Package 'ssBartik'

Description

Paste-ready full-vs-reduced table from [ssb_drop_top()].

Usage

## S3 method for class 'ssb_drop_top'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-droptop",
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Turns an [ssb_estimate()] table into a publication-ready comparison of the point estimate across standard-error methods. '"latex"' uses booktabs rules; '"markdown"' emits a GitHub pipe table. Rows whose standard error is unavailable are dropped. Mirrors [format.ssb_rotemberg()].

Usage

## S3 method for class 'ssb_estimate'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-estimate",
  ...
)

Arguments

Value

A character vector of the table lines (paste-ready); pass to 'writeLines()'.

Description

Paste-ready version of the [ssb_loo()] sensitivity table (one row per dropped shock, with the re-estimated coefficient); the overall estimate is in a note.

Usage

## S3 method for class 'ssb_loo'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-loo",
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Paste-ready statistic/value table for the [ssb_overid()] cross-instrument homogeneity test.

Usage

## S3 method for class 'ssb_overid'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-overid",
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Turns the [ssb_rotemberg()] decomposition into a publication-quality table of the top-weight shocks. The '"latex"' output uses booktabs rules and math-mode headers (load the booktabs LaTeX package); '"markdown"' emits a GitHub pipe table. Both list, per shock, the Rotemberg weight $\hat\alpha_n$, its just-identified estimate $\hat\beta_n$, the first-stage F and the shock $g_n$, with the overall estimate and the positive/negative weight sums in a note.

Usage

## S3 method for class 'ssb_rotemberg'
format(
  x,
  output = c("latex", "markdown"),
  n = 6,
  digits = 3,
  caption = NULL,
  label = "tab:rotemberg",
  ...
)

Arguments

Value

A character vector of the table lines (paste-ready); pass to 'writeLines()'.

See Also

[plot.ssb_rotemberg()] for a rendered image, [ssb_plot_rotemberg()] for the bubble figure.

Description

Paste-ready coefficient table for the [ssb_shock_balance()] test, with the joint Wald statistic in a note.

Usage

## S3 method for class 'ssb_shock_balance'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-shock-balance",
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Paste-ready version of the [ssb_shock_summary()] diagnostic: the top shocks by exposure weight, with the effective number of shocks and concentration in a note.

Usage

## S3 method for class 'ssb_shocks'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-shocks",
  top = 8,
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Paste-ready version of the [ssb_weight_summary()] table (top shocks by weight), with the largest weight and the weight/estimate/F correlations in a note.

Usage

## S3 method for class 'ssb_weight_summary'
format(
  x,
  output = c("latex", "markdown"),
  digits = 3,
  caption = NULL,
  label = "tab:ssb-weights",
  ...
)

Arguments

Value

A character vector of the table lines; pass to 'writeLines()'.

Description

Draws the top-weight-shock table (see [format.ssb_rotemberg()] for the columns) as a paper-style image with normal single-line row spacing. Writes to the active device, or to 'file' when supplied ('.png' default, '.pdf' for vector output). For LaTeX/Markdown source use [format()] instead; for the bubble scatter use [ssb_plot_rotemberg()].

Usage

## S3 method for class 'ssb_rotemberg'
plot(
  x,
  file = NULL,
  width = NULL,
  height = NULL,
  res = 200,
  n = 6,
  digits = 3,
  note = .ssb_rot_note(),
  ...
)

Arguments

Value

The object, invisibly (called for its side effect).

Description

Collapses the unit-level design to one row per shock, following the Borusyak-Hull-Jaravel (2022) equivalence. With controls partialled out of the outcome and treatment (weighted FWL), each shock $n$ gets an exposure weight $s_n=\sum_i e_i s_{in}$ and exposure-weighted means $\bar y_n=\sum_i e_i s_{in}\tilde y_i/s_n$ and $\bar x_n$ similarly. Running an IV of $\bar y_n$ on $\bar x_n$ with instrument $g_n$ and weights $s_n$ reproduces the location-level shift-share estimate exactly (see [ssb_equivalence()]).

Usage

ssb_aggregate(design)

Arguments

Value

A 'data.frame' (class 'ssb_aggregate') with columns 'sector', 'g', 's_bar' (exposure weight), 'x_bar', 'y_bar'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "shift")
ssb_aggregate(d)

Description

'ssb_design()' is the single entry point of the package. It takes the three pieces of a shift-share design — a unit-level table, a long table of exposure shares, and a table of shocks (shifts) — aligns them, and constructs the Bartik instrument $z_i = \sum_n s_{in} g_n$. The resulting object flows directly into diagnostics ([ssb_rotemberg()], [ssb_shock_summary()], ...), estimation ([ssb_estimate()]) and plotting.

Usage

ssb_design(
  data,
  shares,
  shocks,
  y = "y",
  x = "x",
  location = "location",
  sector = "sector",
  time = NULL,
  controls = NULL,
  weights = NULL,
  cluster = NULL,
  share_col = "share",
  shock_col = "shock",
  exogenous = c("shift", "share")
)

Arguments

Details

The **instrument is constructed identically** whichever identification route you take; the 'exogenous' argument only governs which *diagnostics* and *controls* are appropriate downstream (see [ssb_pipeline()]). Set 'exogenous = "share"' for the exogenous-shares route (Goldsmith-Pinkham, Sorkin and Swift 2020; Rotemberg-weight diagnostics) or 'exogenous = "shift"' for the exogenous-shocks route (Borusyak, Hull and Jaravel 2022; Adao, Kolesar and Morales 2019; shock-level diagnostics and AKM inference).

Value

An object of class 'ssb_design'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
d

Description

Removes the 'n' shocks with the largest absolute Rotemberg weight *together* and re-estimates, to see whether the headline result survives without the most influential shocks. (Contrast [ssb_loo()], which drops one at a time.)

Usage

ssb_drop_top(design, n = 5, methods = c("iid", "ehw", "akm", "akm0"))

Arguments

Value

A list (class 'ssb_drop_top') with the 'dropped' sectors and the 'full' and 'reduced' [ssb_estimate()] tables.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_drop_top(d, n = 3)

Description

Verifies numerically that the location-level shift-share IV estimate equals the shock-level IV estimate (Borusyak-Hull-Jaravel 2022). A near-zero difference is a strong internal-consistency check that the instrument and aggregation are behaving as intended.

Usage

ssb_equivalence(design)

Arguments

Value

A list (class 'ssb_equivalence') with 'location', 'shock', and their absolute 'difference'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "shift")
ssb_equivalence(d)

Description

Computes the shift-share 2SLS point estimate of 'x' on 'y' (instrumented by the constructed Bartik instrument, controls partialled out via FWL) and reports a panel of intervals side by side so the practical importance of the inference method is visible:

• 'iid' — classical (homoskedastic) IV,

• 'ehw' — Eicker-Huber-White (heteroskedasticity-robust),

• 'akm', 'akm0' — Adao-Kolesar-Morales exposure-robust inference, via ShiftShareSE when installed,

• 'cluster' — naive cluster-robust (needs 'cluster' in the design),

• 'twoway' — two-way cluster-robust (needs 'cluster' in the design and 'cluster2' here).

The point estimate is identical across rows; only the standard errors and intervals differ, which is exactly what makes the comparison instructive.

Usage

ssb_estimate(
  design,
  methods = c("ehw", "cluster", "akm", "akm0"),
  level = 0.95,
  cluster2 = NULL,
  shock_cluster = NULL
)

Arguments

Details

The primary object of the comparison is the **confidence interval**, not the standard error: AKM0 in particular is defined directly as a (possibly asymmetric, possibly unbounded) interval, and the 'std.error' reported for it is a symmetric pseudo-SE implied by that interval rather than a conventional standard error. Read the table and [ssb_plot_ci()] figure as a comparison of intervals. When the instrument is weak the AKM0 confidence *set* need not be an interval at all: it can be the whole real line or the complement of an interval (a union of two rays). ShiftShareSE encodes the latter as 'conf.low > conf.high'; 'ssb_estimate()' flags both cases in the 'note' column and the table/plot methods render them accordingly.

The default panel is 'ehw' / 'cluster' / 'akm' / 'akm0': two classical robust SEs (heteroskedasticity- and cluster-robust) shown next to the two exposure-robust AKM / AKM0 intervals. 'iid' (homoskedastic) and 'twoway' are not in the default; request them explicitly via 'methods' when wanted (e.g. 'methods = c("iid", "ehw", "cluster", "twoway", "akm", "akm0")', adding 'cluster2' for two-way clustering). The 'cluster' row needs a 'cluster' column in the design (set via [ssb_design()]); without one it is reported as 'NA' with a note rather than an error.

Value

A 'data.frame' of class 'ssb_estimate' with one row per method ('estimate', 'std.error', 'conf.low', 'conf.high'), carrying the first-stage F as an attribute. Plot with [ssb_plot_ci()].

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_estimate(d)

Description

Reports the standard heteroskedasticity-robust first-stage F of the treatment on the constructed instrument, and an exposure-robust "effective" F whose denominator uses the shock-level (AKM-type) variance of the first-stage coefficient — the relevant notion when weak *shocks* are the concern (in the spirit of Montiel Olea & Pflueger 2013, adapted to shift-share).

Usage

ssb_first_stage(design)

Arguments

Value

A list (class 'ssb_first_stage') with 'F_standard', 'F_effective', and the first-stage coefficient 'pi'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_first_stage(d)

Description

Recomputes the overall estimate dropping each of the top sectors (by |Rotemberg weight|) one at a time, to see whether identification hinges on a single shock.

Usage

ssb_loo(
  design,
  top = 5,
  se = c("none", "iid", "ehw", "cluster", "akm", "akm0"),
  level = 0.95
)

Arguments

Value

A 'data.frame' with the dropped 'sector', its 'alpha', and the 'beta_drop' obtained without it (plus the full-sample 'beta_hat' attribute). When 'se' is not '"none"' it also has 'conf.low'/'conf.high' columns and 'se_method'/'level' attributes.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_loo(d, top = 5)

Description

Treats each sector's share as a separate instrument and tests whether the just-identified estimates $\hat\beta_n$ are mutually consistent, using a precision-weighted Cochran's Q statistic $Q=\sum_n (\hat\beta_n-\bar\beta)^2/\widehat{\mathrm{Var}}(\hat\beta_n)$ referred to a $\chi^2_{K-1}$ distribution. Rejection points to a failure of shares/shocks exogeneity **or** to treatment-effect heterogeneity across instruments (Goldsmith-Pinkham, Sorkin & Swift 2020). Very weak instruments are down-weighted automatically; use 'min_F' to drop near-dead instruments entirely.

Usage

ssb_overid(design, min_F = 0)

Arguments

Details

**Caveat.** The $\hat\beta_n$ are estimated from the *same* sample and are therefore mutually correlated; the $\chi^2_{K-1}$ reference treats them as independent and ignores that covariance. Read the p-value as a heuristic screen for gross cross-instrument disagreement, not as a formal overidentification test — for the latter, use a J-type test with an estimator robust to many instruments (e.g. the HFUL-based test in Goldsmith-Pinkham, Sorkin & Swift 2020).

Value

A list (class 'ssb_overid') with 'Q', 'df', 'p', 'I2', 'beta_bar', 'n_instruments', 'n_dropped'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_overid(d)

Description

Given a design, runs the estimation and the route-appropriate battery of diagnostics in one call, dispatching on 'design$exogenous':

• **share** (Goldsmith-Pinkham, Sorkin & Swift 2020): Rotemberg-weight decomposition, leave-one-out sensitivity, and — if 'covariates' are supplied — a share-balance check; a pre-trend check if 'pre_y' is supplied.

• **shift** (Borusyak, Hull & Jaravel 2022): effective-shock / exposure-concentration summary, leave-one-out sensitivity, and the shock-balance hook.

Estimation always reports the full SE panel (naive / EHW / cluster / AKM / AKM0). The point estimate and first-stage F are common to both routes.

Usage

ssb_pipeline(
  design,
  covariates = NULL,
  pre_y = NULL,
  placebo_y = NULL,
  shock_covariates = NULL,
  top = 5,
  level = 0.95
)

Arguments

Value

An 'ssb_result' list with 'estimate', 'route', and route-specific diagnostic elements. 'autoplot()' returns the headline figure.

Description

Runs the *same* shift-share IV but on an outcome that the treatment should not move (a placebo). A coefficient far from zero signals that the design is picking up something other than the intended channel. This is distinct from [ssb_pretrend()], which regresses a *pre-period* outcome on the instrument (reduced form) to look for differential pre-trends.

Usage

ssb_placebo(
  design,
  placebo_y,
  methods = c("ehw", "cluster", "akm"),
  level = 0.95
)

Arguments

Value

An 'ssb_estimate' for the placebo outcome.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
sim$data$y_placebo <- stats::rnorm(nrow(sim$data))   # an unrelated outcome
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_placebo(d, placebo_y = "y_placebo")

Description

Draws the (identical) point estimate with each method's confidence interval, making the practical consequences of the inference method immediately visible — the naive/EHW intervals are typically far too narrow relative to the exposure-robust AKM / AKM0 intervals. The comparison is of *intervals*: AKM0 is defined as an interval directly (and can be asymmetric), so it is the interval, not a standard error, that is the object of interest.

Usage

ssb_plot_ci(x, title = NULL, ...)

Arguments

Value

A 'ggplot' object.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_plot_ci(ssb_estimate(d))

Description

Plots the shift-share estimate re-computed with each top-weight shock dropped (see [ssb_loo()]) against the full estimate (dashed line), so a result that hinges on a single shock is obvious.

Usage

ssb_plot_loo(x, title = NULL, ...)

Arguments

Value

A ggplot2 object.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_plot_loo(ssb_loo(d, top = 5))

Description

Forest plot of the just-identified estimates $\hat\beta_k$ (one per instrument) with confidence intervals, ordered by size, against the precision-weighted mean (dashed line). Wide, mutually inconsistent estimates signal a failure of the exogeneity assumption or treatment-effect heterogeneity (see [ssb_overid()]). Point size is the first-stage F; the axis is trimmed to the bulk since weak instruments have very wide intervals.

Usage

ssb_plot_overid(x, level = 0.95, xlim = NULL, title = NULL, ...)

Arguments

Value

A ggplot2 object.

Description

Histogram of the permuted-shock (placebo) estimates from [ssb_ri()], with the observed estimate marked; the RI p-value is where the observed value falls in this null distribution.

Usage

ssb_plot_ri(x, bins = 30, title = NULL, ...)

Arguments

Value

A ggplot2 object.

Description

Reproduces Figure 1 of Goldsmith-Pinkham, Sorkin and Swift (2020): each sector-cell is a bubble at its first-stage F-statistic (x) and just-identified estimate $\hat\beta_n$ (y); bubble area is proportional to the absolute Rotemberg weight; positive-weight cells are blue open circles and negative ones amber open diamonds; the dashed horizontal line marks the overall estimate $\hat\beta$.

Usage

ssb_plot_rotemberg(x, max_size = 12, label_top = 0, title = NULL, ...)

Arguments

Value

A 'ggplot' object.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_plot_rotemberg(ssb_rotemberg(d))

Description

Lorenz curve of the shock exposure weights from [ssb_shock_summary()]: the further the curve bows below the 45-degree line, the more the identifying variation is concentrated in a few shocks. The effective number of shocks and the HHI are shown in the subtitle.

Usage

ssb_plot_shocks(x, title = NULL, ...)

Arguments

Value

A ggplot2 object.

Description

Reduced-form regression of a pre-period outcome on the constructed instrument (controls partialled out). A coefficient far from zero indicates that exposure predicts differential pre-trends — a threat to identification. This is distinct from [ssb_placebo()], which runs the *full IV* on a placebo outcome; pre-trends ask whether exposure predicts the outcome *before* the shocks, placebo asks whether the design moves an outcome it should not.

Usage

ssb_pretrend(design, pre_y, level = 0.95)

Arguments

Details

Because the regressor is itself a shift-share variable, EHW / cluster standard errors are subject to exactly the over-rejection documented by Adao, Kolesar & Morales (2019): residuals are correlated across units with similar exposure. The test therefore also reports an exposure-robust (AKM-type) standard error that clusters the score at the shock level; treat that one as the headline, especially on the shift route, or spurious "pre-trends" will appear too often.

Value

A list (class 'ssb_pretrend') with the reduced-form coefficient, EHW / cluster / exposure-robust (AKM) standard errors, the corresponding p-values ('p_ehw', 'p_akm'), and intervals ('conf.low'/'conf.high' use the EHW SE; 'conf.low_akm'/'conf.high_akm' the exposure-robust SE).

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
sim$data$y_pre <- stats::rnorm(nrow(sim$data))   # a pre-period outcome
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_pretrend(d, pre_y = "y_pre")

Description

Recentering removes the expected instrument implied by the shock-assignment process, so identification comes only from deviations of shocks from their (conditional) mean. Two methods:

• '"demean"' (default): subtract the single exposure-weighted mean shock $\bar g$. Leaves the point estimate unchanged but makes the identifying variation explicit.

• '"permute"': subtract the *block-specific simple average* shock, i.e. recenter within exchangeability groups. Under uniform within-block permutation every cell in a block is equally likely to receive each of the block's shocks, so $E[g_n]$ is the unweighted within-block mean; subtracting it gives the expectation of the instrument under that assignment process (Borusyak & Hull), computed analytically. With no 'block' this recenters by the grand unweighted mean.

For randomization-inference p-values based on the same permutation idea, see [ssb_ri()].

Usage

ssb_recenter(design, method = c("demean", "permute"), block = NULL, ...)

Arguments

Value

A new [ssb_design()] with recentered shocks/instrument (carries a '"recentered"' attribute).

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "shift")
ssb_recenter(d, method = "demean")

Description

Re-draws the shocks by permutation (optionally within exchangeability 'block's) and reports where the observed statistic falls in the resulting placebo distribution, in the spirit of Adao-Kolesar-Morales (2019) and Borusyak & Hull.

Usage

ssb_ri(design, R = 999, block = NULL, null = 0, seed = NULL)

Arguments

Details

The statistic is Anderson-Rubin-style: the reduced-form coefficient of $y - \beta_0 x$ on the reconstructed instrument, with $\beta_0 =$ 'null'. Under the constant-effects null $\beta = \beta_0$ (plus the exclusion restriction), $y - \beta_0 x$ does not respond to how the shocks are assigned, so the permutation distribution of this statistic is *exact* given the exchangeability encoded in 'block'. Permuting the IV ratio itself (holding the observed treatment fixed) would *not* be exact — the treatment also responds to the shocks through the first stage, and placebo draws with weak first stages give the ratio very heavy tails — so this function does not do that.

Value

A list (class 'ssb_ri') with the IV point estimate 'beta', the observed Anderson-Rubin 'statistic', 'null', 'p_value', 'R', and the vector 'perm' of placebo statistics.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_ri(d, R = 199, seed = 1)

Description

Decomposes the shift-share 2SLS estimate into a weighted sum of the just-identified estimates that use each sector's share as a single instrument, following Goldsmith-Pinkham, Sorkin and Swift (2020):

$\hat\beta = \sum_n \hat\alpha_n \hat\beta_n,\qquad \hat\alpha_n = \frac{g_n\, \tilde s_n' \tilde x}{\sum_{n'} g_{n'}\, \tilde s_{n'}' \tilde x},$

where tildes denote residualisation on the controls (and, in panels, sector-cells are sector $\times$ period pairs). The weights $\hat\alpha_n$ sum to one and measure the sensitivity of $\hat\beta$ to misspecification of each sector's instrument; a small number of large weights is a warning sign. Unlike Goodman-Bacon weights, negative Rotemberg weights are not automatically problematic.

Usage

ssb_rotemberg(design)

Arguments

Value

A 'data.frame' of class 'ssb_rotemberg', one row per sector-cell, with columns 'sector', 'g' (shock), 'alpha' (Rotemberg weight), 'beta' (just-identified estimate), 'F' (first-stage F of that instrument), and 'sign'. Carries the overall estimate 'beta_hat' as an attribute. Pass it to [ssb_plot_rotemberg()] for the canonical figure.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_rotemberg(d)

Description

For the top-exposure sectors, regresses each sector's share on observable unit characteristics to see how strongly exposure correlates with observables — the key credibility check when identification comes from the shares.

Usage

ssb_share_balance(design, covariates, top = 5)

Arguments

Value

A 'data.frame' of slope coefficients and (robust) t-statistics of each covariate in the share regression, one block per tested sector.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_share_balance(d, covariates = "w1", top = 3)

Description

Tests the identifying assumption of the shocks route — that shocks are as-good-as-randomly assigned — by regressing the shocks on pre-determined shock-level characteristics, weighted by exposure (Borusyak, Hull & Jaravel 2022). Coefficients near zero and a non-significant joint test support shock exogeneity.

Usage

ssb_shock_balance(design, shock_covariates, weight = TRUE)

Arguments

Value

A list (class 'ssb_shock_balance') with a coefficient table and the joint Wald test that the characteristics are unrelated to the shocks.

Description

Runs the exposure-weighted IV at the shock level (see [ssb_aggregate()]). The point estimate equals the location-level shift-share estimate; the shock-level heteroskedasticity- or cluster-robust standard error here is the natural shock-level analogue of the AKM exposure-robust SE.

Usage

ssb_shock_iv(design, cluster = NULL, level = 0.95)

Arguments

Value

A one-row 'data.frame' (class 'ssb_shock_iv') with 'estimate', 'std.error', 'conf.low', 'conf.high'.

Description

Reports the Borusyak-Hull-Jaravel (2022) exposure-concentration diagnostics for the shocks route: the average exposure (importance) weight of each shock, its Herfindahl index, and the *effective number of shocks* $1/\sum_n \bar s_n^2$. Few effective shocks undermine the large-n asymptotics that justify the shocks-exogeneity approach.

Usage

ssb_shock_summary(design)

Arguments

Value

A list with 'effective_shocks', 'hhi', 'n_shocks', and a 'data.frame' 'weights' of per-shock importance weights (descending). Class 'ssb_shocks'.

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "shift")
ssb_shock_summary(d)

Description

'plot()' methods that draw the same booktabs-style table [format()] prints, as a standalone image — the sibling of [plot.ssb_rotemberg()]. Pass 'file=' to write a '.png' (default) or '.pdf'; without 'file' the table is drawn on the current graphics device. For LaTeX/Markdown source instead of an image, use 'format(x, "latex")' / 'format(x, "markdown")'.

Usage

## S3 method for class 'ssb_estimate'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

## S3 method for class 'ssb_weight_summary'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

## S3 method for class 'ssb_overid'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

## S3 method for class 'ssb_loo'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

## S3 method for class 'ssb_drop_top'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

Arguments

Value

The 'file' path, invisibly.

Description

Summarises the Rotemberg-weight diagnostic in the spirit of Goldsmith-Pinkham, Sorkin & Swift (2020): the top-weight shocks, the largest single weight, the correlation of the weights with the just-identified estimates and first-stage F, and — if 'covariates' are supplied — the correlation between each shock's Rotemberg weight and its exposure-weighted average of unit observables (do high-weight shocks load on systematically different places?).

Usage

ssb_weight_summary(design, covariates = NULL, top = 5)

Arguments

Value

A list (class 'ssb_weight_summary').

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_weight_summary(d, covariates = "w1")

Description

Convenience wrapper that builds an [ssb_design()] from raw pieces and runs [ssb_pipeline()] — the "give me everything" entry point. Specify the identification route with 'exogenous' and the rest flows through to diagnostics and plots.

Usage

ssbartik(
  data,
  shares,
  shocks,
  y = "y",
  x = "x",
  location = "location",
  sector = "sector",
  time = NULL,
  controls = NULL,
  weights = NULL,
  cluster = NULL,
  share_col = "share",
  shock_col = "shock",
  exogenous = c("shift", "share"),
  covariates = NULL,
  pre_y = NULL,
  placebo_y = NULL,
  shock_covariates = NULL,
  top = 5,
  level = 0.95
)

Arguments

Value

An 'ssb_result' object.

Examples

# Bring your own data; this is a small synthetic design for illustration.
set.seed(1)
n_loc <- 60L; n_sec <- 8L
shares <- expand.grid(location = seq_len(n_loc), sector = seq_len(n_sec))
shares$share <- stats::runif(nrow(shares))
tot <- tapply(shares$share, shares$location, sum)
shares$share <- shares$share / tot[as.character(shares$location)]
shocks <- data.frame(sector = seq_len(n_sec), shock = stats::rnorm(n_sec))
Z <- tapply(shares$share, list(shares$location, shares$sector), sum)
Z[is.na(Z)] <- 0
inst <- as.numeric(Z %*% shocks$shock)
dat <- data.frame(location = seq_len(n_loc),
                  x = 4 * inst + stats::rnorm(n_loc, sd = 0.3))
dat$y <- 1.2 * dat$x + stats::rnorm(n_loc, sd = 0.3)
res <- ssbartik(dat, shares, shocks, exogenous = "share")
res

autoplot(res)                       # headline Rotemberg figure
autoplot(res$estimate)              # CI comparison