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: 2018-11-06For courses in introductory econometrics. This package includes MyLab Economics. Engaging applications bring the theory and practice of modern econometrics to life Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics. Also available with MyLab Economics By combining trusted author content with digital tools and a flexible platform, MyLab(tm) personalizes the learning experience and improves results for each student. Note: You are purchasing a standalone product; MyLab Economics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Economics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Economics, search for: 0134610989 / 9780134610986 Introduction to Econometrics Plus MyLab Economics with Pearson eText -- Access Card Package, 4/e Package consists of: 0134461991 / 9780134461991 Introduction to Econometrics 0134543939 / 9780134543932 MyLab Economics with Pearson eText -- Access Card -- for Introduction to Econometrics
- Data analysis using regression and multilevel / hierarchical models byCall Number:

HA31.3 .G45 2007ISBN: 052168689X - An R Companion to Applied Regression byCall Number: QA278.2 .F628 2011ISBN: 141297514XPublication Date: 2011
- Unifying political methodology : the likelihood theory of statistical inference byISBN: 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:

HA29 .F6816 2004ISBN: 0521535387 - Statistical analysis : an interdisciplinary introduction to univariate & multivariate methods byCall Number:

QA278 .K323 1986ISBN: 0942154991

- 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."
- Descriptive StatisticsDiscusses 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
- OutregUsing outreg2 to report regression outputs, summary statistics, and basic cross-tabulations
- Propensit score matching 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
- Stata tutorialStata tutorial to get started in data analysis
- Survival Analysis with Stata"Aims of the module: * To provide an introduction to the analysis of spell duration data (‘survival analysis’); and * To show how the methods can be implemented using Stata, a program for statistics, graphics and data management.The foc
- Testing for panel-level heteroskedasticity and autocorrelationCorrecting for heteroskedasticity in panel data analysis
- Time Series and Event StudiesDate variables, Granger causality, cointegration test, QLR or sup-Wald test to detect unknown breaks, serial correlation, white-noise, Chow test, cross-correlation and more...

- 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 Models in Stata: A Hands-on TutorialAn introductory guide to estimate logit, ordered logit, and multinomial logit models using Stata
- Multiple imputation"Stata’s new mi command provides a full suite of multiple-imputation methods for the analysis of incomplete data, data for which some values are missing. mi provides both the imputation and the estimation steps. mi’s estimation step encompasses both estimation on individual datasets and pooling in one easy-to-use procedure."
- 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
- Graph Combine in Stata: a common legendst: RE: one legend for a combined graph
- Margins plots"New in Stata 12 is the marginsplot command, which makes it easy to graph statistics from fitted models. marginsplot graphs the results from margins, and margins itself can compute functions of fitted values after almost any estimation command, linear or nonlinear."
- Stata: IRF graphst: RE: Modifying -irf graph- output
- Stata Library: Graph ExamplesStata graphs
- 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."