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  "Title": "Event Study Analysis",
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  "Description": "Financial event study analysis. Implements the classical\nevent study methodology of MacKinlay (1997)\n<https://www.jstor.org/stable/2729691> with multiple return\nmodels: market model, market adjusted, comparison period mean\nadjusted, Fama and French three-factor (1993)\n<doi:10.1016/0304-405X(93)90023-5> and five-factor (2015)\n<doi:10.1016/j.jfineco.2014.10.010>, Carhart (1997) four-factor\n<doi:10.1111/j.1540-6261.1997.tb03808.x>, generalized\nautoregressive conditional heteroskedasticity (GARCH;\nBollerslev, 1986) <doi:10.1016/0304-4076(86)90063-1>, rolling\nwindow, and dynamic conditional correlation GARCH (DCC-GARCH;\nEngle, 2002) <doi:10.1198/073500102288618487>. Provides\nparametric and non-parametric test statistics, including the\nstandardized cross-sectional test of Boehmer, Musumeci and\nPoulsen (1991) <doi:10.1016/0304-405X(91)90032-F> and its\ncross-correlation adjustment by Kolari and Pynnonen (2010)\n<doi:10.1093/rfs/hhq072>; buy-and-hold abnormal returns (Barber\nand Lyon, 1997) <doi:10.1016/S0304-405X(96)00890-2>; volume and\nvolatility event studies; cross-sectional regression; intraday\nevent studies; panel difference-in-differences event studies\nwith the estimators of Callaway and Sant'Anna (2021)\n<doi:10.1016/j.jeconom.2020.12.001>, de Chaisemartin and\nD'Haultfoeuille (2020) <doi:10.1257/aer.20181169>, and\nBorusyak, Jaravel and Spiess (2024)\n<doi:10.1093/restud/rdae007>; synthetic control methods\n(Abadie, Diamond and Hainmueller, 2010)\n<doi:10.1198/jasa.2009.ap08746>; wild bootstrap inference;\nmultiple testing corrections; power analysis via simulation;\ndata download helpers; automated reporting; and visualization\ntools. Results can be exported to comma-separated values (CSV),\n'Excel', or 'LaTeX'.",
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    "tidy.EventStudyTask",
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      "fields": [],
      "table": false,
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      "title": "Carhart Four-Factor Model",
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      "title": "Degenerate-Input Contract for EventStudy Models",
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      "title": "Download Risk-Free Rate",
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      "title": "Download Stock Data",
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      "title": "EventStudy Colour Palette",
      "concept": [
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      "title": "Harvest Diagnostics from a Fitted EventStudyTask",
      "concept": [
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      "title": "Access the EventStudy Grounding Knowledge Base",
      "concept": [
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      "title": "One-Call Event Study Report Orchestrator",
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      "title": "Estimate Panel Event Study",
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      "title": "Estimate Synthetic Control",
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      "title": "Export Event Study Results",
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      "title": "Fama-French Three-Factor Model",
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      "title": "Format Cross-Sectional Regression Results",
      "topics": [
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    },
    {
      "page": "format.es_diagnostics",
      "title": "Format method for es_diagnostics objects",
      "topics": [
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      "page": "format.es_simulation",
      "title": "Format method for es_simulation objects",
      "topics": [
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    },
    {
      "page": "format.EventStudySummary",
      "title": "Format method for EventStudySummary",
      "topics": [
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    },
    {
      "page": "GARCHModel",
      "title": "GARCH Model",
      "concept": [
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      "topics": [
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    },
    {
      "page": "GeneralizedSignTest",
      "title": "Generalized Sign Test (Cowan 1992)",
      "concept": [
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      "topics": [
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    },
    {
      "page": "generate_report",
      "title": "Generate Event Study Report (Multi-Format)",
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      "topics": [
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    },
    {
      "page": "IntradayEventStudyTask",
      "title": "Intraday Event Study Task",
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      "topics": [
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    },
    {
      "page": "KolariPynnonenTest",
      "title": "Kolari-Pynnoenen Adjusted BMP Test",
      "concept": [
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      "topics": [
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    },
    {
      "page": "LinearFactorModel",
      "title": "Linear Factor Model Base",
      "concept": [
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      "topics": [
        "LinearFactorModel"
      ]
    },
    {
      "page": "LogReturn",
      "title": "R6 class for log return calculation",
      "concept": [
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      ],
      "topics": [
        "LogReturn"
      ]
    },
    {
      "page": "MarketAdjustedModel",
      "title": "Market Adjusted Model",
      "concept": [
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      ],
      "topics": [
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      ]
    },
    {
      "page": "MarketModel",
      "title": "Market Model",
      "concept": [
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      "topics": [
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      ]
    },
    {
      "page": "model_diagnostics",
      "title": "Model Diagnostics for Event Study",
      "concept": [
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      "topics": [
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    },
    {
      "page": "ModelBase",
      "title": "ModelBase",
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      "topics": [
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    },
    {
      "page": "MultiEventStatisticsSet",
      "title": "Multi Event Statistic Set",
      "concept": [
        "eventstudy-statistics"
      ],
      "topics": [
        "MultiEventStatisticsSet"
      ]
    },
    {
      "page": "nonparametric_intraday_test",
      "title": "Non-Parametric Intraday Event Study Test",
      "concept": [
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      "topics": [
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    },
    {
      "page": "OpenAICompatProvider",
      "title": "OpenAICompatProvider",
      "concept": [
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      ],
      "topics": [
        "OpenAICompatProvider"
      ]
    },
    {
      "page": "PanelEventStudyTask",
      "title": "Panel Event Study Task",
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    },
    {
      "page": "ParameterSet",
      "title": "Event Study Parameter Set",
      "concept": [
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      "topics": [
        "ParameterSet"
      ]
    },
    {
      "page": "PatellZTest",
      "title": "Patell or Standardized Residual Test (PatellZTest)",
      "concept": [
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      ],
      "topics": [
        "PatellZTest"
      ]
    },
    {
      "page": "plot_car_distribution",
      "title": "Plot CAR Distribution",
      "concept": [
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      "topics": [
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      ]
    },
    {
      "page": "plot_diagnostics",
      "title": "Plot Model Diagnostics",
      "concept": [
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    },
    {
      "page": "plot_event_study",
      "title": "Plot Event Study Results",
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      "topics": [
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    },
    {
      "page": "plot_panel_event_study",
      "title": "Plot Panel Event Study Results",
      "concept": [
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      "topics": [
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    },
    {
      "page": "plot_stocks",
      "title": "Plot Stocks",
      "concept": [
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      "topics": [
        "plot_stocks"
      ]
    },
    {
      "page": "plot_synthetic_control",
      "title": "Plot Synthetic Control Results",
      "concept": [
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      "topics": [
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    },
    {
      "page": "prepare_event_study",
      "title": "Prepare data for an Event Study",
      "concept": [
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      "topics": [
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    },
    {
      "page": "prepare_intraday_event_study",
      "title": "Prepare Intraday Event Study",
      "concept": [
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      "topics": [
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    },
    {
      "page": "pretrend_test",
      "title": "Pre-trend Test for Event Study",
      "concept": [
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    },
    {
      "page": "print.Advice",
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    {
      "page": "print.es_advice",
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      "topics": [
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    },
    {
      "page": "print.es_cross_sectional",
      "title": "Print Cross-Sectional Regression Results",
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    {
      "page": "print.es_diagnostics",
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      "page": "print.EventStudySummary",
      "title": "Print method for EventStudySummary",
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      "page": "provider",
      "title": "Construct a Grounded AI Advisor provider",
      "concept": [
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      "topics": [
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    },
    {
      "page": "ProviderBase",
      "title": "ProviderBase",
      "concept": [
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      "topics": [
        "ProviderBase"
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    },
    {
      "page": "RankTest",
      "title": "Rank Test (Corrado 1989)",
      "concept": [
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      ],
      "topics": [
        "RankTest"
      ]
    },
    {
      "page": "recommend_stat",
      "title": "Recommend Test Statistics via Offline KB Matching",
      "concept": [
        "eventstudy-advisor"
      ],
      "topics": [
        "recommend_stat",
        "recommend_stat.default",
        "recommend_stat.es_diagnostics",
        "recommend_stat.EventStudyTask"
      ]
    },
    {
      "page": "report_table",
      "title": "Render a data frame as a styled table (HTML / PDF / Word / Markdown)",
      "topics": [
        "report_table"
      ]
    },
    {
      "page": "ReturnCalculation",
      "title": "ReturnCalculation",
      "concept": [
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      ],
      "topics": [
        "ReturnCalculation"
      ]
    },
    {
      "page": "RollingWindowModel",
      "title": "Rolling Window Model",
      "concept": [
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      "topics": [
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    },
    {
      "page": "run_event_study",
      "title": "Run a Complete Event Study",
      "concept": [
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      "topics": [
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    },
    {
      "page": "sc_placebo_test",
      "title": "Placebo Test for Synthetic Control",
      "concept": [
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      "topics": [
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    {
      "page": "SignTest",
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    {
      "page": "SimpleReturn",
      "title": "R6 class for simple return calculation",
      "concept": [
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      "topics": [
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    },
    {
      "page": "simulate_event_study",
      "title": "Simulate Event Study for Power Analysis",
      "concept": [
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      ],
      "topics": [
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    },
    {
      "page": "SingleEventStatisticsSet",
      "title": "Single Event Statistic Set",
      "concept": [
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      ],
      "topics": [
        "SingleEventStatisticsSet"
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    },
    {
      "page": "StatisticsSetBase",
      "title": "Adds a test statistic to this container.",
      "concept": [
        "eventstudy-statistics"
      ],
      "topics": [
        "StatisticsSetBase"
      ]
    },
    {
      "page": "SyntheticControlTask",
      "title": "Synthetic Control Task",
      "concept": [
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      "topics": [
        "SyntheticControlTask"
      ]
    },
    {
      "page": "TestStatisticBase",
      "title": "TestStatisticBase",
      "concept": [
        "eventstudy-statistics"
      ],
      "topics": [
        "TestStatisticBase"
      ]
    },
    {
      "page": "theme_eventstudy",
      "title": "EventStudy ggplot2 Theme",
      "concept": [
        "eventstudy-plots"
      ],
      "topics": [
        "theme_eventstudy"
      ]
    },
    {
      "page": "tidy.EventStudyTask",
      "title": "Tidy Event Study Results",
      "concept": [
        "eventstudy-export"
      ],
      "topics": [
        "tidy.EventStudyTask"
      ]
    },
    {
      "page": "validate_task",
      "title": "Validate an Event Study experiment",
      "concept": [
        "eventstudy-statistics"
      ],
      "topics": [
        "validate_task"
      ]
    },
    {
      "page": "VolatilityModel",
      "title": "Volatility Event Study Model",
      "concept": [
        "eventstudy-models"
      ],
      "topics": [
        "VolatilityModel"
      ]
    },
    {
      "page": "VolumeModel",
      "title": "Volume Event Study Model",
      "concept": [
        "eventstudy-models"
      ],
      "topics": [
        "VolumeModel"
      ]
    }
  ],
  "_pkglogo": "https://github.com/cran/EventStudy/raw/HEAD/man/figures/logo.png",
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  "_vignettes": [
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      "source": "automated-reports.Rmd",
      "filename": "automated-reports.html",
      "title": "Automated Event Study Reports",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Requirements",
        "Basic Usage",
        "Customizing the Report",
        "Title and Author",
        "Selecting Sections",
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        "Including Cross-Sectional Analysis",
        "Output Formats",
        "Panel Event Study Reports",
        "Programmatic Report Generation"
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      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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      "source": "custom-models.Rmd",
      "filename": "custom-models.html",
      "title": "Creating Custom Models",
      "author": "Simon Mueller",
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      "headings": [
        "Introduction",
        "The ModelBase Interface",
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        "Using the Custom Model",
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      "created": "2026-10-21 10:19:03",
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      "filename": "custom-test-statistics.html",
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      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "The TestStatisticBase Interface",
        "Example: Wilcoxon Signed-Rank Test (Single Event)",
        "Example: Kolmogorov-Smirnov Test (Multi-Event)",
        "Registering Custom Statistics",
        "Using Custom Statistics in the Pipeline",
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        "Key Points"
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      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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    {
      "source": "cross-sectional-analysis.Rmd",
      "filename": "cross-sectional-analysis.html",
      "title": "Cross-Sectional Analysis of Event Study Results",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Running the Event Study First",
        "Cross-Sectional Regression",
        "Basic Usage",
        "Robust Standard Errors",
        "Custom CAR Window",
        "Accessing the Underlying Data",
        "Group Comparisons",
        "car_by_group()",
        "Custom CAR Window for Groups",
        "CAR Quantiles",
        "Visualizing the CAR Distribution",
        "Histogram",
        "Histogram by Group",
        "A Complete Cross-Sectional Workflow",
        "Tips and Best Practices",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
      "commits": 1
    },
    {
      "source": "diagnostics-validation.Rmd",
      "filename": "diagnostics-validation.html",
      "title": "Diagnostics and Validation for Event Studies",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Setup: Running an Event Study",
        "Step 1: Validate the Task",
        "What It Checks",
        "1. Event Date Exists",
        "2. Sufficient Estimation Window",
        "3. Time Series Gaps",
        "4. Window Overlap",
        "5. Thin Trading",
        "Using Validation Results",
        "Step 2: Model Diagnostics",
        "Diagnostic Tests Explained",
        "Shapiro-Wilk Normality Test (shapiro_p)",
        "Durbin-Watson Statistic (dw_stat)",
        "Ljung-Box Test (ljung_box_p)",
        "First-Order Autocorrelation (acf1)",
        "Sigma and R-squared (sigma, r2)",
        "Diagnostics for a Single Event",
        "Interpreting Diagnostics as a Table",
        "Step 3: Visual Diagnostics",
        "Comparing Diagnostics Across Events",
        "Step 4: Pre-trend Testing",
        "Interpreting Results",
        "Pre-trend Test by Group",
        "What to Do When Pre-trends Are Detected",
        "A Complete Diagnostic Workflow",
        "Decision Guide",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
      "commits": 1
    },
    {
      "source": "data-download.Rmd",
      "filename": "data-download.html",
      "title": "Downloading Stock and Factor Data",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Downloading Stock Data",
        "Multiple Stocks",
        "Format for EventStudyTask",
        "Raw Format",
        "Downloading Factor Data",
        "Fama-French 3-Factor",
        "Fama-French 5-Factor",
        "Monthly Frequency",
        "Using with Factor Models",
        "Downloading Risk-Free Rate",
        "Column Name Mapping"
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      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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      "source": "factor-models-bhar.Rmd",
      "filename": "factor-models-bhar.html",
      "title": "Factor Models, GARCH, and Buy-and-Hold Abnormal Returns",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Factor Data: The factor_tbl",
        "Required Columns by Model",
        "Sourcing Factor Data",
        "Fama-French Three-Factor Model",
        "Running the Study",
        "Inspecting Factor Loadings",
        "Fama-French Five-Factor Model",
        "Carhart Four-Factor Model",
        "GARCH(1,1) Model",
        "Requirements",
        "GARCH-Specific Results",
        "When to Use GARCH",
        "Buy-and-Hold Abnormal Returns (BHAR)",
        "Motivation",
        "Running a BHAR Study",
        "Interpreting BHAR Results",
        "BHAR vs. CAR: When Does It Matter?",
        "Comparing Models",
        "Summary",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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      "source": "inference-robustness.Rmd",
      "filename": "inference-robustness.html",
      "title": "Inference and Robustness: HAC, Bootstrap, and Multiple Testing",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "HAC Standard Errors",
        "Kolari-Pynnonen Test",
        "Wild Bootstrap Inference",
        "Multiple Testing Corrections",
        "Combining Approaches",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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    },
    {
      "source": "intraday-event-study.Rmd",
      "filename": "intraday-event-study.html",
      "title": "Intraday Event Studies with EventStudy",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Data Structure",
        "The IntradayEventStudyTask",
        "Non-Parametric Test",
        "Interpreting Results",
        "Plotting Results",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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    {
      "source": "introduction.Rmd",
      "filename": "introduction.html",
      "title": "Getting Started with EventStudy",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Quick Start",
        "Example: Dieselgate",
        "Initialization",
        "Define the Event Study",
        "Execute the Event Study",
        "Visualize the Results",
        "Cumulative Abnormal Return (CAR)",
        "Cumulative Average Abnormal Return (CAAR)",
        "Next Steps",
        "Conclusion"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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      "source": "modern-did-estimators.Rmd",
      "filename": "modern-did-estimators.html",
      "title": "Modern Difference-in-Differences Estimators",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Data Setup",
        "TWFE (Default)",
        "Sun-Abraham",
        "Callaway-Sant'Anna",
        "de Chaisemartin-D'Haultfoeuille",
        "Borusyak-Jaravel-Spiess",
        "Comparing Estimators",
        "When to Use Which?",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
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    {
      "source": "panel-event-study.Rmd",
      "filename": "panel-event-study.html",
      "title": "Panel Event Studies: A Guide Following Miller (2023)",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Data Structure (Miller Section I)",
        "Dataset A: Single-Cohort Design",
        "Dataset B: Staggered Multi-Cohort Design",
        "Creating Tasks",
        "The Estimating Equation (Miller Section II)",
        "Static TWFE: The Single-Coefficient Model (Miller Section II.A)",
        "Dynamic TWFE: Event-Time Coefficients (Miller Section II.B)",
        "Reference Period and Normalization (Miller Section III)",
        "Endpoint Binning (Miller Section IV)",
        "Staggered Treatment and Heterogeneous Effects (Miller Section V)",
        "The TWFE Problem",
        "Sun & Abraham (2021) Interaction-Weighted Estimator",
        "Comparing TWFE and Sun & Abraham",
        "Pre-trend Testing (Miller Section VI)",
        "Visual Inspection",
        "Joint Test for Pre-trends",
        "Miller's Caution",
        "Reporting and Counterfactuals (Miller Section VII)",
        "Statistical Inference (Miller Section VIII)",
        "Clustered Standard Errors",
        "How Many Clusters Are Enough?",
        "Practical Checklist",
        "References"
      ],
      "created": "2026-10-21 10:19:03",
      "modified": "2026-10-21 10:19:03",
      "commits": 1
    },
    {
      "source": "result-extraction.Rmd",
      "filename": "result-extraction.html",
      "title": "Result Extraction and Export",
      "author": "Simon Mueller",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Running the Example",
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