scf draft

tests/setup.R

@@ -208,13 +208,13 @@ scf_MIcombine( with( scf_design ,
 scf_MIcombine( with( scf_design ,
 	svyquantile(
 		~ networth ,
-		0.5 , se = TRUE , method = 'constant' , interval.type = 'quantile' 
+		0.5 , se = TRUE , interval.type = 'quantile' 
 ) ) )
 
 scf_MIcombine( with( scf_design ,
 	svyby(
 		~ networth , ~ hhsex , svyquantile ,
-		0.5 , se = TRUE , method = 'constant' , interval.type = 'quantile' ,
+		0.5 , se = TRUE , interval.type = 'quantile' ,
 		ci = TRUE 
 ) ) )
 scf_MIcombine( with( scf_design ,
@@ -274,26 +274,14 @@ stopifnot( round( coef( mean_net_worth ) / 1000 , 2 ) == 746.82 )
 # https://www.federalreserve.gov/publications/files/scf20.pdf#page=11
 stopifnot( abs( 15.6 - round( SE( mean_net_worth ) / 1000 , 1 ) ) < 0.1 )
 
-# compute median net worth using the 2019 PUF
-median_net_worth <-
-	scf_MIcombine( with( scf_design ,
-		svyquantile(
-			~ networth ,
-			0.5 , se = TRUE , method = 'constant' , interval.type = 'quantile' ,
-			ci = TRUE
-	) ) )
-
-# for( this_qrule in c("math","school","shahvaish","hf1","hf2","hf3",
-# 		 "hf4","hf5","hf6","hf7","hf8","hf9") ){
-# 			 print( this_qrule )
-# print( scf_MIcombine( with( scf_design , svyquantile( ~ networth , 0.5 , interval.type = 'quantile' , qrule = this_qrule ) ) ) )
-# }
-
-# confirm the estimate and standard error match FRB calculations within one dollar
-# stopifnot( round( coef( median_net_worth ) ) == 97306 )
-# stopifnot( round( SE( median_net_worth ) ) == 2699 )
-
-# scf_MIcombine( with( scf_design , oldsvyquantile( ~networth , 0.5 , method = 'constant' , interval.type = 'quantile' ) ) )
+# compute quantile with all five implicates stacked
+# matches the public excel tables but not the recommended technique
+fake_design <- svydesign( ~ 1 , data = p_df[ c( 'networth' , 'wgt' ) ] , weights = ~ wgt )
+median_net_worth_incorrect_errors <- svyquantile( ~ networth , fake_design , 0.5 )
+
+# exactly match "Table 4" tab cell V6 of
+# https://www.federalreserve.gov/econres/files/scf2019_tables_public_nominal_historical.xlsx
+stopifnot( round( coef( median_net_worth_incorrect_errors ) / 1000 , 2 ) == 121.76 )
 library(convey)
 scf_design$designs <- lapply( scf_design$designs , convey_prep )