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# Re: st: Regression discontinuity with interrupted time series

 From Joshua Mitts To statalist@hsphsun2.harvard.edu Subject Re: st: Regression discontinuity with interrupted time series Date Thu, 7 Mar 2013 19:08:52 -0500

```Austin,

This is very useful, thank you again.  Very much appreciate it.

Josh

On Thu, Mar 7, 2013 at 5:09 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Joshua Mitts <joshua.mitts@yale.edu>:
> I suggest you design a simulation that matches your data and your
> hypothesized effects, then see what seems to work well. Below is a
> quick foray along lines that I gather match your setting
> approximately, where OLS seems to win on MSE grounds but over-rejects
> a true null badly, and the higher MSE of my proposed long differences
> seems outweighed by a good pattern of mean estimated coefs and
> rejection rates.  But YMMV.
>
> clear all
> ssc inst rd, replace
> prog rdits, rclass
> syntax [, n(int 1000) c(real .5) f(real .5)]
> matrix C = (1, .5 \ .5, 1)
> drawnorm x u, corr(C) n(`n') clear
> drawnorm e z
> g int i=_n
> expand 20
> * serial correlation in e and x governed by `c'
> bys i:replace e=e[_n-1]*`c'+rnormal()*(1-`c') if _n>1
> by i:replace x=x[_n-1]*`c'+1+rnormal()*(1-`c') if _n>1
> replace x=exp(x)
> by i:g byte t=_n
> g A=(z>0)
> * A increases p(T) but T and x linked
> g T=(t>6)*(uniform()<(A*(`f'))+normal(ln(x))/2)
> * y increases linearly in t, x
> g y=t/2+x/10+z/10-z^2/10+T/2+T*(t-6)/4+e
> * but we don't observe x, u
> g Tt=T*t
> g At=A*t
> reg y T t Tt, cl(i)
> return scalar tols=_b[t]
> return scalar setols=_se[t]
> return scalar tTols=_b[Tt]
> return scalar setTols=_se[Tt]
> ivreg y t (T Tt=A At), cl(i)
> return scalar tiv=_b[t]
> return scalar setiv=_se[t]
> return scalar tTiv=_b[Tt]
> return scalar setTiv=_se[Tt]
> g k=max(0,1.5-abs(z))
> ivreg y t (T Tt=A At) [aw=k], cl(i)
> return scalar trd=_b[t]
> return scalar setrd=_se[t]
> return scalar tTrd=_b[Tt]
> return scalar setTrd=_se[Tt]
> tsset i t
> forv i=1/12 {
>  g dy`i'=y-L`i'.y if t==6+`i'
>  rd dy`i' T z, mbw(100)
>  return scalar t`i'=_b[lwald]
>  return scalar set`i'=_se[lwald]
>  }
> eret clear
> end
>
> simul, r(1000):rdits
> tw kdensity tols||kdensity tiv||kdensity trd, name(main)
> tw kdensity tTols||kdensity tTiv||kdensity tTrd, name(interact)
> foreach v in ols iv rd {
>  g mse_t`v'=(t`v'-.5)^2
>  g mse_tT`v'=(tT`v'-.25)^2
>  }
> forv v=1/12 {
>  g mse_t`v'=(t`v'-.5-.25*`v')^2
>  }
> foreach v in ols iv rd {
>  g rej_t`v'=abs((t`v'-.5)/set`v')>abs(invnormal(.05))
>  g rej_tT`v'=abs((tT`v'-.25)/setT`v')>abs(invnormal(.05))
>  }
> forv v=1/12 {
>  g rej_t`v'=abs((t`v'-.5-.25*`v')/set`v')>abs(invnormal(.05))
>  }
> su rej*, sep(6)
> su mse*, sep(6)
> g i=_n
> reshape long t, i(i) j(time)
> egen mt=mean(t), by(time)
> sc t mt time, msize(tiny)||function .5+x/4, ra(0 10)
>
>
> On Thu, Mar 7, 2013 at 4:00 PM, Joshua Mitts <joshua.mitts@yale.edu> wrote:
>> Hi all,
>>
>> Thank you all so much for the responses.  Austin, your comments were
>> very helpful and I greatly appreciate it.
> *
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*
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```