PSLmodels · andersonfrailey · Aug 6, 2022 · Jun 23, 2022 · Jun 27, 2022 · Jun 28, 2022
diff --git a/cps_stage1/stage1.py b/cps_stage1/stage1.py
@@ -6,7 +6,7 @@
 
 def main(syr=2014):
     SYR = str(syr)  # Start year of CPS
-    EYR = 2031  # Last year in our extrapolation
+    EYR = 2032  # Last year in our extrapolation
 
     # Read in state SOI estimates
     soi_estimates = pd.read_csv(

diff --git a/cps_stage1/stage_2_targets.csv b/cps_stage1/stage_2_targets.csv
@@ -1,24 +1,24 @@
-,2014,2015,2016,2017,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031
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-wage8,3433224075,3714896131,3792750634,4090211841,3970523643,4029918419,4227328804,4438472084,4645495496,4659228392,4887194471,5111040680,5328020442,5563882936,5809015135,6047280886,6269410483,6496003272,6724312673,6954682009,7200157530
+,2014,2015,2016,2017,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032
+Single,69579590,71086947,71410690,73021932,72028792,71924422,73183813,74331876,75333822,76224441,77122018,77984804,78861507,79745168,80635787,81540322,82451814,83377223,84309589,85248914,86209112,87169310
+Joint,55128595,57272012,57201828,57987204,57069122,56986429,57984256,58893878,59687730,60393376,61104535,61788129,62482750,63182883,63888529,64605200,65327385,66060595,66799319,67543555,68304329,69065104
+HH,22077498,22134303,21659639,21894095,22854626,22821510,23221112,23585391,23903307,24185899,24470699,24744460,25022636,25303020,25585612,25872620,26161835,26455466,26751304,27049351,27354020,27658689
+SS_return,27401498,28087514,28329015,28967603,31684352,32736570,33818929,34953351,36139836,37386604,38614191,39891101,41154310,42384637,43664287,44875433,46007115,47097695,48141692,49089784,49843325,50509181
+Dep_return,96916673,96505181,94809260,94708629,99911398,100735190,101549290,102373082,103187182,104001282,104805690,105610099,106414507,107209224,107984557,108759890,109525532,110281482,111018049,111744924,112452416,113150216
+INTS,156367939,95881223,96640233,106055367,124828526,125813644,138072890,143545768,146829495,143483221,145797466,144515249,159776760,178478366,194850089,209533038,221792285,235380659,247983914,261900661,275614129,289108682
+DIVS,254702232,260252720,254065327,282336042,340613295,332513764,369521998,401130545,421201081,415393870,416234388,452554926,492313944,522725391,546973043,563757920,575703455,588413096,604764980,624580813,646663497,670962090
+SCHCI,374659000,391975736,389078638,416010104,435503622,432281554,462216808,485895257,491852335,497996743,543105686,581770495,607509568,626542246,647635547,670714542,695367104,723541461,752914726,783037310,814171473,846317215
+SCHCL,57400238,60161435,60996610,69637956,63668344,64552308,73696166,77467361,78425945,79396009,86593999,92758785,96862902,99899374,103257288,106936643,110868560,115362998,120046858,124851258,129810638,134936479
+CGNS,705007630,713598090,634341278,859894631,1249696525,1110880523,1505896298,1632163164,1523239485,2000388649,2297760868,2430936809,2356558504,2164655427,2062640823,2012514781,2007297724,2035004524,2081676029,2143928203,2213089451,2289159775
+Pension,663223262,689991999,693626543,729187412,776568117,800178866,846471849,889515039,925594384,986014024,1058835938,1082181397,1138886986,1190817367,1241421302,1294412840,1347868635,1405900671,1464861219,1527336850,1590939961,1657925510
+SCHEI,846331543,891134646,882933054,973306129,1140431754,1129937243,1245546132,1348629314,1385360103,1312829490,1644760721,1734387231,1761723740,1757661349,1785167124,1786521254,1808441241,1858628702,1933190511,2007583053,2080113666,2155691073
+SCHEL,166947674,177896945,176446339,206369393,169401805,168032834,196514107,212774811,218567895,207131979,259486770,273627238,277951182,277316781,281657421,281857758,285330270,293243589,305013400,316749822,328185738,340105801
+SS,575126918,605152093,619856114,644989570,709246515,726500323,755946821,795975655,847794590,899095911,952065100,1016191751,1097687236,1171648557,1244919727,1322159272,1402216939,1488255926,1577285573,1668903291,1762879029,1762879029
+UCOMP,33288898,27225383,25420264,23946035,9810238,9161105,8628482,7962704,7490002,7746327,66767543,10193061,7633144,8711705,9410771,10239665,11098519,12097186,12882804,13372150,13824879,14211031
+wage1,114320683,112017897,111270085,107376379,132211870,134189618,140763057,147839507,154870229,156870841,171538185,186582787,194756716,201444476,208303716,215951770,224308612,232894095,241639628,250728122,260205307,270048317
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+wage3,381259619,383184602,382875414,382789227,440926749,447522541,469444969,493044939,516492406,523164449,572080058,622253824,649513887,671817575,694693152,720199420,748069498,776702096,805868457,836178596,867785019,900611472
+wage4,418567676,428313928,436140082,441282302,484073517,491314738,515382379,541291719,567033631,574358565,628060798,683144304,713071893,737558102,762672162,790674340,821271637,852706070,884726497,918002627,952701887,988740564
+wage5,418379915,424369612,426147995,437485582,483856372,491094344,515151189,541048906,566779271,574100919,627779062,682837859,712752023,737227248,762330043,790319659,820903231,852323563,884329626,917590830,952274525,988297035
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diff --git a/cps_stage2/cps_weights.csv.gz b/cps_stage2/cps_weights.csv.gz
diff --git a/cps_stage2/solver.jl b/cps_stage2/solver.jl
@@ -80,7 +80,7 @@ function Solve_func(year, tol)
 
 end
 
-year_list = [x for x in 2014:2031]
+year_list = [x for x in 2014:2032]
 tol = 0.70
 
 # Run solver function for all years and tolerances (in order)

diff --git a/cps_stage2/stage2.py b/cps_stage2/stage2.py
@@ -11,7 +11,7 @@
 STAGE_1_PATH = Path(CUR_PATH, "..", "puf_stage1", "Stage_I_factors.csv")
 STAGE_2_PATH = Path(CUR_PATH, "..", "cps_stage1", "stage_2_targets.csv")
 START_YEAR = 2014
-END_YEAR = 2031
+END_YEAR = 2032
 
 # Read hashes used to see which years can be skipped
 with open(Path(CUR_PATH, "..", "datahashes.json")) as f:
@@ -62,12 +62,15 @@ def main():
     # write .npz input files for solver
     skipped_years = []
     for year in range(START_YEAR, END_YEAR + 1):
-        factor_match = _factors[year].equals(CUR_FACTORS[year])
-        target_match = stage_2_targets[f"{year}"].equals(CUR_TARGETS[f"{year}"])
-        if files_match and factor_match and target_match:
-            print(f"Skipping {year}")
-            skipped_years.append(year)
-            continue
+        try:
+            factor_match = _factors[year].equals(CUR_FACTORS[year])
+            target_match = stage_2_targets[f"{year}"].equals(CUR_TARGETS[f"{year}"])
+            if files_match and factor_match and target_match:
+                print(f"Skipping {year}")
+                skipped_years.append(year)
+                continue
+        except KeyError:
+            pass
         dataprep(cps, stage_1_factors, stage_2_targets, year)
 
     # Solver (in Julia)

diff --git a/docs/book/content/methods/CBO_Baseline_Updating_Instructions.md b/docs/book/content/methods/CBO_Baseline_Updating_Instructions.md
@@ -47,9 +47,9 @@ As previously mentioned, most of the variables we used can be found in the
 To manually update these variables download the latest file and copy/paste the 
 specific variables.
 
-Previous Document: July 2020
+Previous Document: February 2021
 
-Current Document: February 2021
+Current Document: May 2022
 
 | Variable | Name In CBO Document                                |
 |----------|-----------------------------------------------------|
@@ -70,9 +70,9 @@ Current Document: February 2021
 
 Source: [Revenue Projections, By Category (CBO)](https://www.cbo.gov/about/products/budget-economic-data#7)
 
-Previous: [January 2020](https://www.cbo.gov/system/files/2020-01/51138-2020-01-revenue-projections.xlsx)
+Previous: [February 2021](https://www.cbo.gov/system/files/2021-02/51138-2021-02-11-revenueprojections.xlsx)
 
-Current: [February 2021](https://www.cbo.gov/system/files/2021-02/51138-2021-02-11-revenueprojections.xlsx)
+Current: [May 2022](https://www.cbo.gov/system/files/2022-05/51138-2022-05-Revenue-Projections.xlsx)
 
 In the revenue projections file, the data is in the `Capital Gains Realizations`
 tab under the `Capital Gains Realizations` column.
@@ -81,9 +81,9 @@ tab under the `Capital Gains Realizations` column.
 
 Source: [IRS Publication 6187 Table 1B](https://www.irs.gov/statistics/projections-of-federal-tax-return-filings)
 
-Previous: [Fall 2019 Report](https://www.irs.gov/pub/irs-soi/19projpub6187tables.xls)
+Previous: [Fall 2020 Report](https://www.irs.gov/pub/irs-soi/20projpub6187tables.xls)
 
-Current: [Fall 2020 Report](https://www.irs.gov/pub/irs-soi/20projpub6187tables.xls)
+Current: [Fall 2021 Report](https://www.irs.gov/pub/irs-soi/21projpub6187tables.xls)
 
 The projections in the publication typically end a few years before the
 10-year projections do. We use the growth rate from the final year in the
@@ -108,7 +108,7 @@ column of this table.
 Source: [BLS Database](http://data.bls.gov/timeseries/CUSR0000SAM?output_view=pct_1mth)
 
 Series ID: CUSR0000SAM
-Access Date: June 16 2021
+Access Date: June 27 2022
 
 Manual Instructions:
 
@@ -124,9 +124,9 @@ Manual Instructions:
 
 Source: [CBO Unemployment Compensation projections](https://www.cbo.gov/about/products/baseline-projections-selected-programs#24)
 
-Previous: [January 2020](https://www.cbo.gov/system/files/2020-01/51316-2020-01-unemployment.pdf)
+Previous: [March 2020](https://www.cbo.gov/system/files/2020-03/51316-2020-03-unemployment.pdf)
 
-Current: [March 2020](https://www.cbo.gov/system/files/2020-03/51316-2020-03-unemployment.pdf)
+Current: [May 2022](https://www.cbo.gov/system/files/2022-05/51316-2022-05-unemployment.xlsx)
 
 Note: Change the `Total Benefits` number from the table to be in terms of
 billions rather than millions of dollars

diff --git a/history/reports/taxdata_report_2022-07-25.pdf b/history/reports/taxdata_report_2022-07-25.pdf
diff --git a/history/reports/taxdata_report_2022-08-02.pdf b/history/reports/taxdata_report_2022-08-02.pdf
diff --git a/puf_stage1/CBO_baseline.csv b/puf_stage1/CBO_baseline.csv
@@ -1,16 +1,16 @@
-,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031
-GDP,14718.6,14418.7,14964.4,15517.9,16163.2,16800,17348.1,18036.7,18624.5,19543,20611.9,21433.2,20933.5,22253.1,23334.8,24322,25402.1,26529.2,27630.3,28689.4,29783.9,30903,32039.8,33237.6
-TPY,12429.6,12087.5,12429.3,13202,13887.7,14166.9,14694.2,15458.5,15928.7,16948.6,17851.9,18551.5,19675.2,20007.9,20357.1,21132.1,22020.3,23006.2,24053.4,25099.9,26242.6,27410.3,28631.6,29983.7
-Wages,6531.9,6251.4,6377.5,6633.2,6932.1,7124.7,7477.8,7854.8,8085.3,8471.5,8894.2,9309.4,9336.7,9793.5,10241.9,10676.8,11149.9,11641.1,12118.2,12563.6,13017.3,13475.2,13936.8,14428.2
-SCHC,979.5,937.5,986.7,1068.1,1187.9,1253.5,1268.5,1336.8,1298.7,1467.5,1542.9,1608,1611.2,1778.7,1785.7,1850.7,1936.1,2023.1,2107.3,2188.7,2272.9,2355.3,2439.3,2530.3
-SCHF,47,35.5,46,75.5,72.3,83.2,78.1,40,43.2,41.5,43,49.7,66.4,62,54.7,58,59.9,61.7,63.3,65.2,67.2,69.6,72.2,74.8
-INTS,1361.6,1264.3,1195,1231.6,1255.9,1255.2,1302,1302.8,1415.3,1577.7,1641.6,1677.4,1638.3,1557.9,1512.7,1526,1549.8,1631.6,1782.2,1958.7,2161.2,2362.2,2586.1,2810.1
-DIVS,805.4,553.7,544.6,682.2,832.7,824.5,815.5,951.1,962.5,1160.9,1305.1,1290.4,1261.2,1345.7,1444.8,1510.2,1566.5,1618.6,1666.1,1717.8,1779,1850.9,1934,2025
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