Stata

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    Books
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    A Gentle Introduction to Stata (7th Edition) by Alan C. Acock

    This new edition offers a beginner-friendly introduction to Stata, designed for both students and non-technical users. Through straightforward examples and practical exercises, it covers essential topics like data management, statistical analysis, and visualization, empowering readers to use Stata with confidence and ease.

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    Stata Survival Manual (David Pevalin and Karen Robson)

    This book takes a hands-on approach to mastering Stata, focusing on data cleaning, transformation, and statistical analysis. Filled with practical examples from health and social sciences, it’s an excellent resource for students and researchers working with real-world datasets and seeking actionable insights.

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    Data Analysis Using Stata (3rd Edition) by Ulrich Kohler and Frauke Kreuter

    This updated edition is ideal for those aiming to improve their data analysis skills. It provides comprehensive coverage of descriptive statistics, hypothesis testing, regression models, and data visualization, making it a versatile and valuable resource for applied research across disciplines.

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    Microeconometrics Using Stata (Revised Edition) by A. Colin Cameron and Pravin K. Trivedi

    This comprehensive guide to advanced econometrics explores complex topics such as panel data, instrumental variables, and limited dependent variables. Widely regarded by graduate students and professionals in economics and finance, it’s an essential resource for mastering sophisticated analytical techniques.

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    An Introduction to Modern Econometrics Using Stata (Christopher F. Baum)

    This book is an excellent choice for understanding econometric modeling in Stata. It breaks down concepts like time series, multicollinearity, and heteroskedasticity with step-by-step tutorials and detailed command explanations.

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    Using Stata for Quantitative Analysis (3rd Edition) by Kyle C. Longest

    With a focus on practical application, this book walks readers through quantitative research methods using Stata. The updated edition adds new insights on advanced statistical techniques and includes social science examples for better clarity.

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    Practical Statistics: A Handbook for Researchers in Health and Social Sciences (Brodie R. Lawson et al.)

    This handbook combines statistical theory with hands-on Stata tutorials, making it an essential resource for applied researchers. It’s especially useful for those analyzing clinical or survey data with limited statistical backgrounds.

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    Stata for the Behavioral Sciences (Michael N. Mitchell)

    Designed for behavioral science students and researchers, this book simplifies complex statistical methods like ANOVA and multivariate analysis. It includes annotated output and examples that help readers connect theory with practice.

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    The Workflow of Data Analysis Using Stata (J. Scott Long)

    This hands-on guide emphasizes efficient workflows for managing, cleaning, and analyzing data in Stata. With a focus on reproducibility and detailed documentation, it’s an essential resource for graduate students and research teams aiming to streamline their data processes effectively.

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    Statistics with Stata: Version 18 (Lawrence C. Hamilton)

    This latest edition covers the full range of Stata’s statistical capabilities, from basic to advanced methods. With updated examples and clear instructions, it’s perfect for learners seeking comprehensive knowledge of Stata’s tools.

    Reference Materials

    Data Management Methods

    Command

    Description

    generate

    Creates new variables.

    replace

    Modifies the values of an existing variable.

    drop

    Removes variables or observations.

    keep

    Retains specified variables or observations.

    rename

    Changes a variable's name.

    sort

    Arranges data in ascending order by variable(s).

    merge

    Combines datasets by matching key variables.

    append

    Adds new observations from another dataset.

     

    Statistical Analysis Methods

    Command

    Description

    summarize

    Provides descriptive statistics for variables.

    tabulate

    Creates frequency or cross-tabulation tables.

    regress

    Runs linear regression analysis.

    logit

    Estimates logistic regression models.

    anova

    Performs analysis of variance (ANOVA).

    probit

    Runs probit regression models.

    xtreg

    Runs panel data regression models.

     

    Graphing and Visualization Methods

    Command

    Description

    graph

    Creates customizable plots (bar, scatter, etc.).

    histogram

    Plots histograms for a variable.

    kdensity

    Plots the kernel density estimate.

    twoway

    Combines scatterplots, lines, and other plots.

    boxplot

    Creates box-and-whisker plots for comparison.

     

    Data Reshaping Methods

    Command

    Description

    reshape wide

    Converts data from long format to wide format.

    reshape long

    Converts data from wide format to long format.

    collapse

    Aggregates data by groups, summarizing variables.

    xtset

    Declares panel data structure for time-series.

    tsset

    Prepares data for time-series analysis.

     

    Labeling and Formatting Methods

    Command

    Description

    label variable

    Assigns a descriptive label to a variable.

    label define

    Creates a value label for a categorical variable.

    label values

    Links a value label to a variable.

    encode

    Converts string variables into labeled numeric ones.

    decode

    Converts numeric variables with labels to strings.

     

    Programming and Automation Methods

    Command

    Description

    foreach

    Loops over a list of items to run commands.

    forvalues

    Loops over numeric ranges to apply commands.

    capture

    Suppresses errors from a command if it fails.

    macro define

    Creates macros for automating repetitive tasks.

    do

    Executes commands from a .do script file.

    Example Project
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    Git Integration in Stata: Streamlining Version Control

    Git integration within Stata allows users to run Git commands directly from the Stata command prompt, making it easier to manage project versions without leaving the Stata environment.

    Key Features:

    • Seamless Workflow: You can perform all Git operations like initializing a repository, staging files, committing changes, and pushing to remote repositories directly within Stata using the shell function. This eliminates the need to switch between Stata and external Git tools like Git Bash or Git GUI.
    • Minimal Interface Overhead: Since the commands run in the background, there’s no need for additional windows or notifications. Stata’s command prompt manages everything, keeping the workflow simple and uncluttered.
    • Version Control in Data Analysis: Stata users can take full advantage of Git’s version control capabilities, ensuring that analysis scripts and data transformations are tracked over time, which is crucial for collaborative and reproducible research.

    Use Case: For instance, to initialize a Git repository in your Stata project, simply use

    stata

    shell git init .

    In summary, this integration allows you to handle version control directly from within Stata, providing a smooth, efficient, and consistent workflow for data analysis projects. This is especially useful when managing complex analyses that require frequent updates or collaboration.

    Source https://github.com/mpaulacaldas/git-with-stata

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    How to Use This Repository: A Guide for Efficient Table Creation in Stata

    Overview: This repository provides an organized framework for creating tables in Stata, with an emphasis on streamlined workflows for reproducible research. It includes example code, outputs, and a demo with LaTeX code for generating publication-ready tables.

    Steps

    • Find do-files with example code: These files contain practical examples for creating tables from Stata outputs.
    • Example Outputs: Access outputs of the example code to see how the tables should look after execution.
    • LaTeX Demo: A demo with LaTeX code is included to demonstrate formatting for professional papers.

    Adapting the Code

    • Edit the provided template do-files to experiment with the commands or use your own data.
    • To begin, click on Clone or Download at the top right of the page and save the files to your computer.
    • Modify the file paths in the do-files to match your local environment.
    • Enjoy creating tables without time-consuming manual formatting!

    Creating Nice Tables in Stata without Wasting Time:

    To avoid the hassle of manually copying and formatting tables, this repository focuses on automating table creation within Stata, allowing easy adjustments and exports, especially useful for research papers and reports.

    Source https://github.com/worldbank/stata-tables

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    PLS-SEM Stata Package (v0.3.0)

    Stata Version Required: 14.2+

    Overview

    This Stata package is designed for Structural Equation Modeling (SEM) with Partial Least Squares (PLS-SEM), providing tools for path modeling and estimation of complex relationships between observed and latent variables.

    Installation Steps:

    1. Copy the following files into one of Stata's system directories:
      • ado
      • mlib
      • data
      • help files
    2. The easiest installation method is to copy all files into the PERSONAL directory, which you can find by running the adopath command in Stata.
    3. Notes:

      The provided mlib file was compiled for Stata 15. If you are using Stata 14, make sure to copy the mlib file from the mlib/Stata 14 directory included in the package.

      This package streamlines the process of conducting PLS-SEM in Stata, making it easier to apply advanced statistical techniques to your data analysis.

      Source https://github.com/guhjy/plssem

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    MarkDoc: General-Purpose Literate Programming for Stata

    MarkDoc is a powerful, easy-to-use literate programming package for Stata that allows users to create dynamic documents in various formats, including sthlp, pdf, docx, tex, html, odt, epub, and markdown. It's designed to be beginner-friendly, making it suitable for teaching statistical reporting and code documentation in introductory courses.

    Installation

    1. Install via GitHub: github install haghish/markdoc
    2. Dependencies
      • Pandoc
      • wkhtmltopdf
      • pdfLaTeX (optional, required for PDF slides and LaTeX typesetting)

    Source https://github.com/guhjy/markdoc

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    kaksongen: Stata Package for Taxation Impact Decomposition

    kaksongen is a Stata package that computes the decomposition of the impact of taxation on social welfare, based on the generalization of Kakwani and Son (2021) by Ledić, Rubil, and Urban (2022).

    Installation

    To install, run the following command in Stata:
    net install kaksongen, replace from(https://raw.githubusercontent.com/irubil/kaksongen/main)

    Latest Version

    • kaksongen.ado: July 2022
    • kaksongen.sthlp: December 2022

    Source https://github.com/irubil/kaksongen

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    Stata-TeX: Custom LaTeX Tables from Stata

    Stata-TeX enables the creation of highly customizable LaTeX tables for complex regression results. It separates LaTeX table templates from data, allowing for automatic population of placeholders with estimation results.

    Advantages

    • Full customization of Stata/LaTeX tables
    • Easy iteration on LaTeX table formatting without regenerating estimates
    • Cleaner Stata code
    • Ability to modify tables without returning to Stata
    • Reuse estimates across multiple tables

    Limitations

    • Modifying columns requires editing the LaTeX template directly.
    • Placeholders must be unique within the template

    Installation:

    Ensure table_from_tpl.py is accessible via the $PYTHONPATH for Python (preferably 2.7).

    Source https://github.com/murattasdemir/stata-tex

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    Scalarsave: Save Numeric Scalars for LaTeX Documents

    Scalarsave is a Stata ado file that helps economists and researchers save numeric scalar values to delimited files, allowing automatic updates of these values in LaTeX documents. This simplifies the process of keeping numbers consistent in research papers, especially when values change over time.

    Usage:

    • Save scalars to a CSV file:

    scss

    scalarsave using "example.csv", scalar(100) id("Example scalar")

    Installation:

    • Move scalarsave.ado and scalarsave.sthlp to a directory in your Stata adopath (e.g., PERSONAL).
    • Use adopath to check the paths and place the files accordingly.

    LaTeX Integration:

    • Use with the LaTeX datatools and filecontents packages to programmatically insert scalar values into your documents.
    • Example: If scalars.csv contains:

    yaml

    observations, 100

    mean_income, 2000

    In LaTeX, use:

    kotlin

    There are \var{observations} observations in my data and the mean income is \var{mean_income} dollars.

    This will display the numbers dynamically based on the values in the CSV file.

    Testing:

    • The ado includes tests. Make sure to run test.do after making changes to confirm functionality.

    This tool automates updates for numbers in research papers, making document management much more efficient.

    Source https://github.com/onoratod/scalarsave

    ...
    Data Analysis with Stata: Estimating Cobb-Douglas Production Function

    Objective:

    Estimate the Cobb-Douglas production function for formal firms using the Annual Survey of Industries (2016-2017) data.

    Data Set:

    • Source: Annual Survey of Industries 2016-2017
    • Variables: Capital, Labour, Output, Research and Development expenses

    Data Preparation:

    • Merging: Data from different blocks of the survey were merged using the Schedule Dispatch Number (DSL No) as the primary key.
    • Data Reshaping: Some variables were reshaped from long to wide format to avoid duplicates.
    • Variable Selection:
      • Capital (C_178): Fixed capital
      • Labour (E159): Employees
      • Output (J1712): Gross output
      • Control Variable (logRD): Research and Development expenses

    Inference:

    • Interpretation of Coefficients:
      • For a unit increase in logCapital, logOutput increases by 0.407419.
      • For a unit increase in logLabour, logOutput increases by 0.6425619.
      • For a unit increase in logRD, logOutput increases by 0.0407316.
    • Statistical Significance:
      • Since the p-value (0.0000) is less than the alpha value of 0.05, we conclude that capital, labour, and research & development expenditures significantly affect the output of firms.

    This analysis demonstrates how changes in capital, labour, and R&D investment contribute to the overall output of a firm, supporting the relevance of these factors in the Cobb-Douglas production function.

    Source https://github.com/alliajagbe/stata-data-analysis

    Expert Answers

    Stata is used for data management, statistical analysis, and producing graphics, often utilized in fields like economics, sociology, and public health.

    Check if the variable name exceeds 32 characters or contains invalid characters. Use describe to verify all variables in your dataset.

    Yes, use the if qualifier, e.g., regress depvar indepvar if age > 30 to perform commands only for certain observations.

    Ensure the file is in your working directory or specify the full file path. Check for typos in the filename or extension.

    Macros are placeholders for text or values, helping to automate repetitive tasks. Use global or local to define them, e.g., local myvar age.

    Use descriptive labels for variables and values. For example, label variable income "Annual Income" makes the dataset easier to understand.

    Check your variable's scale or missing values. Use graph display to redraw graphs or adjust options like axis limits.

    Yes, but performance depends on your computer’s RAM. Use commands like compress to reduce memory usage for large datasets.

    Install and use the vif command after regression. Variables with a Variance Inflation Factor (VIF) above 10 may indicate multicollinearity.

    A do-file allows you to save and execute a series of commands, ensuring reproducibility, while the command line is used for quick, one-off commands.