STATA Mastery: Practical Data Analysis & Interpretation

Learn how to make different pie charts in Stata using a sample data set, including married vs non-married, labeling with percent, and combining wage with race, plus exploded pie charts.

Learn to create and customize histograms in Stata for continuous and categorical variables using the built-in BP dataset, including density plots and save options for by-sex comparisons.

Explore how to build bar charts in stata using continuous and categorical variables, including cluster and stacked charts by marital status, race, college graduation, and mean wage.

Multicollinearity arises when independent variables are highly correlated, and it can affect analysis and forecasting. It stems from data collection methods, model specification, and the use of dummy variables.

Identify multicollinearity by examining results: high R square with insignificant coefficients, low t-values and high p-values, contradictory coefficient signs, large standard errors, and unstable changes when adding or removing variables.

Explore how multicollinearity causes unstable regression coefficients and inflated standard errors, lowering t-statistics and statistical power, and producing misleading R-squared that jeopardizes forecasting.

Detect multicollinearity using correlation matrix, VIF, tolerance, and condition index, with 80% thresholds and practical analysis to follow in software.

Learn practical strategies to address multicollinearity, including variable removal, data transformation, composite indices, PCA, ridge regression, and centering to improve model stability.

Learn to detect multicollinearity in Stata through correlation matrices, VIF and tolerance tests, and the condition index, with data imported from Excel and a linear regression example.

Apply the augmented Dickey-Fuller test in Stata to assess stationarity of a time series, importing data from Excel and interpreting 5% critical values and the null and alternative hypotheses.

Apply first difference in Stata to diagnose stationarity using the Augmented Dickey-Fuller test, then verify results in Excel for consistency.

Learn how to convert a non-stationary time series into a stationary one in Stata by differencing, after checking stationarity with line plots and autocorrelation tests.

Master stata techniques to differentiate time series and panel data and convert time series data into panel data using multi-country examples.

Check the stationarity of the panel data by running unit root tests, including the living Q and Hendry M tests, after declaring the data as panel in Stata.

Identify homoscedasticity and heteroscedasticity, explain constant versus nonconstant error variance, and discuss residual spread, implications for ols, and detection tests.

Identify and explain the seven main causes of heteroscedasticity, including changing relationships, outliers, incorrect models, omitted variables, measurement error, time-related variability, and data aggregation, with practical examples.

Explain how heteroscedasticity leaves OLS estimates unbiased and consistent but inefficient due to large, non-constant variance, inflating standard errors and reducing t and F statistics.

Detect heteroscedasticity with a graphical method using OLS residuals and a residuals vs predicted values plot, and with a park test on ln e^2 and ln x at 0.05.

Apply the Breusch–Pagan test in Stata to detect heteroskedasticity in a regression, and interpret the p-value to conclude constant variance (no heteroscedasticity).

Apply remedial measures for heteroscedasticity in regression using known variance with weighted least squares, transformations, and GLS, and improve models by addressing omitted variables.

Apply descriptive statistics in Stata to raw data to reveal mean, median, mode, variance, standard deviation, skewness, kurtosis, and identify outliers.

Learn to run a linear regression model in Stata by importing data from Excel, defining GDP g as the dependent, and interpreting coefficients, p-values, t-values, and r-squared.

master how to run OLS regression in Stata and interpret t values, p values, r-squared, and F statistics to judge significance and model fit.

Learn to run a linear regression, obtain residuals, and create a resid variable. Then use the Shapiro-Wilk test to assess normality, where p-value above 0.05 indicates normal residuals.

Learn to check residuals for constant variance in Stata by importing Excel data, running a regression, generating residuals, and applying a heteroskedasticity test with p-values guiding the decision.

Learn to check residuals for serial correlation in Stata by running a linear regression, generating residuals, and applying Durbin-Watson and Breusch-Godfrey tests with a time series setup.

Apply the Durbin-Watson d-statistic in Stata to detect serial correlation in regression residuals, using an Excel-to-Stata workflow and the 1.5–2.5 no autocorrelation guideline.

Learn to detect and remove serial correlation in time series data with Stata, using Durbin-Watson and Roche Godfrey tests, plus differencing, log transforms, and lagged variables.

Apply the Sk test (skewness and kurtosis) and the Jarque-Bera test in Stata to assess normality, interpreting p-values for GDPG, GOV, and POPD and for regression residuals.

Apply the Johansen cointegration test to detect long-run relationships, verifying first-difference stationarity, and decide between vector error correction model and unrestricted VAR in Stata, with command-based options.

Apply the granger causality test in stata to assess whether gdp and population causally influence each other, interpret p-values, select lags, and report one-way or two-way effects.

Apply the Westerlund co-integration test to panel data to determine if some panels are co-integrated or all panels are co-integrated, and learn to run it in Stata and interpret p-values.

Learn to apply the Pedroni test for cointegration in panel data using Stata, declare panel, specify id and time, choose lag length, and interpret p-values to confirm a long-run relationship.

Apply Kao test for cointegration by using cov test in Stata on panel data, interpret p-values below 0.05 as evidence of long-run relationships, and compare with Westerlund and Pedroni tests.

Explore the first method to determine the lag length for a VAR model using LR, FPE, AIC, HQC, and SBIC, with data imported from Excel to Stata.

learn the second method for lag selection in VAR models with Stata, including importing Excel data, setting year as the time variable, and using AIC to pick the best lag.

Decide between var and vector error correction model using the Johansen cointegration test; if no cointegration, apply var.

Estimate and interpret a VAR in Stata with five lags chosen by lag length criteria, then use Granger causality and diagnostics to assess short-run relationships and confirm no long-run cointegration.

Evaluate a VAR model's diagnostics, including LM tests for residual autocorrelation at lag 1 and 2, and Jarque-Bera normality checks. Confirm that residuals are normally distributed and variance is constant.

Apply the Johansen co-integration test to confirm relationships, then use a vector error correction model (VECM) to interpret long-run cointegration and short-run dynamics among GDP, FDI, and related variables.

Learn to forecast with a VAR model in Stata by preparing yearly data, selecting lag lengths, validating residuals for autocorrelation and normality, and generating forecasts.

This lecture demonstrates applying fixed and random effects in Stata for panel data, running pooled and linear regressions, and using the Hausman test to choose the better model.

Compare fixed and random effects in Stata using Hausman and LM tests, decide between pooled, fixed, or random models, and learn data setup for panel analysis.

Master ARDL in Stata for time series and extract short-run results by selecting lags using FPE, AIC, HQIC, and SBIC, and interpreting GDPG with GOV, POPD, and FDI.