A self-guided tour to help you find and analyze data using Stata, R, Excel and SPSS. The goal is to provide basic learning tools for classes, research and/or professional development

- Introduction to Econometrics byCall Number: HB139 .S765 2019ISBN: 9780134461991Publication Date: 2018-11-06Introduction to Econometrics, Fourth Edition, by James H. Stock and Mark W. Watson, provides an outstanding introduction to econometrics. Using an ingenious set of real-world questions and answers, they produced an excellent introduction to estimation, inference, and interpretation in econometrics.

The text also provides an excellent introduction to causal inference and explains the role of regression as a tool for it.

The fourth edition adds some key concepts and methods used in big-data analysis and machine learning. - Econometric Analysis byCall Number: HB139 .G74 2018ISBN: 9780134461366Publication Date: 2018Econometric Analysis has been the leading textbook for graduate econometrics in social science programs worldwide since 1990. It is also a major reference work for empirical research. Econometric Analysis ranked 34th with over 48,000 citations in Google Scholar's October 2014 Nature Journal list of the world's 100 all time most cited works. In 2022, with over 90,000 citations, Econometric Analysis is the most cited work ever written by an economist.
- Introductory Econometrics byCall Number: (Stokes) HB139 .W665 2018ISBN: 9781337558860Publication Date: 2019-01-04Unlike traditional texts, this book's practical yet professional approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines. The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called "causal effects" or "treatment effects" to provide a complete understanding of the impact and importance of econometrics today.
- Econometric analysis of cross section and panel data byCall Number: HB139 .W663 2010ISBN: 9780262232586Publication Date: 2010
- Mostly Harmless Econometrics byCall Number: OnlineISBN: 9780691120348Publication Date: 2009-01-04From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometrics The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications
- Mastering "metrics" : the path from cause to effect byCall Number: HB139 .A53984 2015ISBN: 9780691152837Publication Date: 2015
- Causal Inference : The Mixtape byCall Number: OnlineISBN: 9780300255881Publication Date: 2021An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences.

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. The book introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages. - Quantitative Social Science: An Introduction in Stata byCall Number: H62 .I5365 2021ISBN: 9780691191089Publication Date: 2021The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fields
- Microeconometrics using Stata byCall Number: HB139 .C36 2022ISBN: 9781597183598Publication Date: 2022
- R for Data Science byPublication Date: 2023
- An Introduction to Statistical and Data Sciences Using R byPublication Date: 2024This book is good for people who are completely new to coding and interested in learning the basics of R programming.
- Data analysis using regression and multilevel / hierarchical models byCall Number: OnlineISBN: 052168689XPublication Date: 2006
- Unifying political methodology : the likelihood theory of statistical inference byCall Number: OnlineISBN: 9780472022519Publication Date: 1998
- Econometric analysis of panel data byCall Number: HB139 .B35 2021ISBN: 9783030539528Publication Date: 2021
- Longitudinal and panel data : analysis and applications in the social sciences byCall Number: OnlineISBN: 0521535387Publication Date: 2004
- Statistical analysis : an interdisciplinary introduction to univariate & multivariate methods byCall Number:

QA278 .K323 1986ISBN: 0942154991 - Microeconometrics : methods and applications byCall Number: HB172 .C343 2005ISBN: 0521848059Publication Date: 2005
- Bayesian Data Analysis byCall Number: QA279.5 .G45 2013ISBN: 158488388XPublication Date: 2013This book is a comprehensive guide to Bayesian analysis. The third edition includes R code for many of the examples, making it a valuable resource for learning Bayesian methods with R.

- UCLA Data Analysis GuidesThe link contains examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages, including Stata.
- Useful Stata FAQThe page contains links for frequently asked questions in Stata.
- STATALIST: The Stata ForumThis is a Stata user forum. In the search bar, you can look for the answer related to your Stata coding problem. After registering/logging in, you can also ask questions to the user.
- German Rodriguez’s Stata tutorialGerman Rodriguez’s Stata tutorial is a great place to get started learning Stata.
- Stata PDF flyersThe link contains Stata flyers that provide Stata codes for different types of statistical analysis. These flyers are similar to R cheat sheets.
- Internet Guide to StataThis page provides short tutorials for learning Stata that are especially convenient for newbies (even though not for absolute beginners).
- Annotated Output: Stata"These pages contain example programs and output with footnotes explaining the meaning of the output. This is to help you more effectively read the output that you obtain and be able to give accurate interpretations."
- Stata cheat sheetsNeed a little help with some Stata basics? Look no further than these excellent cheat sheets by data practitioners Dr. Tim Essam and Dr. Laura Hughes. These compact yet well-organized sheets cover everything you need, from syntax and data processing to plotting and programming, making them handy references to download for quick use.
- Stata module to access World Bank databases"wbopendata" package allows Stata users to download over 3,000 series of indicators from the World Bank databases.
- Descriptive Statistics in StataDiscusses techniques to explore data using Stata. To explore data, we usually need to know about the format of the variables, summary statistics, crosstab, frequency, etc.
- Differences-in-differences using StataBasic differences-in-differences estimation (Stata)
- Fuzzy Merge in Stata: Matching Fuzzy Text/String using StataThis tutorial provides a step-by-step guide to conduct fuzzy matching using Stata
- outreg2 to make publication-quality tables in StataUsing outreg2 to report regression outputs, summary statistics, and basic cross-tabulations
- Propensit score matching in Stata using --teffects--".....Stata 13 introduced a new teffects command for estimating treatments effects in a variety of ways, including propensity score matching....."
- Resources to help you learn and use Stata (UCLA)Data analysis using Stata
- Getting started with StataStata tutorial to get started in data analysis
- Survival Analysis with StataThis module provides an introduction to survival analysis using Stata.

The focus of the Lessons is on models for single-spell survival time data with no left censoring or left truncation. - Testing for panel-level heteroskedasticity and autocorrelationCorrecting for heteroskedasticity in panel data analysis
- Introduction to Time Series Using StataDate variables, Granger causality, cointegration test, QLR or sup-Wald test to detect unknown breaks, serial correlation, white-noise, Chow test, cross-correlation and more...
- Stata Tutorials by the Data HallThe page provides step-by-step Stata tutorials with code examples and practical examples
- Introduction to Time Series AnalysisThis link contains resources to conduct time series analysis using Stata

- Durbin-Watson Significance Tables"The [DW] test statistic tests the null hypothesis that the residuals from an [OLS] regression are not Autocorrelated against the alternative that the residuals follow an AR1 process"
- FAQ: Chow and Wald tests"...the short answer is that you estimate your model using regress, vce(robust) and then use Stata’s test command. You then call the result a Wald test. "
- FAQ: Chow tests"A Chow test is simply a test of whether the coefficients estimated over one group of the data are equal to the coefficients estimated over another..."
- FAQ: Do-it-yourself R-squared"Users often request an R-squared value when a regression-like command in Stata appears not to supply one"
- FAQ: Relationship between the chi-squared and F distributions"F and chi-squared statistics are really the same thing in that, after a normalization, chi-squared is the limiting distribution of the F as the denominator degrees of freedom goes to infinity."
- FAQ: Saving frequencies produced by tabulate"Is there any direct way to save into a new variable the frequencies obtained by applying the command tabulate?"
- FAQ: Working with tmap and mapsMaps in Stata
- GEIVARS: Stata module to calculate Generalized Entropy inequality indices"geivars estimates several inequality indices commonly used by economists, together with their asymptotic sampling variances."
- Granger causality (Stata)"A variable X Granger-causes Y if Y can be better predicted using the histories of both X and Y than it can using the history of Y alone."
- Linear Regression using StataBasic introduction to linear regression analysis, diagnostics and presentation (using Stata)
- Logit, Ordered Logit, and Multinomial Logit in Stata: A Hands-on TutorialAn introductory guide to estimate logit, ordered logit, and multinomial logit models using Stata
- Propensity score matching/StataPropensity score matching
- Random Coefficient Models for Longitudinal Data (Stata)Examples using Stata, SPSS, SAS and R
- Stata Tutorial - Choosing the Correct Weight Syntax"One of the most common mistakes made when analyzing data from sample surveys is specifying an incorrect type of weight for the sampling weights. Only one of the four weight keywords provided by Stata, pweight, is correct to use for sampling weights."
- Unit Roots and Cointegration"This issue focuses on time series models, with special emphasis on the tests of unit roots and cointegration. I am providing instructions for both R and STATA. I would like to remark that the theoretical background given in class is essential to proceed.
- Which Stata is right for me?Comparison across Stata versions.
- Missing Data: Multiple Imputation in StataThis guide discusses multiple imputation techniques for missing data using Stata.

- Data Visualization in Stata: Generating Basic Graphs/FiguresThis tutorial provides instructions to generate basic graphs/figures using Stata.
- Graph bar: arranging categories in prespecified orderReordering in graph bar
- Margins plots in StataStata makes it easy to graph statistics from fitted models using marginsplot. marginsplot graphs the results from margins, and margins itself can compute functions of fitted values after almost any estimation, linear or nonlinear.
- Stata library: graph examplesBasic graphs in Stata
- Visual overview for creating graphsGraphs in Stata

- Encode string into numeric/numeric into stringStata help for encode/decode
- String to numeric / numeric to stringStata help for destring/tostring

- estout - Making Regression Tables in Stata"estout assembles a regression table from one or more models previously fitted and stored. "
- Publications quality tables in Stata: a tutorial for the tabout program"tabout is a Stata program for producing publication quality tables.1 It is more than just a means of exporting Stata results into spreadsheets, word processors, web browsers or compilers like LATEX. tabout is actually a complete table building program"
- Stata FAQ: How can I use -estout- to make regression tables that look like those in journal articles?"This FAQ illustrates the estout command that makes regression tables in a format that is commonly used in journal articles."
- TABLETUTORIAL: Stata module to provide tutorial on automated table generation and reporting with Stata"-tabletutorial- illustrates how Stata can be used to export statistical results and generate customized reports."

- FAQ: One-sided tests for coefficients"Estimation commands provide a t test or z test for the null hypothesis that a coefficient is equal to zero. The test command can perform Wald tests for simple and composite linear hypotheses on the parameters, but these Wald tests are also limited to tests of equality."

- Dynamic panel data analysis"Linear dynamic panel-data models include p lags of the dependent variable as covariates and contain unobserved panel-level effects, fixed or random." [Source: http://www.stata.com/help.cgi?xtdpd"

- Capabilities: Principal components"Stata’s pca command allows you to estimate parameters of principal-component models."
- Factor AnalysisIntroduction to factor analysis.
- Stata help for pca_postestimation"Postestimation tools for pca and pcamat"

- Maximum likelihood estimation"In addition to providing built-in commands to fit many standard maximum likelihood models, such as logistic, Cox, Poisson, etc., Stata can maximize user-specified likelihood functions."
- Maximum likelihood estimation"To perform maximum likelihood estimation (MLE) in Stata, you must write a short Stata program defining the likelihood function for your problem."

- Estimating the product of observations"prod provides a multiplicative function for egen analogous to the additive sum function. The product of all non-missing observations of meeting optional in and if conditions is returned in for each observation meeting the conditions. would most commonly be an existing variable in the data set. This is version 1.3.4 of the software."
- Using oureg2 with ivreg2Statalist: The Stata Listserver - "RE: st: Using outreg2 in combination with ivreg2?..."

- Mata: Stata’s matrix programming language"As of version 9, Stata contains a full-fledged matrix programming language, Mata, with all of the capabilities of MATLAB, Ox or GAUSS. Mata can be used interactively, or Mata functions can be developed to be called from Stata."