Fully merge investment with loans and all bridges or loans with all investments, with attributes.

Implements step 3 of https://github.com/FanWangEcon/PrjThaiHFID/issues/22 _notes/issues/issues_20240217_roster_invest_bridge.md

Usage

ffp_hfid_invest_loan_linked(
  df_roster_invest_loan_linker,
  df_invest,
  df_loans_pn_nd,
  df_loans_bridges,
  df_loans_bridges_1t2,
  it_ll_gw1_max = 48,
  it_ll_gw2_max = 48,
  it_ll_gw3_max = 48,
  verbose = FALSE,
  verbose_detail = FALSE,
  it_verbose_detail_nrow = 100
)

Arguments

df_roster_invest_loan_linker

Data frame containing the linker file between roster, investment, and loan information.

df_invest

Data frame containing the investment information.

df_loans_pn_nd

Data frame containing the non-duplicate loan information.

df_loans_bridges

Data frame containing the triply-linked bridge loan information.

df_loans_bridges_1t2

Data frame containing the doubly-linked loan-hook information.

it_ll_gw1_max

Maximum value for ll_gw1.

it_ll_gw2_max

Maximum value for ll_gw2.

it_ll_gw3_max

Maximum value for ll_gw3.

verbose

Logical value indicating whether to print verbose output.

verbose_detail

Logical value indicating whether to print verbose detail output.

it_verbose_detail_nrow

Number of rows to print for verbose detail output.

Value

A list containing the fully merged joint information file.

References

PrjThaiHFID-issue-22, step 3

See also

Used by vignette(s) ffv_invest_loan_bridge. Related issue(s): PrjThaiHFID-#32.

Author

Fan Wang, http://fanwangecon.github.io

Examples

df_invdates_uniq <- PrjThaiHFID::tstm_invdates_uniq
df_loans_bridges_type <- PrjThaiHFID::tstm_loans_bridges_type
df_loans_pn_nd <- PrjThaiHFID::tstm_loans_pn_nd
df_invest <- PrjThaiHFID::tstm_invest

df_loans_hooks <- ffp_hfid_hook_pairs(df_loans_pn_nd)$tstm_loans_hooks
ls_return_bfh <- ffp_hfid_bridge_from_hook(df_loans_pn_nd, df_loans_hooks)
df_loans_bridges <- ls_return_bfh$tstm_loans_bridges
df_loans_bridges_1t2 <- ls_return_bfh$tstm_loans_bridges_1t2

ls_return_lbr <- ffp_hfid_invest_loan_bridge_roster(
  df_invdates_uniq, df_loans_bridges_type, df_loans_pn_nd
)
#> Adding missing grouping variables: `br_type`, `br_type_id`
df_roster_invest_loan_bridge <- ls_return_lbr$tstm_roster_invest_loan_bridge
ls_return_lbl <- ffp_hfid_invest_loan_or_bridge_linker(df_roster_invest_loan_bridge)
df_roster_invest_loan_linker <- ls_return_lbl$tstm_roster_invest_loan_linker

ls_return <- ffp_hfid_invest_loan_linked(
 df_roster_invest_loan_linker,
 df_invest,
 df_loans_pn_nd,
 df_loans_bridges,
 df_loans_bridges_1t2)
print(ls_return)
#> $tstm_roster_invest_loan_linked
#> # A tibble: 145,746 × 44
#>    thres_inv_mgap thres_inv_dfsd hhid_Num ivars      hh_inv_asset_ctr hh_inv_ctr
#>             <dbl>          <dbl>    <int> <chr>                 <int>      <int>
#>  1              2           2.33     1003 agg_BS_10…                1          1
#>  2              2           2.33     1003 agg_BS_10…                1          1
#>  3              2           2.33     1003 agg_BS_10…                1          1
#>  4              2           2.33     1003 agg_BS_10…                1          1
#>  5              2           2.33     1003 agg_BS_10…                1          1
#>  6              2           2.33     1003 agg_BS_10…                1          1
#>  7              2           2.33     1003 agg_BS_10…                1          1
#>  8              2           2.33     1003 agg_BS_10…                1          1
#>  9              2           2.33     1003 agg_BS_10…                1          1
#> 10              2           2.33     1003 agg_BS_10…                1          1
#> # ℹ 145,736 more rows
#> # ℹ 38 more variables: mth_inv_start <dbl>, mth_inv_end <dbl>,
#> #   capital_prior <dbl>, capital_end <dbl>, capital_invest <dbl>,
#> #   hh_loan_id_nd <int>, loan_start <dbl>, loan_end <dbl>,
#> #   number_indi_loan <int>, forinfm4 <chr>, loan_principal_interest <dbl>,
#> #   loan_principal <dbl>, loan_principal_last <dbl>,
#> #   loan_interest_monthly <dbl>, merge_type <chr>, …
#>