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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Regression discontinuity with interrupted time series |

Date |
Thu, 7 Mar 2013 17:09:51 -0500 |

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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**re: Re: st: Regression discontinuity with interrupted time series***From:*"Ariel Linden. DrPH" <ariel.linden@gmail.com>

**Re: st: Regression discontinuity with interrupted time series***From:*Joshua Mitts <joshua.mitts@yale.edu>

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