Title



     ivmw -- Moving window prefix command



Syntax



        ivmw , window(#) par(string) [saving(filename [, replace]) rolling_options]: iv_cmd



    options                         Description
    ----------------------------------------------------------------------------------------------
      par(string)                   Parameter from the iv_cmd (ivreg2, ivmsmm, ivlsmm, ivtsps,
                                      ivtsri) to collect
      window(#)                     number of consecutive data points in each sample
      saving(filename [, replace])  Save the moving window output to a dataset.  Specify replace
                                      to overwrite the dataset if it already exists.
      rolling_options               rolling##options



Description



    ivmw implements the moving window (a.k.a. sliding / rolling window) approach to the estimator
    specified after the prefix, as per Burgess et al., 2014.  It is implemented as a call to 
    rolling.



Options



    Please see rolling##options



Examples



    Simulate data with different outcome-exposure relationships; y# outcome, x exposure, g
    instrumental variable (genotype).



        . use https://raw.github.com/remlapmot/ivonesamplemr/main/data/ivmwdata, clear



    Plot outcome-exposure relationships



        . twoway line y1 x, sort(x)
        . twoway line y2 y3 y4 x, sort(x)
        . twoway line y5 x, sort(x)



    Example moving window fits.



        . ivmw, window(3950) par(x): ivreg2 y1 (x = g)
        . ivmw, window(3950) par(x): ivreg2 y2 (x = g)
        . ivmw, window(3950) par(x): ivreg2 y3 (x = g)
        . ivmw, window(3950) par(x): ivreg2 y4 (x = g)
        . ivmw, window(3950) par(x): ivreg2 y5 (x = g)



    Change window size (e.g. for outcome y2).



        . ivmw, window(3750) par(x): ivreg2 y2 (x = g)
        . ivmw, window(3000) par(x): ivreg2 y2 (x = g)
        . ivmw, window(2000) par(x): ivreg2 y2 (x = g)
        . ivmw, window(1000) par(x): ivreg2 y2 (x = g)



    Save the moving window dataset.



        . ivmw, window(3950) par(x) saving(ivmw): ivreg2 y2 (x = g)



    Use different iv commands.



        . ivmw, window(3950) par(x): ivregress 2sls y (x = z1 z2 z3)
        . ivmw, window(3950) par(x): ivregress liml y (x = z1 z2 z3)
        . ivmw, window(3950) par(x): ivregress gmm y (x = z1 z2 z3)
        . ivmw, window(3950) par(x): ivlsmm y (x = z1 z2 z3)
        . ivmw, window(3950) par(x): ivmsmm y (x = z1 z2 z3)
        . ivmw, window(3950) par(b1): ivtsps y (x = z1 z2 z3)
        . ivmw, window(3950) par(b1): ivtsps y (x = z1 z2 z3), link(logadd)
        . ivmw, window(3950) par(b1): ivtsps y (x = z1 z2 z3), link(logmult)
        . ivmw, window(3950) par(b1): ivtsps y (x = z1 z2 z3), link(logit)
        . ivmw, window(3950) par(b1): ivtsri y (x = z1 z2 z3)
        . ivmw, window(3950) par(b1): ivtsri y (x = z1 z2 z3), link(logadd)
        . ivmw, window(3950) par(b1): ivtsri y (x = z1 z2 z3), link(logmult)
        . ivmw, window(3950) par(b1): ivtsri y (x = z1 z2 z3), link(logit)



Stored results



    Please see rolling##results



References



    Burgess S, Davies N, Thompson SG, EPIC-InterAct Consortium.  Instrumental variable analysis
        with a nonlinear exposure-outcome relationship.  Epidemiology, 2014, 25, 6, 877-885.  DOI



    StataCorp. Stata Base Reference Manual. Release 16.  rolling - Rolling-window and recursive
        estimation. 2016.  URL



Author



INCLUDE help ivonesamplemr-author