help margintegrate
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Title
    margintegrate -- Estimate generalized additive non-parametric functions
        of two dependent variables; output graphical representation of
        E(yvar|xvar1).

Syntax
    margintegrate yvar xvar1 xvar2 [if] [in] , bw(numlist) [options]

Description
    margintegrate estimates a non-parametric regression of yvar on xvar1 and
    xvar2, treating the expectation of yvar|xvar1 as the quantity of
    interest. It integrates out the effect of the "nuisance" variable xvar2
    by means of marginal integration, following the methods outlined in
    Linton and Neilsen (1996), Tjostheim and Auestad (1994); see also Li and
    Racine (2007, 283-289).


Required Argument
    bw(numlist) must contain two values, bw_1 and bw_2, the bandwidths for
        xvar1 and xvar2, respectively. The literature on generalized additive
        models has suggested using a multiple of the standard deviation as
        the bandwidth each independent variable of interest (i.e. .7, .9, 1.1
        times sd); this solution is not optimal in the mean squared error
        sense, but visual inspection can provide a good guide to appropriate
        levels of smoothing.

Options
    n(integer 50) contains the number of points at which the estimation will
        be executed. The maximum is 800; default is 50. The marginal
        integration estimator requires n x n regression smoothes to get
        predicted values, so n(100) estimates 10,000 regression smoothes.
        Points are chosen according to percentile values of the data sorted
        by xvar1 and xvar2.

    generate(varlist) can contain up to two variable names, the first for the
        predicted value of the estimator, the second for the pointwise
        estimate of the variance.

    ci(level) shows pointwise confidence intervals at specified level,
        assuming asymptotic normal distribution of the expected value. If
        boot is not specified, the estimator uses asymptotic variance (see Li
        and Racine (2007, p.285)) to construct confidence intervals; this
        expression can be fragile, especially if the support for either of
        the independent variables is not compact.

    boot(integer) estimates confidence intervals by bootstrapping original
        data integer number of times.

    trim(real) specifies that a total of real percent of the data will be
        trimmed for the purposes of estimation; trims real/2 percent at top
        and bottom of the data sorted by xvar1.

    nograph specifies no graphical output: predicted values will be
        calculated and stored if generate(varlist) is specified; use to
        incorporate your own graph preferences.


Examples
    Estimate a locally linear regression of y on x1 and x2 and use the
    asymptotic variance to show 95% confidence intervals. Use 100 points to
    in the distribution of x1 and x2 to derive estimates.

        . margintegrate y x1 x2, bw(4 5) n(100) ci(95)


    Estimate a locally linear regression of y on x1 and x2 without producing
    any graphical output, but using the bootstrap procedure to estimate
    pointwise variances. Use 100 points to in the distribution of x1 and x2
    to derive estimates.