STATA 蒙特卡洛模拟程序包

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使用方式+案例+参考来源

      将这两个文件放到stata的ado文件夹下的base文件夹中就可以了。然后在stata命令窗口中输入help xtarsim，就能显示该命令的使用方法。



1.使用方式

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1. ---------------------------------------------------------------------------------------------
   
2. help for xtarsim
   
3. ---------------------------------------------------------------------------------------------
   
4. Simulate panel dataset
   
5.         xtarsim newdepvar newindepvar newindeffect newtimeffect , nid(#) time(#)
   
6.                 gamma(real) beta(real) rho(real) snratio(real) [sigma(real)
   
7.                 oneway(effect_type load) twoway(effect_type load) unbd(N_1 T_1) seed(#)]
   
8. 
   
9.     xtarsim creates panel datasets for use in Monte Carlo experiments as pseudo-random
   
10.     realizations from (possibly) dynamic twoway linear panel data models.
    
11. 
    
12.     Description
    
13.     xtarsim creates a dataset from the following general panel data model
    
14.     y[i,t] =  y[i,t-1]gamma + x[i,t]beta + u[i] + u[t] + e[i,t]
    
15.     x[i,t] =  x[i,t-1]rho + v[i,t]      i={1,...,N};     t={1,...,T},
    
16.     where
    
17.     gamma, beta and rho are real numbers chosen by the user.
    
18.     e[i,t] are iid Normal(0,sigma^2), with sigma chosen by the user.
    
19.     v[i,t] are iid Normal(0,sigma_v^2), with sigma_v being uniquely determined once
    
20.     choosing the model parameters and the signal to noise ratio of the y[i,t] regression.
    
21.     Attention should be paid to supply parameter values that ensure a finite positive
    
22.     variance for v[i,t]. When this does not happen an error message is issued by xtarsim.
    
23.     e[i,t] and v[i,t] are not correlated, so that x[i,t] is a strictly exogenous regressor
    
24.     in the first equation of the model.
    
25.     u[i] and u[t] are, respectively, the individual and time effects, and may or may not be
    
26.     correlated with x[i,t].
    
27.     If correlated, individual effects are determined as u[i]=load_1*(1-gamma)*(1+x[i]-x),
    
28.     where x[i] and x, respectively, are the group mean and the overall mean of x[i,t], and
    
29.     load_1 is a load factor chosen by the user. Correlated time effects, instead, are
    
30.     determined as contrasts to the first period, u[t]=load_2*(1-gamma)*(x[t]-x[1]), where
    
31.     again load_2 is a load factor chosen by the user. Such normalisation is convenient in
    
32.     that the constant term in xtreg, in its one-way fixed effect version as well as two-way
    
33.     fixed effect version excluding the first time indicator, can be interpreted as an
    
34.     estimate for load_1*(1-gamma) (see the example file static2way_bias.do available for
    
35.     download). If not correlated, both effects are taken to be iid
    
36.     Normal(0,load^2*(1-gamma)^2) with a specific load factor for each effect.
    
37.     Following Kiviet (1995) start-up values y[i,0] and x[i,0] are obtained according to the
    
38.     model using the McLeod and Hipel (1978) procedure. This avoids wasting random numbers
    
39.     in generating start-up values and also small-sample non-stationarity problems. This
    
40.     procedure has been also applied by Bun and Kiviet (2003), Bruno (2005a) and (2005b).

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2.案例

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1. Examples
   
2.     (Create a panel from a static one-way random effect Data Generation Process (DGP))
   
3.         . xtarsim y x eta, n(200) t(10) g(0) b(.8) r(.2) sn(9) seed(1234)
   
4.         . describe
   
5.         . xtdes
   
6.     (Create a panel from a dynamic one-way fixed effect DGP)
   
7.         . xtarsim y x eta, n(200) t(10) g(.2) b(.8) r(.2) one(corr 1) sn(9) seed(1234)
   
8.         . xtdes
   
9.     (Demonstrate, on this dataset, the expected good perfomance of the basic Arellano-Bond
   
10.     estimator in terms of estimation error and specification tests)
    
11.         . xtabond y x,noco
    
12.     (Create a panel from a dynamic two-way fixed effect DGP)
    
13.         . xtarsim y x eta theta, n(200) t(10) g(.2) b(.8) r(.2) two(corr 5) sn(9)
    
14.             seed(1234)
    
15.         . describe
    
16.         . xtdes
    
17.     (Demonstrate, on this dataset, the expected poor perfomance of the basic Arellano-Bond
    
18.     estimator in terms of estimation error and specification tests)
    
19. 
    
20.         . xtabond y x,noco
    
21.     (Demonstrate the expected better perfomance of the two-way Arellano-Bond estimator)
    
22. 
    
23.         . tab tvar,gen(time)
    
24.         . xtabond y x time*,noco
    
25.     (Make the foregoing dataset unbalanced: the last 5 time observations are missing for
    
26.     the first 50 individuals in the sample)
    
27.         . xtarsim y x eta theta, n(200) t(10) g(.2) b(.8) r(.2) two(corr 5) sn(9) unbd(50
    
28.             5) seed(1234)
    
29.         . xtdes
    
30.     For examples of xtarsim in Monte Carlo experiments download the do files dyn_bias.do
    
31.     and static2way_bias.do. The former, upon setting up a dynamic one-way random effect
    
32.     DGP, estimates the unconditional small-sample biases of the dynamic one-way fixed
    
33.     effect and random effect estimators by 1000 Monte Carlo simulations. The latter sets up
    
34.     a static two-way fixed effect DGP and estimates the unconditional small-sample biases
    
35.     of the one-way and two-way fixed effect estimators using 1000 Monte Carlo simulations.

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3、参考

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