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Generate Pseudo CRSP Data

Source: R/download_data_pseudo_crsp.R
download_data_pseudo_crsp.Rd

Returns pseudo CRSP data with the same column layout as download_data_wrds_crsp(). Useful for testing and for reproducing the workflow of analyses that rely on CRSP without a WRDS subscription. The returned values are simulated and not suitable for inference.

Usage

download_data_pseudo_crsp(
  dataset = NULL,
  start_date = NULL,
  end_date = NULL,
  version = "v2",
  additional_columns = NULL,
  add_ccm_links = FALSE,
  adjust_volume = FALSE,
  batch_size = 500,
  n_assets = 1000L,
  seed = 1234L
)

Arguments

dataset

A string specifying the dataset to simulate. Supported: "crsp_monthly" and "crsp_daily".

start_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the start date for the pseudo panel.

end_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the end date for the pseudo panel.

version

Accepted for API compatibility with download_data_wrds_crsp(); the pseudo schema follows the v2 output.

additional_columns

Additional CRSP columns to include. Filled with plausible random draws so call sites that pass additional_columns continue to work; the values themselves are not economically meaningful.

A logical indicating whether CRSP-Compustat links should be appended. When TRUE, the output gains a gvkey column whose values are derived from the same pseudo identifier universe used by download_data_pseudo_ccm_links().

adjust_volume

Accepted for API compatibility with download_data_wrds_crsp(); ignored for pseudo data.

batch_size

Accepted for API compatibility with download_data_wrds_crsp(); ignored for pseudo data.

n_assets

Integer. Number of pseudo firms in the universe. Defaults to 1000.

seed

Integer. Random seed; defaults to 1234. Identical (seed, n_assets) produces identical output across calls and matches the identifier universe used by download_data_pseudo_compustat() and download_data_pseudo_ccm_links().

Value

For "crsp_monthly", a tibble with columns permno, date, calculation_date, ret, shrout, prc, primaryexch, siccd, listing_age, mktcap, mktcap_lag, exchange, industry, and ret_excess. For "crsp_daily", a tibble with columns permno, date, ret, and ret_excess. If add_ccm_links = TRUE, a gvkey column is appended.

Details

Both "crsp_monthly" and "crsp_daily" are supported. The daily panel uses weekdays (Monday-Friday) only; weekend dates are excluded so the pseudo calendar approximates a trading-day grid.

See also

Other pseudo functions: download_data_pseudo_ccm_links(), download_data_pseudo_compustat()

Examples

download_data_pseudo_crsp(
  "crsp_monthly",
  start_date = "2020-01-01",
  end_date = "2024-12-31",
  n_assets = 20
)
#> # A tibble: 1,200 × 14
#>    permno date       calculation_date      ret shrout   prc primaryexch siccd
#>     <int> <date>     <date>              <dbl>  <dbl> <dbl> <chr>       <int>
#>  1      1 2020-01-01 2020-01-31       -0.187   12887. 269.  Q            2118
#>  2      1 2020-02-01 2020-02-29       -0.0861  26252. 202.  Q            2118
#>  3      1 2020-03-01 2020-03-31        0.0599   5872. 346.  Q            2118
#>  4      1 2020-04-01 2020-04-30       -0.0446  45175. 770.  Q            2118
#>  5      1 2020-05-01 2020-05-31       -0.0636  42106. 764.  Q            2118
#>  6      1 2020-06-01 2020-06-30        0.327   21754. 918.  Q            2118
#>  7      1 2020-07-01 2020-07-31        0.243   27680. 836.  Q            2118
#>  8      1 2020-08-01 2020-08-31        0.0724  37520.  27.4 Q            2118
#>  9      1 2020-09-01 2020-09-30       -0.00322 27728. 402.  Q            2118
#> 10      1 2020-10-01 2020-10-31        0.0968  11404. 383.  Q            2118
#> # ℹ 1,190 more rows
#> # ℹ 6 more variables: listing_age <int>, mktcap <dbl>, mktcap_lag <dbl>,
#> #   exchange <chr>, industry <chr>, ret_excess <dbl>
download_data_pseudo_crsp(
  "crsp_daily",
  start_date = "2020-01-01",
  end_date = "2020-03-31",
  n_assets = 20
)
#> # A tibble: 1,300 × 4
#>    permno date            ret ret_excess
#>     <int> <date>        <dbl>      <dbl>
#>  1      1 2020-01-01 -0.0178    -0.0179 
#>  2      1 2020-01-02 -0.00942   -0.00951
#>  3      1 2020-01-03 -0.00485   -0.00492
#>  4      1 2020-01-06  0.0196     0.0195 
#>  5      1 2020-01-07 -0.0328    -0.0330 
#>  6      1 2020-01-08  0.00729    0.00721
#>  7      1 2020-01-09 -0.0241    -0.0241 
#>  8      1 2020-01-10  0.0260     0.0259 
#>  9      1 2020-01-13 -0.0164    -0.0164 
#> 10      1 2020-01-14  0.0120     0.0119 
#> # ℹ 1,290 more rows