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Download and Process Macro Predictor Data

Source: R/download_data_macro_predictors.R
download_data_macro_predictors.Rd

Downloads and processes macroeconomic predictor data based on the specified dataset (monthly, quarterly, or annual), date range, and source URL. The function downloads the data from a Google Sheets export link. It processes the raw data into a structured format, calculating additional financial metrics and filtering by the specified date range.

Usage

download_data_macro_predictors(
  dataset = NULL,
  start_date = NULL,
  end_date = NULL,
  type = deprecated(),
  sheet_id = "1bM7vCWd3WOt95Sf9qjLPZjoiafgF_8EG"
)

Arguments

dataset

The dataset to download ("monthly", "quarterly", "annual").

start_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the start date for the data. If not provided, the full dataset is returned.

end_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the end date for the data. If not provided, the full dataset is returned.

type

[Deprecated] Use dataset instead.

sheet_id

The Google Sheets ID from which to download the dataset, with the default "1bM7vCWd3WOt95Sf9qjLPZjoiafgF_8EG".

Value

A tibble with processed data, filtered by the specified date range and including financial metrics.

References

Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455-1508. doi:10.1093/rfs/hhm014

See also

Other download functions: download_data(), download_data_constituents(), download_data_factors_ff(), download_data_factors_q(), download_data_fred(), download_data_fred_md(), download_data_huggingface(), download_data_jkp(), download_data_osap(), download_data_pastor_stambaugh(), download_data_risk_free(), download_data_stambaugh_yuan(), download_data_stock_prices(), download_factor_library_grid(), download_factor_library_ids()

Examples

# \donttest{
  download_data_macro_predictors("monthly")
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A tibble: 1,164 × 15
#>    date         rp_div    dp    dy    ep     de     svar    bm   ntis    tbl
#>    <date>        <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl> <dbl>  <dbl>  <dbl>
#>  1 1926-12-01 -0.0220  -2.97 -2.96 -2.39 -0.586 0.000465 0.441 0.0509 0.0307
#>  2 1927-01-01  0.0422  -2.94 -2.96 -2.37 -0.568 0.000470 0.444 0.0508 0.0323
#>  3 1927-02-01  0.00363 -2.98 -2.93 -2.43 -0.549 0.000287 0.429 0.0517 0.0329
#>  4 1927-03-01  0.0142  -2.98 -2.97 -2.45 -0.531 0.000924 0.470 0.0464 0.032 
#>  5 1927-04-01  0.0459  -2.98 -2.97 -2.47 -0.513 0.000603 0.457 0.0505 0.0339
#>  6 1927-05-01 -0.0112  -3.03 -2.98 -2.53 -0.495 0.000392 0.435 0.0553 0.0333
#>  7 1927-06-01  0.0575  -3.01 -3.02 -2.53 -0.476 0.000825 0.452 0.0588 0.0307
#>  8 1927-07-01  0.0392  -3.06 -3.00 -2.60 -0.457 0.000426 0.415 0.0597 0.0296
#>  9 1927-08-01  0.0388  -3.10 -3.05 -2.66 -0.439 0.00128  0.396 0.0545 0.027 
#> 10 1927-09-01 -0.0543  -3.13 -3.09 -2.71 -0.421 0.00112  0.381 0.0946 0.0268
#> # ℹ 1,154 more rows
#> # ℹ 5 more variables: lty <dbl>, ltr <dbl>, tms <dbl>, dfy <dbl>, infl <dbl>
  download_data_macro_predictors("quarterly", "2000-01-01", "2020-12-31")
#> # A tibble: 84 × 15
#>    date         rp_div    dp    dy    ep     de    svar    bm     ntis    tbl
#>    <date>        <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl>    <dbl>  <dbl>
#>  1 2000-01-01 -0.0437  -4.49 -4.47 -3.38 -1.11  0.0149  0.150  0.0183  0.0569
#>  2 2000-04-01 -0.0268  -4.47 -4.50 -3.33 -1.13  0.0155  0.157  0.00712 0.0569
#>  3 2000-07-01 -0.0984  -4.48 -4.49 -3.29 -1.19  0.00470 0.154  0.00447 0.06  
#>  4 2000-10-01 -0.142   -4.40 -4.48 -3.27 -1.12  0.0141  0.152 -0.00226 0.0577
#>  5 2001-01-01  0.0418  -4.29 -4.41 -3.24 -1.05  0.0146  0.133 -0.00521 0.0442
#>  6 2001-04-01 -0.169   -4.36 -4.30 -3.50 -0.852 0.0115  0.125  0.00504 0.0349
#>  7 2001-07-01  0.0900  -4.19 -4.35 -3.60 -0.587 0.0123  0.149  0.00865 0.0264
#>  8 2001-10-01 -0.00483 -4.29 -4.19 -3.84 -0.450 0.00724 0.131  0.0135  0.0169
#>  9 2002-01-01 -0.150   -4.29 -4.29 -3.84 -0.451 0.00677 0.237  0.0138  0.0179
#> 10 2002-04-01 -0.195   -4.12 -4.27 -3.61 -0.511 0.0101  0.267  0.0275  0.017 
#> # ℹ 74 more rows
#> # ℹ 5 more variables: lty <dbl>, ltr <dbl>, tms <dbl>, dfy <dbl>, infl <dbl>
# }