{ "paper": { "rank": 10, "paper_id": "aeraej-164", "title": "The Berlin Stock Exchange in Imperial Germany: A Market for New Technology?", "authors": "Sibylle Lehmann-Hasemeyer; Jochen Streb", "year": 2016, "journal": "American Economic Review", "doi": "10.1257/aer.20150626", "package_bytes": 331660, "source_specification": "Table 2" }, "mapping": { "paper_id": "aeraej-164", "match": false, "outcome": "Patente", "treatment": "policy", "unit": "nr", "time": "year", "cluster": "nr", "mutate": { "policy": "as.integer(year >= yearipo)" }, "exclude_pattern": "IPOyear", "benchmark_term": "IPOyear::15", "note": "The source reports event-specific IPO effects relative to event zero. The comparison uses adoption at IPO, a minus-one baseline and a treated-cell post average; latest IPO cohorts serve as controls only before they adopt." }, "status": "estimated", "source_fit": { "formula": "list(linear = Patente ~ i(IPOyear, ref = 11) + i(year), fixef = ~nr)", "coefficients": { "IPOyear::6": -0.320742619794548, "IPOyear::7": -0.415694819417013, "IPOyear::8": -0.432209646437786, "IPOyear::9": -0.315916178901181, "IPOyear::10": 0.0667948100771405, "IPOyear::12": 0.150849058522666, "IPOyear::13": 0.333634460490876, "IPOyear::14": 0.360167041547534, "IPOyear::15": 0.423005534813873, "IPOyear::16": 0.17592338762801, "year::1889": -0.0756020466225113, "year::1890": 0.0166959285772129, "year::1891": 0.0526675517186575, "year::1892": 0.0682200996839294, "year::1893": -0.00451326780789347, "year::1894": -0.264561097350292, "year::1895": -0.423662013020787, "year::1896": -0.468899322834952, "year::1897": -0.566646454567252, "year::1898": -0.554345890719898, "year::1899": -0.430992111931074, "year::1900": -0.310911472189203, "year::1901": -0.417897219015403, "year::1902": -0.302372643238143, "year::1903": -0.144488502808027, "year::1904": -0.24749045427729, "year::1905": 0.344410746446343, "year::1906": 0.406414952868814, "year::1907": 0.420027899357311, "year::1908": 0.714301439638262, "year::1909": 0.681119449728722, "year::1910": 0.363900015179415, "year::1911": -0.0140059359782078, "year::1912": 0.0909078136097232, "year::1913": -0.0728618119854097 }, "se": { "IPOyear::6": 0.161626371515051, "IPOyear::7": 0.193147530567334, "IPOyear::8": 0.188469661777581, "IPOyear::9": 0.152277788814831, "IPOyear::10": 0.202120184232976, "IPOyear::12": 0.14294822117301, "IPOyear::13": 0.315980645011194, "IPOyear::14": 0.23636746815635, "IPOyear::15": 0.201535376828174, "IPOyear::16": 0.110133475937957, "year::1889": 0.0999938839058087, "year::1890": 0.0829986418412321, "year::1891": 0.113574224007936, "year::1892": 0.130024097176519, "year::1893": 0.179165106138738, "year::1894": 0.261435203898542, "year::1895": 0.30928847869383, "year::1896": 0.318104077067724, "year::1897": 0.372301329352438, "year::1898": 0.372226040690446, "year::1899": 0.331718714958102, "year::1900": 0.322170889459078, "year::1901": 0.53385750050771, "year::1902": 0.683369914245293, "year::1903": 0.744448074920583, "year::1904": 0.612005000931476, "year::1905": 0.340764592091919, "year::1906": 0.349151436861235, "year::1907": 0.303771839857469, "year::1908": 0.532281249610418, "year::1909": 0.375495329972037, "year::1910": 0.24439263659426, "year::1911": 0.236332627902552, "year::1912": 0.192239580959771, "year::1913": 0.149472788059074 }, "n": 5039 }, "source_check": { "source_term": "IPOyear::15", "source_beta": 0.423005534813873, "template_source_sample_beta": 0.15738163046659592, "template_source_sample_n": 5039, "template_source_sample_se": 0.1255034599457287, "same_point_specification": false, "dropped_missing_identifiers": 0, "controls": {}, "absolute_coefficient_difference": 0.26562390434727706 }, "settings": { "slug": "aeraej-164", "specification_id": "aeraej-164-source", "source_specification_id": "Table 2", "study": "The Berlin Stock Exchange in Imperial Germany: A Market for New Technology?", "citation": "Sibylle Lehmann-Hasemeyer; Jochen Streb (2016). https://doi.org/10.1257/aer.20150626", "description": "The source reports event-specific IPO effects relative to event zero. The comparison uses adoption at IPO, a minus-one baseline and a treated-cell post average; latest IPO cohorts serve as controls only before they adopt.", "data": "analyses/new-library/tranche50/aeraej-164/panel.parquet", "unit": "unit", "time": "time", "cluster": "cluster", "covariates": [], "time_fe": null, "unit_fe": null, "allow_repeated_cells": false, "period_values": null, "period_step": 1, "latest_cohort_control": true, "reversal_policy": "drop_post_untreated", "test_mode": "classical", "post_weighting": "observations", "display_window": [ -10, 10 ], "labels": { "y": "Patente", "d": "policy", "unit": "nr", "time": "year", "cluster": "nr" } }, "options": { "output_dir": "analyses/new-library/tranche50/aeraej-164/results", "timeout_seconds": 300 }, "comparison": { "schema": "expensive-replication-v1", "config": { "outcomes": [ "y" ], "treatments": [ "d" ], "unit": "unit", "time": "time", "cluster": "cluster", "covariates": [], "baseline_by_time": [], "weight": null, "period_step": 1, "period_values": null, "display_window": [ -10, 10 ], "seed": 20261008, "panelview_max_units": 100, "test_mode": "classical", "test_horizon": null, "post_horizon": null, "post_weighting": "observations", "sample_query": null, "reversal_policy": "drop_post_untreated", "labels": { "y": "Patente", "d": "policy", "unit": "nr", "time": "year", "cluster": "nr" }, "allow_repeated_cells": false, "time_fe": null, "unit_fe": null, "frequency_weights": false, "source_sd": null, "require_never_control_periods": false, "latest_cohort_control": true, "slug": "aeraej-164", "specification_id": "aeraej-164-source", "source_specification_id": "Table 2", "study": "The Berlin Stock Exchange in Imperial Germany: A Market for New Technology?", "citation": "Sibylle Lehmann-Hasemeyer; Jochen Streb (2016). https://doi.org/10.1257/aer.20150626", "description": "The source reports event-specific IPO effects relative to event zero. The comparison uses adoption at IPO, a minus-one baseline and a treated-cell post average; latest IPO cohorts serve as controls only before they adopt.", "data": "analyses/new-library/tranche50/aeraej-164/panel.parquet", "output_dir": "analyses/new-library/tranche50/aeraej-164/results" }, "options": { "output_dir": "analyses/new-library/tranche50/aeraej-164/results", "timeout_seconds": 300 }, "source": { "file": "panel.parquet", "bytes": 13518, "sha256": "b9b95a6534ebc6fefc9b2e03fe3a063cdc844e28dceb344742726278a5494629" }, "regressions": { "y__d": { "sample": { "outcome": "y", "treatment": "d", "source_rows": 5039, "source_sd": 7.035670767468174, "rows": 4820, "units": 474, "clusters": 474, "treated_units": 463, "never_treated_units": 11, "treated_cohorts": 20, "latest_cohort_control_index": 26, "unique_unit_period_cells": 4820, "unsupported_prediction_rows_removed": 0, "rows_without_never_control_time_fe_removed": 0, "initially_treated_units_removed": 0, "post_adoption_untreated_rows_removed": 0, "units_without_minus_one_removed": 0, "additional_rows_removed": 0 }, "method_status": { "Sun\u2013Abraham": "unvalidated: package aggregation disagrees with observed cohort-cell weights" }, "estimates": [ { "method": "Static TWFE", "estimate": 0.021044808241743065, "se": 0.01800014312189244, "pvalue": 0.24293337140627838, "lower": -0.014325328937750455, "upper": 0.05641494542123658 }, { "method": "Dynamic TWFE", "estimate": -0.15574676794799144, "se": 0.09606686778959252, "pvalue": 0.10563407908079751, "lower": -0.34451739433203976, "upper": 0.033023858436056874 }, { "method": "BJS / did2s", "estimate": 0.0077344190947883276, "se": 0.048274755945222604, "pvalue": 0.8727788214108827, "lower": -0.08712508999502484, "upper": 0.10259392818460149 }, { "method": "Sun\u2013Abraham", "estimate": null, "se": null, "pvalue": null, "lower": null, "upper": null, "status": "unvalidated", "reason": "unvalidated: package aggregation disagrees with observed cohort-cell weights" } ], "event_studies": { "Dynamic TWFE": [ { "event_time": -5, "estimate": 0.16344188699985265, "se": 0.07953392264506459, "pvalue": 0.040428048517153044, "lower": 0.007158365511752196, "upper": 0.3197254084879531 }, { "event_time": -4, "estimate": 0.0954388845230692, "se": 0.053164219658109024, "pvalue": 0.07326466220372914, "lower": -0.009028381632220361, "upper": 0.19990615067835876 }, { "event_time": -3, "estimate": 0.03760592921890329, "se": 0.03386220806324912, "pvalue": 0.2673230927890506, "lower": -0.02893303862635637, "upper": 0.10414489706416294 }, { "event_time": -2, "estimate": -0.0028437763809836068, "se": 0.024930944201812476, "pvalue": 0.9092337697608357, "lower": -0.05183288221536527, "upper": 0.04614532945339806 }, { "event_time": 0, "estimate": -0.0653167008007984, "se": 0.0490347251739154, "pvalue": 0.1834852497077134, "lower": -0.16166954334296424, "upper": 0.031036141741367443 }, { "event_time": 1, "estimate": -0.0929930396144723, "se": 0.06208712805319176, "pvalue": 0.13485668290694064, "lower": -0.2149937493561232, "upper": 0.029007670127178573 }, { "event_time": 2, "estimate": -0.11824631975039072, "se": 0.06714879356868216, "pvalue": 0.07889135255840643, "lower": -0.2501931617215049, "upper": 0.013700522220723474 }, { "event_time": 3, "estimate": -0.17312161917902613, "se": 0.10506973464372144, "pvalue": 0.10008182005950503, "lower": -0.3795828068815545, "upper": 0.03333956852350223 }, { "event_time": 4, "estimate": -0.22085564475504482, "se": 0.1432444653665255, "pvalue": 0.12378867011362107, "lower": -0.5023298728157684, "upper": 0.06061858330567865 }, { "event_time": 5, "estimate": -0.3064991153870696, "se": 0.18275560733046428, "pvalue": 0.09418393697643683, "lower": -0.6656124211995277, "upper": 0.05261419042538851 } ], "BJS / did2s": [ { "event_time": -5, "estimate": 0.01815854909949973, "se": 0.009349872414570415, "pvalue": 0.05271616979737088, "lower": -0.0002138753681646781, "upper": 0.03653097356716414 }, { "event_time": -4, "estimate": -0.006930965829892697, "se": 0.005766064541428173, "pvalue": 0.22995446920314933, "lower": -0.01826123650802306, "upper": 0.004399304848237667 }, { "event_time": -3, "estimate": -0.015789835990168585, "se": 0.008936293673005117, "pvalue": 0.07788421148497719, "lower": -0.033349581540525086, "upper": 0.0017699095601879204 }, { "event_time": -2, "estimate": -0.01459621153059732, "se": 0.01095861424827572, "pvalue": 0.18352149073734506, "lower": -0.03612980082674251, "upper": 0.006937377765547866 }, { "event_time": -1, "estimate": 0.019158252332656625, "se": 0.011704842304553284, "pvalue": 0.10234156078523196, "lower": -0.0038416691220158304, "upper": 0.042158173787329084 }, { "event_time": 0, "estimate": -0.00869674368300387, "se": 0.02830402812296352, "pvalue": 0.7587794215843159, "lower": -0.06431393242767926, "upper": 0.04692044506167151 }, { "event_time": 1, "estimate": 0.007947288879641795, "se": 0.032552781976951786, "pvalue": 0.8072317583310351, "lower": -0.05601866718537183, "upper": 0.07191324494465542 }, { "event_time": 2, "estimate": 0.0271059624058544, "se": 0.032786098172217816, "pvalue": 0.40879467027831823, "lower": -0.03731845811562902, "upper": 0.09153038292733782 }, { "event_time": 3, "estimate": 0.021264862885870088, "se": 0.053296959840594835, "pvalue": 0.6900818998606087, "lower": -0.08346323666568531, "upper": 0.12599296243742547 }, { "event_time": 4, "estimate": 0.028167938281807536, "se": 0.07756197278097486, "pvalue": 0.7166424817684416, "lower": -0.1242407175048582, "upper": 0.18057659406847326 }, { "event_time": 5, "estimate": -0.030148805154363703, "se": 0.10203888557174415, "pvalue": 0.7677691960168191, "lower": -0.2306543986863216, "upper": 0.17035678837759416 } ] }, "target": { "event_times": [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ], "weights": [ 0.18897959183673468, 0.17877551020408164, 0.17183673469387756, 0.16285714285714287, 0.1510204081632653, 0.14653061224489797 ], "weighting": "observations" }, "checks": { "did2s_vs_direct_imputation_max_post_difference": 1.734723475976807e-17, "package_aggregate_vs_estimable_cell_weights": 0.0030291304614603887, "saturated_vs_deviations_relative_rss_difference": 1.5816795708445174e-15 }, "inference": { "standard_errors": "Cluster-robust; did2s uses two-stage covariance", "confidence_intervals": "Pointwise t, clusters minus one degrees of freedom" }, "f_test": { "F": 0.4104807135025483, "df_num": 75, "df_den": 4156, "pvalue": 0.9999984892104209, "log10_pvalue": -6.561280731795291e-07, "inference": "Homoskedastic nested-model F", "observations": 4820, "design_columns": 166, "restricted_design_columns": 91, "fixed_effect_rank": 498, "covariance_rank": 75, "rss_unrestricted": 1581.3003316089573, "rss_restricted": 1593.0139977852205, "test_horizon": null, "reference_cohort": 6, "pyfixest": "0.27.0", "formulaic": "1.0.2", "formula": "y~i(rel_time,ref=-1.0)+i(rel_time,cohort_dummy_7,ref=-1.0)+i(rel_time,cohort_dummy_8,ref=-1.0)+i(rel_time,cohort_dummy_9,ref=-1.0)+i(rel_time,cohort_dummy_10,ref=-1.0)+i(rel_time,cohort_dummy_11,ref=-1.0)+i(rel_time,cohort_dummy_12,ref=-1.0)+i(rel_time,cohort_dummy_13,ref=-1.0)+i(rel_time,cohort_dummy_14,ref=-1.0)+i(rel_time,cohort_dummy_15,ref=-1.0)+i(rel_time,cohort_dummy_16,ref=-1.0)+i(rel_time,cohort_dummy_17,ref=-1.0)+i(rel_time,cohort_dummy_18,ref=-1.0)+i(rel_time,cohort_dummy_19,ref=-1.0)+i(rel_time,cohort_dummy_20,ref=-1.0)+i(rel_time,cohort_dummy_21,ref=-1.0)+i(rel_time,cohort_dummy_22,ref=-1.0)+i(rel_time,cohort_dummy_23,ref=-1.0)+i(rel_time,cohort_dummy_24,ref=-1.0)+i(rel_time,cohort_dummy_25,ref=-1.0)|unit_fe+time_fe", "restriction_names": [ "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_7", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_7", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_7", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_7", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_7", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_8", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_8", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_8", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_8", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_8", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_9", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_9", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_9", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_9", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_9", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_10", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_10", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_10", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_10", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_10", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_11", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_11", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_11", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_11", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_11", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_12", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_12", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_12", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_12", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_12", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_13", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_13", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_13", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_13", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_13", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_14", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_14", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_14", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_14", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_14", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_15", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_15", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_15", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_15", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_15", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_16", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_16", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_16", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_16", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_16", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_17", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_17", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_17", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_17", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_17", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_18", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_18", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_18", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_18", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_18", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_19", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_19", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_19", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_19", "C(rel_time, contr.treatment(base=-1.0))[T.5.0]:cohort_dummy_19", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_20", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_20", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_20", "C(rel_time, contr.treatment(base=-1.0))[T.4.0]:cohort_dummy_20", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_21", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_21", "C(rel_time, contr.treatment(base=-1.0))[T.3.0]:cohort_dummy_21", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_22", "C(rel_time, contr.treatment(base=-1.0))[T.2.0]:cohort_dummy_22", "C(rel_time, contr.treatment(base=-1.0))[T.1.0]:cohort_dummy_23" ], "deviations": [ { "event_time": -5.0, "cohort": 7, "estimate": -0.26439435322989935 }, { "event_time": -4.0, "cohort": 7, "estimate": -0.08090805312755311 }, { "event_time": -3.0, "cohort": 7, "estimate": -0.06446586559418876 }, { "event_time": -2.0, "cohort": 7, "estimate": -0.14448020453362032 }, { "event_time": 0.0, "cohort": 7, "estimate": -0.18932172803656655 }, { "event_time": 1.0, "cohort": 7, "estimate": -0.16206946250977095 }, { "event_time": 2.0, "cohort": 7, "estimate": 0.17241703429222943 }, { "event_time": 3.0, "cohort": 7, "estimate": -0.0005604196669664052 }, { "event_time": 4.0, "cohort": 7, "estimate": -0.5765767632038388 }, { "event_time": 5.0, "cohort": 7, "estimate": 0.6089833318017996 }, { "event_time": -5.0, "cohort": 8, "estimate": -0.11241018885667975 }, { "event_time": -4.0, "cohort": 8, "estimate": 0.09259447230882972 }, { "event_time": -3.0, "cohort": 8, "estimate": 0.03409849443257007 }, { "event_time": -2.0, "cohort": 8, "estimate": 0.0668382754654448 }, { "event_time": 0.0, "cohort": 8, "estimate": -0.11638390074149033 }, { "event_time": 1.0, "cohort": 8, "estimate": 0.2110906902611016 }, { "event_time": 2.0, "cohort": 8, "estimate": 0.3603323137401746 }, { "event_time": 3.0, "cohort": 8, "estimate": -0.3916225849816004 }, { "event_time": 4.0, "cohort": 8, "estimate": 0.19127125545565182 }, { "event_time": 5.0, "cohort": 8, "estimate": 0.46449543921627445 }, { "event_time": -5.0, "cohort": 9, "estimate": -0.05245487895385301 }, { "event_time": -4.0, "cohort": 9, "estimate": 0.05857596606524315 }, { "event_time": -3.0, "cohort": 9, "estimate": 0.1328650870411816 }, { "event_time": -2.0, "cohort": 9, "estimate": -0.026653077984654744 }, { "event_time": 0.0, "cohort": 9, "estimate": 0.16179190636464247 }, { "event_time": 1.0, "cohort": 9, "estimate": 0.2814027919923668 }, { "event_time": 2.0, "cohort": 9, "estimate": -0.12369865774941907 }, { "event_time": 3.0, "cohort": 9, "estimate": 0.2538197976707405 }, { "event_time": 4.0, "cohort": 9, "estimate": -0.04999257345218622 }, { "event_time": 5.0, "cohort": 9, "estimate": 1.4467412575365626 }, { "event_time": -5.0, "cohort": 10, "estimate": -0.40602421757520774 }, { "event_time": -4.0, "cohort": 10, "estimate": -0.1682797753768226 }, { "event_time": -3.0, "cohort": 10, "estimate": -0.28853785518872965 }, { "event_time": -2.0, "cohort": 10, "estimate": -0.35356933862136886 }, { "event_time": 0.0, "cohort": 10, "estimate": -0.04406582998336156 }, { "event_time": 1.0, "cohort": 10, "estimate": -0.5023872945679684 }, { "event_time": 2.0, "cohort": 10, "estimate": 0.2331322850349126 }, { "event_time": 3.0, "cohort": 10, "estimate": -0.24052225639644154 }, { "event_time": 4.0, "cohort": 10, "estimate": 0.635296361338697 }, { "event_time": 5.0, "cohort": 10, "estimate": 0.32524911120042266 }, { "event_time": -5.0, "cohort": 11, "estimate": -0.37140848524245607 }, { "event_time": -4.0, "cohort": 11, "estimate": -0.31706356862918994 }, { "event_time": -3.0, "cohort": 11, "estimate": -0.3633803914947741 }, { "event_time": -2.0, "cohort": 11, "estimate": -0.12718611980036237 }, { "event_time": 0.0, "cohort": 11, "estimate": -0.5910379552364248 }, { "event_time": 1.0, "cohort": 11, "estimate": 0.09281709908679024 }, { "event_time": 2.0, "cohort": 11, "estimate": -0.005790745032088207 }, { "event_time": 3.0, "cohort": 11, "estimate": 0.750265486567866 }, { "event_time": 4.0, "cohort": 11, "estimate": -0.1281543341757918 }, { "event_time": 5.0, "cohort": 11, "estimate": 0.2293113082550028 }, { "event_time": -5.0, "cohort": 12, "estimate": 0.03284582872231835 }, { "event_time": -4.0, "cohort": 12, "estimate": 0.10682875056764589 }, { "event_time": -3.0, "cohort": 12, "estimate": 0.3817275754891465 }, { "event_time": -2.0, "cohort": 12, "estimate": 0.40333157533461467 }, { "event_time": 0.0, "cohort": 12, "estimate": 0.6462086043537492 }, { "event_time": 1.0, "cohort": 12, "estimate": 0.5541745049096336 }, { "event_time": 2.0, "cohort": 12, "estimate": 1.6460059517852192 }, { "event_time": 3.0, "cohort": 12, "estimate": 0.7126001990136798 }, { "event_time": 4.0, "cohort": 12, "estimate": 0.6335950822493872 }, { "event_time": 5.0, "cohort": 12, "estimate": 0.7350753949180876 }, { "event_time": -5.0, "cohort": 13, "estimate": -0.6427421537221917 }, { "event_time": -4.0, "cohort": 13, "estimate": -0.18166884002800324 }, { "event_time": -3.0, "cohort": 13, "estimate": -0.15089577224967626 }, { "event_time": -2.0, "cohort": 13, "estimate": -0.6562247928584648 }, { "event_time": 0.0, "cohort": 13, "estimate": -0.2839484967566779 }, { "event_time": 1.0, "cohort": 13, "estimate": 0.7523062550920936 }, { "event_time": 2.0, "cohort": 13, "estimate": 0.07761282812511716 }, { "event_time": 3.0, "cohort": 13, "estimate": -0.274119281584698 }, { "event_time": 4.0, "cohort": 13, "estimate": -0.6397495572340137 }, { "event_time": 5.0, "cohort": 13, "estimate": 0.2525487837055544 }, { "event_time": -5.0, "cohort": 14, "estimate": -0.1545686643453358 }, { "event_time": -4.0, "cohort": 14, "estimate": 0.07373205450891203 }, { "event_time": -3.0, "cohort": 14, "estimate": -0.43378106047025883 }, { "event_time": -2.0, "cohort": 14, "estimate": 0.1656406062826632 }, { "event_time": 0.0, "cohort": 14, "estimate": 0.8773081912244466 }, { "event_time": 1.0, "cohort": 14, "estimate": 0.10893947650154716 }, { "event_time": 2.0, "cohort": 14, "estimate": 0.08949691752301453 }, { "event_time": 3.0, "cohort": 14, "estimate": -0.3877109453792008 }, { "event_time": 4.0, "cohort": 14, "estimate": -0.10690485104856678 }, { "event_time": 5.0, "cohort": 14, "estimate": 0.12148061110461071 }, { "event_time": -5.0, "cohort": 15, "estimate": -1.0004035437948318 }, { "event_time": -4.0, "cohort": 15, "estimate": -1.3462348313338044 }, { "event_time": -3.0, "cohort": 15, "estimate": -0.7473300113402997 }, { "event_time": -2.0, "cohort": 15, "estimate": -1.0147894292245476 }, { "event_time": 0.0, "cohort": 15, "estimate": -0.8785482827265015 }, { "event_time": 1.0, "cohort": 15, "estimate": -0.9466504439649347 }, { "event_time": 2.0, "cohort": 15, "estimate": -1.0699362183836123 }, { "event_time": 3.0, "cohort": 15, "estimate": -0.9329272713877719 }, { "event_time": 4.0, "cohort": 15, "estimate": -1.2248356115237569 }, { "event_time": 5.0, "cohort": 15, "estimate": -0.87596404892951 }, { "event_time": -5.0, "cohort": 16, "estimate": -0.7549621385814134 }, { "event_time": -4.0, "cohort": 16, "estimate": 0.07992123051588079 }, { "event_time": -3.0, "cohort": 16, "estimate": -0.17771809893979676 }, { "event_time": -2.0, "cohort": 16, "estimate": 0.7539882137113927 }, { "event_time": 0.0, "cohort": 16, "estimate": -0.18861866443002395 }, { "event_time": 1.0, "cohort": 16, "estimate": -0.38640637907854136 }, { "event_time": 2.0, "cohort": 16, "estimate": 0.01859888265077775 }, { "event_time": 3.0, "cohort": 16, "estimate": -0.49786391097609545 }, { "event_time": 4.0, "cohort": 16, "estimate": -0.6654097999755171 }, { "event_time": 5.0, "cohort": 16, "estimate": -0.29128115235944746 }, { "event_time": -4.0, "cohort": 17, "estimate": -0.037553818203405906 }, { "event_time": -3.0, "cohort": 17, "estimate": 0.9152139998932679 }, { "event_time": -2.0, "cohort": 17, "estimate": 0.060957514858501344 }, { "event_time": 0.0, "cohort": 17, "estimate": -0.2940124201483546 }, { "event_time": 1.0, "cohort": 17, "estimate": 0.1588543716388075 }, { "event_time": 2.0, "cohort": 17, "estimate": 0.13831731364033523 }, { "event_time": 3.0, "cohort": 17, "estimate": -0.3242033998180633 }, { "event_time": 4.0, "cohort": 17, "estimate": -0.6046195002450447 }, { "event_time": 5.0, "cohort": 17, "estimate": -0.03456792737860073 }, { "event_time": -4.0, "cohort": 18, "estimate": 1.1387414432286194 }, { "event_time": -3.0, "cohort": 18, "estimate": 0.33894768546549164 }, { "event_time": -2.0, "cohort": 18, "estimate": 0.17748501118555343 }, { "event_time": 0.0, "cohort": 18, "estimate": 0.23701359676811648 }, { "event_time": 1.0, "cohort": 18, "estimate": 0.03668860659390985 }, { "event_time": 2.0, "cohort": 18, "estimate": 0.0028174089154852644 }, { "event_time": 3.0, "cohort": 18, "estimate": -0.41764869984064223 }, { "event_time": 4.0, "cohort": 18, "estimate": -0.37351163777184926 }, { "event_time": 5.0, "cohort": 18, "estimate": 0.20007172128380457 }, { "event_time": -4.0, "cohort": 19, "estimate": -0.6847611507025038 }, { "event_time": -3.0, "cohort": 19, "estimate": -0.7982546185163821 }, { "event_time": -2.0, "cohort": 19, "estimate": -1.0580183401669299 }, { "event_time": 0.0, "cohort": 19, "estimate": -0.6753058952805276 }, { "event_time": 1.0, "cohort": 19, "estimate": -0.6293217350692908 }, { "event_time": 2.0, "cohort": 19, "estimate": -0.3580531932612126 }, { "event_time": 3.0, "cohort": 19, "estimate": -0.6764040635978759 }, { "event_time": 4.0, "cohort": 19, "estimate": -0.8535420203967564 }, { "event_time": 5.0, "cohort": 19, "estimate": -0.5149465874893708 }, { "event_time": -4.0, "cohort": 20, "estimate": 0.044388015807198436 }, { "event_time": -3.0, "cohort": 20, "estimate": -0.2310179210028072 }, { "event_time": -2.0, "cohort": 20, "estimate": 0.07342264258522244 }, { "event_time": 0.0, "cohort": 20, "estimate": -0.3049091852947818 }, { "event_time": 1.0, "cohort": 20, "estimate": -0.4246352169720088 }, { "event_time": 2.0, "cohort": 20, "estimate": -0.18878587955804133 }, { "event_time": 3.0, "cohort": 20, "estimate": -0.41927280079689977 }, { "event_time": 4.0, "cohort": 20, "estimate": -0.4749517691370243 }, { "event_time": -4.0, "cohort": 21, "estimate": -0.08529753760287336 }, { "event_time": -3.0, "cohort": 21, "estimate": 0.2832320241506026 }, { "event_time": -2.0, "cohort": 21, "estimate": 0.21135992184359711 }, { "event_time": 0.0, "cohort": 21, "estimate": -0.12395201400405456 }, { "event_time": 1.0, "cohort": 21, "estimate": 0.03222691558607209 }, { "event_time": 2.0, "cohort": 21, "estimate": 0.14661726454127882 }, { "event_time": 3.0, "cohort": 21, "estimate": -0.19757594376532767 }, { "event_time": -4.0, "cohort": 22, "estimate": 0.46515998671760006 }, { "event_time": -3.0, "cohort": 22, "estimate": 0.22524139839773363 }, { "event_time": -2.0, "cohort": 22, "estimate": 0.04751216051917567 }, { "event_time": 0.0, "cohort": 22, "estimate": -0.028408749464876343 }, { "event_time": 1.0, "cohort": 22, "estimate": 0.014153004567719077 }, { "event_time": 2.0, "cohort": 22, "estimate": 0.07997758044148567 }, { "event_time": -4.0, "cohort": 23, "estimate": -0.0722217339383916 }, { "event_time": -3.0, "cohort": 23, "estimate": -0.2244027018308134 }, { "event_time": -2.0, "cohort": 23, "estimate": -0.2610076134964544 }, { "event_time": 0.0, "cohort": 23, "estimate": -0.1284364197106289 }, { "event_time": 1.0, "cohort": 23, "estimate": -0.2112991163425023 }, { "event_time": -3.0, "cohort": 24, "estimate": -0.14885186670688488 }, { "event_time": -2.0, "cohort": 24, "estimate": -0.0975928292747209 }, { "event_time": 0.0, "cohort": 24, "estimate": -0.11361507881608576 } ] } } }, "alternative_models": { "y__d": [ { "method": "CS-DID", "status": "unidentified", "reason": "UnidentifiedTarget: CS-DID cannot estimate 31 observed post-treatment group-time cells with the configured controls; no target cells were dropped. See csdid-group-time.json and package log." }, { "method": "SDID", "status": "unidentified", "reason": "ValueError: SDID cohort 5 has no observed donor/treated pre-period overlap", "parameters": {}, "crabbymetrics_build": { "source_commit": "cb3fcc0e8664cdbb6848d3998ddc4365d65740dc", "extension_sha256": "3e1dd2e2bd6d38612b9200b15094fc37138cd002749f8b31148d6265fc533cbd" } }, { "method": "Matrix completion", "class": "MatrixCompletion", "parameters": { "lambda_fraction": 0.25, "max_iterations": 1000, "tolerance": 1e-12 }, "partial_pooling": { "parameter": null, "value": null, "status": "not_applicable", "fit_scope": "Joint untreated-cell fit", "within_cohort_target": null, "cross_cohort_partial_pooling": false, "shared_structure": "Low-rank matrix and unit/time effects", "note": "Regularization penalties are not partial-pooling parameters." }, "estimate": 0.042585638202474595, "event_study": [ { "event_time": -5, "estimate": 0.01070766594090691, "raw_gap": 0.01070766594090691, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": 0.0019371990681956736 }, { "event_time": -4, "estimate": -0.004036676261521852, "raw_gap": -0.004036676261521852, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": -0.012807143134233088 }, { "event_time": -3, "estimate": -0.009898527765746958, "raw_gap": -0.009898527765746958, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": -0.018668994638458194 }, { "event_time": -2, "estimate": -0.005542889831422617, "raw_gap": -0.005542889831422617, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": -0.014313356704133853 }, { "event_time": -1, "estimate": 0.008770466872711236, "raw_gap": 0.008770466872711236, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": 0.0 }, { "event_time": 0, "estimate": 0.00594734880094606, "raw_gap": 0.00594734880094606, "n": 463, "n_cells": 463, "weight_sum": 463.0, "relative_to_minus_one": -0.0028231180717651757 }, { "event_time": 1, "estimate": 0.028711570137444995, "raw_gap": 0.028711570137444995, "n": 438, "n_cells": 438, "weight_sum": 438.0, "relative_to_minus_one": 0.019941103264733757 }, { "event_time": 2, "estimate": 0.05807843702908531, "raw_gap": 0.05807843702908531, "n": 421, "n_cells": 421, "weight_sum": 421.0, "relative_to_minus_one": 0.04930797015637407 }, { "event_time": 3, "estimate": 0.06276737433178738, "raw_gap": 0.06276737433178738, "n": 399, "n_cells": 399, "weight_sum": 399.0, "relative_to_minus_one": 0.05399690745907614 }, { "event_time": 4, "estimate": 0.0776852636253066, "raw_gap": 0.0776852636253066, "n": 370, "n_cells": 370, "weight_sum": 370.0, "relative_to_minus_one": 0.06891479675259536 }, { "event_time": 5, "estimate": 0.02999100694159413, "raw_gap": 0.02999100694159413, "n": 359, "n_cells": 359, "weight_sum": 359.0, "relative_to_minus_one": 0.021220540068882895 } ], "package_event_study": { "event_time": [ -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ], "estimate": [ 0.010707665940906911, -0.004036676261521848, -0.00989852776574696, -0.005542889831422623, 0.00877046687271123, 0.005947348800946034, 0.028711570137445002, 0.058078437029085314, 0.06276737433178733, 0.07768526362530671, 0.02999100694159412 ], "n": [ 463.0, 463.0, 463.0, 463.0, 463.0, 463.0, 438.0, 421.0, 399.0, 370.0, 359.0 ] }, "sdid_pre_gap_correction": [], "diagnostics": { "converged": true, "iterations": 140, "termination_reason": "Relative objective tolerance reached", "objective": 0.07769713545020925, "lambda_l": 0.0037059697178866693, "singular_values": [ 13.621578578696568, 0.5820881508482811, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], "history_objective": [ 0.07853023785541001, 0.07812852727041712, 0.07799591923559165, 0.07791739958529803, 0.07786371989466254, 0.0778251873257404, 0.07779688775165845, 0.07777577974628794, 0.07775983087573927, 0.07774763636938956, 0.0777382063550687, 0.0777308337980292, 0.07772500820272668, 0.07772035751077602, 0.07771660819723564, 0.07771355747136614, 0.0777110537012039, 0.0777089825128439, 0.07770725685112266, 0.07770580983019626, 0.07770458956257585, 0.07770355539910188, 0.0777026751799882, 0.07770192321355301, 0.07770127878089722, 0.07770072502231276, 0.07770024810199673, 0.0776998365766283, 0.07769948091406349, 0.07769917312320715, 0.0776989064667451, 0.07769867523606097, 0.07769847457317906, 0.07769830032855436, 0.07769814894643448, 0.07769801737161573, 0.07769790297295812, 0.07769780348015803, 0.07769771693110185, 0.07769764162774892, 0.07769757609894887, 0.07769751906894508, 0.07769746943057745, 0.07769742622239623, 0.07769738860905319, 0.07769735586445645, 0.07769732735726026, 0.0776973025383476, 0.07769728093001091, 0.07769726211658859, 0.07769724573635198, 0.07769723147446936, 0.07769721905690034, 0.07769720824509113, 0.07769719883136884, 0.0776971906349325, 0.07769718349837293, 0.07769717728464098, 0.07769717187441105, 0.0776971671637898, 0.0776971630623165, 0.07769715949122744, 0.07769715638194273, 0.07769715367474883, 0.07769715131765306, 0.07769714926538505, 0.07769714747852721, 0.0776971459227587, 0.07769714456819622, 0.07769714338881994, 0.07769714236197506, 0.07769714146793608, 0.07769714068952774, 0.07769714001179617, 0.07769713942172071, 0.07769713890796506, 0.07769713846065848, 0.07769713807120707, 0.07769713773212804, 0.0776971374369065, 0.0776971371798697, 0.07769713695607935, 0.07769713676123523, 0.07769713659159316, 0.07769713644389359, 0.07769713631529851, 0.07769713620333688, 0.07769713610585711, 0.07769713602098595, 0.07769713594709307, 0.07769713588275792, 0.0776971358267445, 0.07769713577797638, 0.07769713573551654, 0.07769713569854853, 0.07769713566636266, 0.07769713563833985, 0.07769713561394159, 0.07769713559269956, 0.0776971355742051, 0.0776971355581029, 0.07769713554408353, 0.07769713553187765, 0.07769713552125063, 0.07769713551199811, 0.07769713550394237, 0.07769713549692886, 0.07769713549082236, 0.07769713548550573, 0.07769713548087698, 0.07769713547684691, 0.07769713547333817, 0.07769713547028301, 0.07769713546762341, 0.07769713546530757, 0.0776971354632915, 0.07769713546153603, 0.07769713546000777, 0.07769713545867707, 0.07769713545751852, 0.0776971354565098, 0.07769713545563187, 0.07769713545486712, 0.07769713545420145, 0.0776971354536217, 0.0776971354531173, 0.07769713545267787, 0.07769713545229555, 0.07769713545196234, 0.07769713545167242, 0.07769713545142003, 0.07769713545120022, 0.07769713545100881, 0.0776971354508421, 0.07769713545069719, 0.07769713545057082, 0.07769713545046088, 0.07769713545036515, 0.07769713545028178, 0.07769713545020925 ], "history_rmse": [ 0.16002746732365106, 0.1588966956513398, 0.15858529263282073, 0.15845338167220824, 0.15839138496572255, 0.15835701470933797, 0.1583340339188103, 0.15831642189576606, 0.15830198791915145, 0.15828991036099838, 0.15827982803927762, 0.15827152085684704, 0.15826480599561468, 0.15825950941174532, 0.15825546165422594, 0.15825250021525053, 0.15825047262419253, 0.1582492386914026, 0.15824867168947332, 0.15824865865602897, 0.1582491000697762, 0.15824990912075554, 0.158251010744315, 0.1582523405405022, 0.15825384366238077, 0.15825547372797916, 0.1582571917898652, 0.15825896538162432, 0.15826076765045546, 0.15826257657833548, 0.15826437428983983, 0.1582661464420212, 0.15826788169025693, 0.1582695712232413, 0.15827120836014583, 0.158272788203108, 0.15827430733858242, 0.15827576358158316, 0.1582771557573547, 0.15827848351559035, 0.1582797471728194, 0.1582809475791184, 0.15828208600575108, 0.15828316405077314, 0.15828418356002022, 0.1582851465612524, 0.1582860552094931, 0.15828691174191076, 0.15828771844079687, 0.15828847760340017, 0.15828919151755286, 0.15828986244218618, 0.15829049259195654, 0.15829108412530374, 0.15829163913540445, 0.1582921596435, 0.15829264759422837, 0.15829310485258227, 0.15829353320220876, 0.1582939343448202, 0.15829430990046287, 0.1582946614085119, 0.1582949903292189, 0.1582952980456872, 0.15829558586618545, 0.15829585502669782, 0.15829610669364674, 0.1582963419667278, 0.15829656188180827, 0.15829676741384788, 0.15829695947981567, 0.1582971389415667, 0.15829730660866898, 0.15829746324116062, 0.15829760955222302, 0.15829774621076761, 0.15829787384392086, 0.15829799303941344, 0.15829810434786126, 0.158298208284947, 0.1582983053334932, 0.15829839594543962, 0.15829848054371007, 0.15829855952398686, 0.15829863325639212, 0.15829870208706948, 0.15829876633968268, 0.1582988263168254, 0.15829888230135186, 0.15829893455762997, 0.1582989833327134, 0.1582990288574548, 0.1582990713475411, 0.15829911100447192, 0.1582991480164726, 0.15829918255936073, 0.15829921479734205, 0.15829924488377323, 0.1582992729618688, 0.15829929916535884, 0.15829932361911597, 0.15829934643973154, 0.15829936773606215, 0.15829938760973675, 0.1582994061556332, 0.15829942346232395, 0.15829943961249204, 0.1582994546833179, 0.15829946874684703, 0.15829948187032766, 0.15829949411652824, 0.15829950554403524, 0.15829951620752936, 0.15829952615804965, 0.15829953544322609, 0.1582995441075164, 0.1582995521924088, 0.15829955973662527, 0.15829956677630141, 0.15829957334516156, 0.15829957947467876, 0.15829958519422405, 0.15829959053120446, 0.15829959551119982, 0.15829960015807865, 0.15829960449411767, 0.15829960854010047, 0.15829961231542602, 0.15829961583819313, 0.15829961912529458, 0.1582996221924914, 0.1582996250544917, 0.15829962772502035, 0.15829963021688429, 0.1582996325420356, 0.1582996347116244, 0.1582996367360581, 0.15829963862504604, 0.15829964038764938, 0.15829964203232313 ], "n_observed": 4820, "n_missing": 7030, "n_treated_observed": 2450, "n_untreated_observed": 2370 }, "checks": { "scalar_vs_treated_cell_average": 6.938893903907228e-18, "native_scalar_vs_unweighted_matrix_average": 2.0816681711721685e-17 }, "native_matrix_att": 0.042585638202474616, "matrix_preparation": { "source_rows": 4820, "matrix_shape": [ 474, 25 ], "observed_cells": 4820, "missing_cells": 7030, "repeated_cells": 0, "minimum_cell_count": 1, "maximum_cell_count": 1, "observation_mass": 4820.0, "treated_observation_mass": 2450.0, "within_cell_aggregation": "Observation-weighted outcome mean", "fitting_weights": "Standard crabbymetrics objective on cell means; no cell-count precision weighting", "reporting_weights": "Sum of original observation weights per treated unit-period" }, "seconds": 0.04039429174736142, "crabbymetrics": "0.9.1", "crabbymetrics_build": { "source_commit": "cb3fcc0e8664cdbb6848d3998ddc4365d65740dc", "extension_sha256": "3e1dd2e2bd6d38612b9200b15094fc37138cd002749f8b31148d6265fc533cbd" }, "normalization": "Original source outcome / source SD; no mean subtraction", "target": "All observed treated observations, using configured observation weights; cell mass equals sum of row weights", "status": "estimated" }, { "method": "Horizontal ridge", "status": "unidentified", "reason": "ValueError: ridge cohort 5, period 5: no donor/pre-period overlap for observed treated outcomes", "parameters": { "penalty": 1.0 }, "crabbymetrics_build": { "source_commit": "cb3fcc0e8664cdbb6848d3998ddc4365d65740dc", "extension_sha256": "3e1dd2e2bd6d38612b9200b15094fc37138cd002749f8b31148d6265fc533cbd" } }, { "method": "Multisynth (cohort-averaged)", "status": "unsupported", "reason": "multisynth-cohort: requires a complete finite outcome matrix after unit-period aggregation; 474 x 25 grid has 7030 missing cells, which are not zero-filled." }, { "method": "Multisynth (individual)", "status": "unsupported", "reason": "multisynth-individual: requires a complete finite outcome matrix after unit-period aggregation; 474 x 25 grid has 7030 missing cells, which are not zero-filled." } ] }, "combined_table": [ { "Outcome / treatment": "y__d", "Estimator": "Static TWFE", "Estimate (SDs)": 0.021044808241743065, "SE": 0.01800014312189244, "Aggregation": "Coefficient", "Event study": "No", "Status": "estimated", "Note": "" }, { "Outcome / treatment": "y__d", "Estimator": "Dynamic TWFE", "Estimate (SDs)": -0.15574676794799144, "SE": 0.09606686778959252, "Aggregation": "observations", "Event study": "Yes", "Status": "estimated", "Note": "" }, { "Outcome / treatment": "y__d", "Estimator": "BJS / did2s", "Estimate (SDs)": 0.0077344190947883276, "SE": 0.048274755945222604, "Aggregation": "observations", "Event study": "Yes", "Status": "estimated", "Note": "" }, { "Outcome / treatment": "y__d", "Estimator": "Sun\u2013Abraham", "Estimate (SDs)": null, "SE": null, "Aggregation": "observations", "Event study": "No", "Status": "unvalidated", "Note": "unvalidated: package aggregation disagrees with observed cohort-cell weights" }, { "Outcome / treatment": "y__d", "Estimator": "CS-DID", "Estimate (SDs)": null, "SE": null, "Aggregation": "Equal post-event times", "Event study": "No", "Status": "unidentified", "Note": "UnidentifiedTarget: CS-DID cannot estimate 31 observed post-treatment group-time cells with the configured controls; no target cells were dropped. See csdid-group-time.json and package log." }, { "Outcome / treatment": "y__d", "Estimator": "SDID", "Estimate (SDs)": null, "SE": null, "Aggregation": "Treated cells", "Event study": "No", "Status": "unidentified", "Note": "ValueError: SDID cohort 5 has no observed donor/treated pre-period overlap" }, { "Outcome / treatment": "y__d", "Estimator": "Matrix completion", "Estimate (SDs)": 0.042585638202474595, "SE": null, "Aggregation": "Treated cells", "Event study": "Yes", "Status": "estimated", "Note": "" }, { "Outcome / treatment": "y__d", "Estimator": "Horizontal ridge", "Estimate (SDs)": null, "SE": null, "Aggregation": "Treated cells", "Event study": "No", "Status": "unidentified", "Note": "ValueError: ridge cohort 5, period 5: no donor/pre-period overlap for observed treated outcomes" }, { "Outcome / treatment": "y__d", "Estimator": "Multisynth (cohort-averaged)", "Estimate (SDs)": null, "SE": null, "Aggregation": "Treated cells", "Event study": "No", "Status": "unsupported", "Note": "multisynth-cohort: requires a complete finite outcome matrix after unit-period aggregation; 474 x 25 grid has 7030 missing cells, which are not zero-filled." }, { "Outcome / treatment": "y__d", "Estimator": "Multisynth (individual)", "Estimate (SDs)": null, "SE": null, "Aggregation": "Treated cells", "Event study": "No", "Status": "unsupported", "Note": "multisynth-individual: requires a complete finite outcome matrix after unit-period aggregation; 474 x 25 grid has 7030 missing cells, which are not zero-filled." } ], "adapter_sha256": { "validate_template.py": "9c8cc9bb42c9d4eb4f48bfa0a6ee09498fe83516d08aa9f26a49c21c4686618a", "mtgpax_worker.py": "7279b1b2aaa3f5909cd39ac1964d2e9c75907219011fe661a015511131affa85", "csdid_general.py": "07619c3b3dfee796ae9e4842afdbc6e367a74cece254ede73f7c403dfc924f6d", "runner.py": "3d6f87d28c1a24f0efe21b6d9b06d77866b0d16abcfdaaaef47465cc9cc1078e", "matrix_inputs.py": "3f675276f9022147996dd5b62762721be2dd436d5db171491a286b05628176ae", "csdid_model.py": "b9fd5536d4886e99a707d2b162db5b11da3ff463930eca5b7590d27500096a3f", "multisynth_model.R": "3ae0740c5b75b14f563dacc9961688e8286655866a6b435045a3ef6f453c1604", "csdid_streaming.py": "378198e98c44bd46862601bdcb514b19d9ea4038cddc38dad7fdba9b0aa0216b", "multisynth_sparse.R": "f9ab3d8d7f7b52a79ccd694c7173f626276a0830f23bb79e527c88c0f0292540", "matrix_models.py": "dc7188bf9bf709f0803169dfc3b1198e8b9d2b79d5904ebc5f010a48edde2ee2", "csdid_aggregations.py": "cbed0fb13450d9398f9a59d53dbfdf8e275a74daa01158c38c0f3396a3e8c6ee", "bootstrap_matrix.py": "a55b171313d43a703063c87e668021b75eb3153fa30795b9ae3d9e2721d9b9d0", "eligibility.py": "f0eb686ff3f13e89c8f3d85f49890601c4a7f9a611a2938753be599b8ceac9a6" } } }