COLÓQUIO DE MATEMÁTICA

Transcrição

1 COLÓQUIO DE MATEMÁTICA Orientações para a escolha de Software Econométrico STATA ISABEL PROENÇA

2 Plano da Apresentação Breve introdução ao STATA Exemplo 1: Regressão linear com VIs Exemplo 2: Modelação de variáveis ordinais com dados secionais Exemplo 3: Modelação de modelos lineares dinâmicos para dados de painel Exemplo 4: Ficheiro de comandos

3 Introdução ao Stata Software de referência para microconometria e dados de painel Tratamento de séries temporais Licenças individuais (dispendioso) Stata/MP: (dual-core multicore/multiprocessor) Stata/SE: grandes bases de dados Stata/IC: bases de dados com tamanho moderado Small Stata

4 Fácil de usar Introdução ao Stata interface point-and-click comandos com sintaxe intuitiva ajuda on-line Completamente programável Editor de gráficos flexível

5 Introdução ao Stata Leitura de dados: STAT/TRANSFER Ficheiro para guardar resultados Editor de dados Transformação de variáveis Análise exploratória de dados

6 Regressão Linear com VIs log( wage) = β + β educ + β exper u Variável endógena: educ Variáveis instrumentais: meduc e feduc

7 Ordered Probit y y y y = 0 se não recorre à universidade = 1 se usa recorre pouco à universidade = 2 se recorre moderadamente à universidade = 3 se recorre muito à universidade Py ( = 0 x) =Φ( α xβ) 1 Py ( = j x) =Φ( α xβ) Φ( α xβ) j= 1, 2 j+ 1 Py ( = 3 x) = 1 Φ( α xβ) 3 j

8 Modelos lineares dinâmicos com dados de painel Blundell e Bond log( y ) = β + αlog( y ) + β log( k ) + it 0 it 1 1 it β log( k ) + β log( n ) + β log( n ) + λ + η + u 2 it 1 3 it 4 it 1 t i it Modelo transformado log( y ) = α log( y ) + β log( k ) + it it 1 1 it β log( k ) + β log( n ) + β log( n ) + 2 it 1 3 it 4 it 1 λ + u t it

9 ----- name: <unnamed> log: C:\Users\isabelp\Documents\Isabel\Apresentações\colóquio matemática março 2013\coloquio.log log type: text. use data_1.dta, clear. describe Contains data from data_1.dta obs: 72,922 vars: 13 1 Mar :17 size: 9,625, storage display value variable name type format label variable label year double %10.0g year year diary kept month double %10.0g month Month diary kept day double %10.0g day Day of week diary kept mar_status str40 %40s marital status hhldsize double %10.0g hhldsize # people in household nchild double %10.0g nchild # child aged<18 in hhold agekid2 double %10.0g agekid2 Actual age youngest child in hhold incorig double %10.0g incorig Original household income sex double %10.0g sex Sex age double %10.0g age Age educa double %10.0g educa Education-original study codes twork float %9.0g total working hours timevar double %10.0g number of activities in a day ** Cria a variável numérica x a partir da variável string mar_status. encode mar_status, generate(x) ** estatísticas descritivas. summarize Variable Obs Mean Std. Dev. Min Max year month day mar_status 0 hhldsize nchild agekid incorig sex age educa twork timevar x

10 ** Gera Dummies ed_1 a ed_6 para cada categoria de educa. quietly tabulate educa, generate(ed_). summarize ed_1-ed_6 Variable Obs Mean Std. Dev. Min Max ed_ ed_ ed_ ed_ ed_ ed_ histogram timevar, discrete (start=2, width=1) Density number of activities in a day. tabstat timevar, by(sex) Summary for variables: timevar by categories of: sex (Sex) sex mean Man Woman Total use ordered.dta, clear

11 . describe Contains data from ordered.dta obs: 967 vars: 7 7 Mar :11 size: 34, storage display value variable name type format label variable label Universities double %10.0g level of recurrence to universities lvnm_euros float %9.0g log level revenue innovsuc float %9.0g index of innovation success innovint float %9.0g innovation intensity (proportion) KIBS double %10.0g KIBS Knowledge Intensive Services leaders float %9.0g launched radical innovations trademark float %9.0g registered trademarks tabulate Universities level of recurrence to universitie s Freq. Percent Cum Total **Ordered Probit com indicação de que KIBS, leaders e trademark são dummies. oprobit Universities lvnm_euros innovsuc innovint i.kibs i.leaders i.trademark Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Ordered probit regression Number of obs = 929 LR chi2(6) = Prob > chi2 = Log likelihood = Pseudo R2 = Universities Coef. Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark /cut /cut /cut

12 .. margins, predict(outcome(0)) dydx( lvnm_euros innovsuc innovint KIBS leaders trademark) Average marginal effects Number of obs = 929 Model VCE : OIM Expression : Pr(Universities==0), predict(outcome(0)) dy/dx w.r.t. : lvnm_euros innovsuc innovint 1.KIBS 1.leaders 1.trademark Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark Note: dy/dx for factor levels is the discrete change from the base level. margins, predict(outcome(0)) dydx( lvnm_euros innovsuc innovint KIBS leaders trademark) Average marginal effects Number of obs = 929 Model VCE : OIM Expression : Pr(Universities==0), predict(outcome(0)) dy/dx w.r.t. : lvnm_euros innovsuc innovint 1.KIBS 1.leaders 1.trademark Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark Note: dy/dx for factor levels is the discrete change from the base level. Average marginal effects Number of obs = 929 Model VCE : OIM Expression : Pr(Universities==1), predict(outcome(1)) dy/dx w.r.t. : lvnm_euros innovsuc innovint 1.KIBS 1.leaders 1.trademark Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark Note: dy/dx for factor levels is the discrete change from the base level.

13 Average marginal effects Number of obs = 929 Model VCE : OIM Expression : Pr(Universities==2), predict(outcome(2)) dy/dx w.r.t. : lvnm_euros innovsuc innovint 1.KIBS 1.leaders 1.trademark Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark Note: dy/dx for factor levels is the discrete change from the base level. Average marginal effects Number of obs = 929 Model VCE : OIM Expression : Pr(Universities==3), predict(outcome(3)) dy/dx w.r.t. : lvnm_euros innovsuc innovint 1.KIBS 1.leaders 1.trademark Delta-method dy/dx Std. Err. z P> z [95% Conf. Interval] lvnm_euros innovsuc innovint KIBS leaders trademark Note: dy/dx for factor levels is the discrete change from the base level.. clear. use blundbondbalanc.dta", clear. xtset id year panel variable: id (strongly balanced) time variable: year, 1982 to 1989 delta: 1 year. xtdescribe id: 886, 1030,..., n = 492 year: 1982, 1983,..., 1989 T = 8 Delta(year) = 1 year Span(year) = 8 periods (id*year uniquely identifies each observation) Distribution of T_i: min 5% 25% 50% 75% 95% max Freq. Percent Cum. Pattern XXXXXXXX

14 ** Estimador de efeitos fixos. xtreg ly ln lk D84-D89, fe vce(cluster id) Fixed-effects (within) regression Number of obs = 3936 Group variable: id Number of groups = 492 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(8,491) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 492 clusters in id) Robust ly Coef. Std. Err. t P> t [95% Conf. Interval] ln lk D D D D D D _cons sigma_u sigma_e rho (fraction of variance due to u_i) ** Exemplo para estimador System GMM de Blundell e Bond de modelos lineares ** dinâmicos com o procedimento xtabond2 feito por Roodman (e que foi ** necessário instalar previamente). use xtabond2 n l.n l(0/1).(w k) yr1980-yr1984, gmm(l.n w k) iv(yr1980-yr1984) Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Dynamic panel-data estimation, one-step system GMM Group variable: id Number of obs = 891 Time variable : year Number of groups = 140 Number of instruments = 127 Obs per group: min = 6 Wald chi2(10) = avg = 6.36 Prob > chi2 = max = 8 n Coef. Std. Err. z P> z [95% Conf. Interval] n L w L k

15 L yr yr yr yr yr _cons Instruments for first differences equation Standard D.(yr1980 yr1981 yr1982 yr1983 yr1984) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/.).(L.n w k) Instruments for levels equation Standard _cons yr1980 yr1981 yr1982 yr1983 yr1984 GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.n w k) Arellano-Bond test for AR(1) in first differences: z = Pr > z = Arellano-Bond test for AR(2) in first differences: z = Pr > z = Sargan test of overid. restrictions: chi2(116) = Prob > chi2 = (Not robust, but not weakened by many instruments.) Difference-in-Sargan tests of exogeneity of instrument subsets: GMM instruments for levels Sargan test excluding group: chi2(93) = Prob > chi2 = Difference (null H = exogenous): chi2(23) = Prob > chi2 = iv(yr1980 yr1981 yr1982 yr1983 yr1984) Sargan test excluding group: chi2(111) = Prob > chi2 = Difference (null H = exogenous): chi2(5) = 7.31 Prob > chi2 = exit, clear