# ivreg_ss.fit: Inference in an IV regression with a shift-share instrument In ShiftShareSE: Inference in Regressions with Shift-Share Structure

## Description

Basic computing engine to calculate confidence intervals and p-values in an instrumental variables regression with a shift-share instrument, using different inference methods, as specified by `method`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```ivreg_ss.fit( y1, y2, X, W, Z, w = NULL, method = c("akm", "akm0"), beta0 = 0, alpha = 0.05, region_cvar = NULL, sector_cvar = NULL ) ```

## Arguments

 `y1` Outcome variable. A vector of length `N`, with each row corresponding to a region. `y2` Endogenous variable, vector of length `N`, with each row corresponding to a region. `X` Shift-share vector with length `N` of sectoral shocks, aggregated to regional level using the share matrix `W`. That is, each element of `X` corresponds to a region. `W` A matrix of sector shares, so that `W[i, s]` corresponds to share of sector `s` in region `i`. The ordering of the regions must coincide with that in the other inputs, such as `X`. The ordering of the sectors in the columns of `W` is irrelevant but the identity of the sectors in must coincide with those used to construct `X`. `Z` Matrix of regional controls, matrix with `N` rows corresponding to regions. `w` vector of weights (length `N`) to be used in the fitting process. If not `NULL`, weighted least squares is used with weights `w`, i.e., `sum(w * residuals^2)` is minimized. `method` Vector specifying which inference methods to use. The vector elements have to be one or more of the following strings: `"homosk"`Assume i.i.d. homoskedastic errors `"ehw"`Eicker-Huber-White standard errors `"region_cluster"`Standard errors clustered at regional level `"akm"`Adão-Kolesár-Morales `"akm0"`Adão-Kolesár-Morales with null imposed. Note the reported standard error for this method corresponds to the normalized standard error, given by the length of the confidence interval divided by 2z_{1-alpha/2} `"all"`All of the methods above `beta0` null that is tested (only affects reported p-values) `alpha` Determines confidence level of reported confidence intervals, which will have coverage `1-alpha`. `region_cvar` A vector with length `N` of cluster variables, for method `"cluster_region"`. If the vector `1:N` is used, clustering is effectively equivalent to `ehw` `sector_cvar` A vector with length `S` of cluster variables, if sectors are to be clustered, for methods `"akm"` and `"akm0"`. If the vector `1:S` is used, this is equivalent to not clustering.

## Value

Returns an object of class `"SSResults"` containing the estimation and inference results. The `print` function can be used to print a summary of the results. The object is a list with at least the following components:

beta

Point estimate of the effect of interest beta

se, p

A vector of standard errors and a vector of p-values of the null H_0 : beta = beta0 for the inference methods in `method`, with beta0 specified by the argument `beta0`. For the method `"akm0"`, the standard error corresponds to the effective standard error (length of the confidence interval divided by `2*stats::qnorm(1-alpha/2)`)

ci.l, ci.r

Upper and lower endpoints of the confidence interval for the effect of interest beta, for each of the methods in `method`

ShiftShareSE documentation built on Jan. 8, 2020, 1:07 a.m.