Y slope X 1 Y (X, Y ) 1 (X, Y ) ( ) 2 2012 4 2 / 42 1 β = β = β (4.2) = β 0 + β (4.3) ( ) 2 2012 4 3 / 42 = β 0 + β + (4.4) ( )
Microsoft Word - 計量研修テキスト_第5版）.doc
Q8-1 テキスト P131 Engle-Granger 検定 Dependent Variable: RM2 Date: 11/04/05 Time: 15:15 Sample: 1967Q1 1999Q1 Included observations: 129 RGDP 0.012792 0.000194 65.92203 0.0000 R -95.45715 11.33648-8.420349
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,
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
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. 2 3 2 Conservative Depress. 3.1 2. SEM. 1. x SEM. Depress.
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
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
Rによる計量分析：データ解析と可視化 - 第3回 Rの基礎とデータ操作・管理
R 3 R 2017 Email: [email protected] October 23, 2017 (Toyama/NIHU) R ( 3 ) October 23, 2017 1 / 34 Agenda 1 2 3 4 R 5 RStudio (Toyama/NIHU) R ( 3 ) October 23, 2017 2 / 34 10/30 (Mon.) 12/11 (Mon.)
評論・社会科学 １２３号（Ｐ）☆／１．福田
VECM 2002 1 2007 12 VECM 55 VECM 1 2 3 3-1ECM 3-2 3-3VECM 3-4 4 1 2017 9 28 2017 10 16 2 1 1 2015, 79-85 BLUEBest Linear Unbiased Estimator 1 Vector Error-Correction Model VECM II III VECM VECM 55 15-34
: (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.
こんにちは由美子です
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
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
1 I EViews View Proc Freeze
EViews 2017 9 6 1 I EViews 4 1 5 2 10 3 13 4 16 4.1 View.......................................... 17 4.2 Proc.......................................... 22 4.3 Freeze & Name....................................
.. est table TwoSLS1 TwoSLS2 GMM het,b(%9.5f) se Variable TwoSLS1 TwoSLS2 GMM_het hi_empunion totchr
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
kubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and [email protected] http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
untitled
Horioka Nakagawa and Oshima u ( c ) t+ 1 E β (1 + r ) 1 = t i+ 1 u ( c ) t 0 β c t y t uc ( t ) E () t r t c E β t ct γ ( + r ) 1 0 t+ 1 1 = t+ 1 ξ ct + β ct γ c t + 1 1+ r ) E β t + 1 t ct (1
こんにちは由美子です
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
seminar0220a.dvi
1 Hi-Stat 2 16 2 20 16:30-18:00 2 2 217 1 COE 4 COE RA E-MAIL: [email protected] 2004 2 25 S-PLUS S-PLUS S-PLUS S-code 2 [8] [8] [8] 1 2 ARFIMA(p, d, q) FI(d) φ(l)(1 L) d x t = θ(l)ε t ({ε t }
1 15 R Part : website:
1 15 R Part 4 2017 7 24 4 : website: email: http://www3.u-toyama.ac.jp/kkarato/ [email protected] 1 2 2 3 2.1............................... 3 2.2 2................................. 4 2.3................................
03.Œk’ì
HRS KG NG-HRS NG-KG AIC Fama 1965 Mandelbrot Blattberg Gonedes t t Kariya, et. al. Nagahara ARCH EngleGARCH Bollerslev EGARCH Nelson GARCH Heynen, et. al. r n r n =σ n w n logσ n =α +βlogσ n 1 + v n w
LA-VAR Toda- Yamamoto(1995) VAR (Lag Augmented vector autoregressive model LA-VAR ) 2 2 Nordhaus(1975) 3 1 (D2)
LA-VAR 1 1 1973 4 2000 4 Toda- Yamamoto(1995) VAR (Lag Augmented vector autoregressive model LA-VAR ) 2 2 Nordhaus(1975) 3 1 (D2) E-mail [email protected] 2 Toda, Hiro Y. and Yamamoto,T.(1995) 3
fiúŁÄ”s‘ê‡ÌŁª”U…−…X…N…v…„…~…A…•‡Ì ”s‘ê™´›ß…−…^†[…fiŠ‚ª›Âfl’«
2016/3/11 Realized Volatility RV 1 RV 1 Implied Volatility IV Volatility Risk Premium VRP 1 (Fama and French(1988) Campbell and Shiller(1988)) (Hodrick(1992)) (Lettau and Ludvigson (2001)) VRP (Bollerslev
<4D F736F F D20939D8C7689F090CD985F93C18EEA8D758B E646F63>
Gretl OLS omitted variable omitted variable AIC,BIC a) gretl gretl sample file Greene greene8_3 Add Define new variable l_g_percapita=log(g/pop) Pg,Y,Pnc,Puc,Ppt,Pd,Pn,Ps Add logs of selected variables
オーストラリア研究紀要 ３６号（Ｐ）☆／３．橋本
36 p.9 202010 Tourism Demand and the per capita GDP : Evidence from Australia Keiji Hashimoto Otemon Gakuin University Abstract Using Australian quarterly data1981: 2 2009: 4some time-series econometrics
R による共和分分析 1. 共和分分析を行う 1.1 パッケージ urca インスツールする 共和分分析をするために R のパッケージ urca をインスツールする パッケージとは通常の R には含まれていない 追加的な R のコマンドの集まりのようなものである R には追加的に 600 以上のパッ
R による共和分分析 1. 共和分分析を行う 1.1 パッケージ urca インスツールする 共和分分析をするために R のパッケージ urca をインスツールする パッケージとは通常の R には含まれていない 追加的な R のコマンドの集まりのようなものである R には追加的に 600 以上のパッケージが用意されており それぞれ分析の目的に応じて標準の R にパッケージを追加していくことになる インターネットに接続してあるパソコンで
自由集会時系列part2web.key
spurious correlation spurious regression xt=xt-1+n(0,σ^2) yt=yt-1+n(0,σ^2) n=20 type1error(5%)=0.4703 no trend 0 1000 2000 3000 4000 p for r xt=xt-1+n(0,σ^2) random walk random walk variable -5 0 5 variable
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
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,
T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,
![T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,](/thumbs/93/114160334.jpg "T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,")
(ver. 4:. 2005-07-27) 1 1.1 (mixed matrix) (layered mixed matrix, LM-matrix) m n A = Q T (2m) (m n) ( ) ( ) Q I m Q à = = (1) T diag [t 1,, t m ] T rank à = m rank A (2) 1.2 [ ] B rank [B C] rank B rank
読めば必ずわかる 分散分析の基礎 第２版
2 2003 12 5 ( ) ( ) 2 I 3 1 3 2 2? 6 3 11 4? 12 II 14 5 15 6 16 7 17 8 19 9 21 10 22 11 F 25 12 : 1 26 3 I 1 17 11 x 1, x 2,, x n x( ) x = 1 n n i=1 x i 12 (SD ) x 1, x 2,, x n s 2 s 2 = 1 n n (x i x)
k3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k
2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) [email protected] web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................
201711grade2.pdf
2017 11 26 1 2 28 3 90 4 5 A 1 2 3 4 Web Web 6 B 10 3 10 3 7 34 8 23 9 10 1 2 3 1 (A) 3 32.14 0.65 2.82 0.93 7.48 (B) 4 6 61.30 54.68 34.86 5.25 19.07 (C) 7 13 5.89 42.18 56.51 35.80 50.28 (D) 14 20 0.35
以下の内容について説明する 1. VAR モデル推定する 2. VAR モデルを用いて予測する 3. グレンジャーの因果性を検定する 4. インパルス応答関数を描く 1. VAR モデルを推定する ここでは VAR(p) モデル : R による時系列分析の方法 2 y t = c + Φ 1 y t
以下の内容について説明する 1. VAR モデル推定する 2. VAR モデルを用いて予測する 3. グレンジャーの因果性を検定する 4. インパルス応答関数を描く 1. VAR モデルを推定する ここでは VAR(p) モデル : R による時系列分析の方法 2 y t = c + Φ 1 y t 1 + + Φ p y t p + ε t, ε t ~ W.N(Ω), を推定することを考える (
Use R
Use R! 2008/05/23( ) Index Introduction (GLM) ( ) R. Introduction R,, PLS,,, etc. 2. Correlation coefficient (Pearson s product moment correlation) r = Sxy Sxx Syy :, Sxy, Sxx= X, Syy Y 1.96 95% R cor(x,
X X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
こんにちは由美子です
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
JFE.dvi
,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : [email protected] E-mail : [email protected] SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho
II (2011 ) ( ) α β û i R
II 3 9 9 α β 3 û i 4 R 3 5 4 4 3 6 3 6 3 6 4 6 5 3 6 F 5 7 F 6 8 GLS 8 8 heil and Goldberger Model 9 MLE 9 9 I 3 93 II 3 94 AR 4 95 5 96 6 6 8 3 3 3 3 3 i 3 33 3 Wald, LM, LR 33 3 34 4 38 5 39 6 43 7 44
( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
σ t σ t σt nikkei HP nikkei4csv H R nikkei4<-readcsv("h:=y=ynikkei4csv",header=t) (1) nikkei header=t nikkei4csv 4 4 nikkei nikkei4<-dataframe(n
R 1 R R R tseries fseries 1 tseries fseries R Japan(Tokyo) R library(tseries) library(fseries) 2 t r t t 1 Ω t 1 E[r t Ω t 1 ] ɛ t r t = E[r t Ω t 1 ] + ɛ t ɛ t 2 iid (independently, identically distributed)
一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM
.. ( ) (2) GLMM [email protected] I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!
(p.2 ( ) 1 2 ( ) Fisher, Ronald A.1932, 1971, 1973a, 1973b) treatment group controll group (error function) 2 (Legendre, Adrian
2004 1 1 1.1 Maddala(1993) Mátyás and Sevestre (1996) Hsiao(2003) Baltagi(2001) Lee(2002) Woolridge(2002a), Arellano(2003) Journal of Econometrics Econometrica Greene(2000) Maddala(2001) Johnston and Di-
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
磁性物理学 - 遷移金属化合物磁性のスピンゆらぎ理論
email: [email protected] 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
Solution Report
CGE 3 GAMS * Date: 2018/07/24, Version 1.1 1 2 2 GAMSIDE 3 2.1 GAMS................................. 3 2.2 GAMSIDE................................ 3 2.3 GAMSIDE............................. 7 3 GAMS 11
カルマンフィルターによるベータ推定（ ）
β TOPIX 1 22 β β smoothness priors (the Capital Asset Pricing Model, CAPM) CAPM 1 β β β β smoothness priors :,,. E-mail: [email protected]., 104 1 TOPIX β Z i = β i Z m + α i (1) Z i Z m α i α i β i (the
Autumn 2005 1 9 13 14 16 16 DATA _null_; SET sashelp.class END=eof; FILE 'C: MyFiles class.txt'; /* */ PUT name sex age; IF eof THEN DO; FILE LOG; /* */ PUT '*** ' _n_ ' ***'; END; DATA _null_;
講義のーと : データ解析のための統計モデリング. 第５回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
講義のーと : データ解析のための統計モデリング. 第３回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
Microsoft Word - 表紙.docx
黒住英司 [ 著 ] サピエンティア 計量経済学 訂正および練習問題解答 (206/2/2 版 ) 訂正 練習問題解答 3 .69, 3.8 4 (X i X)U i i i (X i μ x )U i ( X μx ) U i. i E [ ] (X i μ x )U i i E[(X i μ x )]E[U i ]0. i V [ ] (X i μ x )U i i 2 i j E [(X i
Stata User Group Meeting in Kyoto / ( / ) Stata User Group Meeting in Kyoto / 21
Stata User Group Meeting in Kyoto / 2017 9 16 ( / ) Stata User Group Meeting in Kyoto 2017 9 16 1 / 21 Rosenbaum and Rubin (1983) logit/probit, ATE = E [Y 1 Y 0 ] ( / ) Stata User Group Meeting in Kyoto
Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i
![,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i](/thumbs/87/95421301.jpg ",, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i")
Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,
