Out of Sample Forecast - VAR Model in Stata

Out of Sample Forecast - VAR Model in Stata Learn how to estimate a Vector Autoregression (VAR) model in Stata, apply the Johansen cointegration test, and generate out-of-sample forecasts with confidence bands. This tutorial uses U.S. money supply (M2) and CPI data to show, step by step, how to test for stationarity, estimate the VAR, run impulse response functions (IRFs), perform variance decomposition, and produce professional-quality forecasts in Stata. Vector Autoregression (VAR) in Stata | Out-of-Sample Forecasts with Confidence Bands In this tutorial, I take you step by step through the entire process of estimating a VAR model. We begin by importing and transforming the data into logs, differences, and growth rates, and then check for stationarity using unit root tests such as ADF and DF-GLS. Once the data properties are clear, we apply the Johansen cointegration test to investigate whether a long-run relationship exists between money supply and prices. With this foundation, we move on to estimating the VAR model, carefully selecting the optimal lag length and writing out the formal representation of the system. To identify shocks, we use the Cholesky decomposition, which allows us to interpret the short-run restrictions. The stability of the model is then verified through VAR stability conditions and residual diagnostics, including the LM test for autocorrelation. From there, we examine the predictive structure of the system with Granger causality tests before moving into the dynamic analysis. We generate Impulse Response Functions (IRFs) to trace out the effects of shocks over time and produce a Variance Decomposition to quantify the contribution of money supply and prices to forecast errors. Finally, the tutorial culminates with the creation of out-of-sample forecasts with confidence bands, producing publication-ready graphs that can be used in academic research, policy analysis, or applied forecasting. 📣 Get the complete package (Slides + Dataset + Do File + Graphics Code): 👉 https://jdeconomicstore.com/b/var-sta... 📊 Download the free dataset to replicate this tutorial: 👉https://www.forecastingeconomics.com/... 🎓 This video is part of my FREE STATA Course – see the full outline here: 👉hhttps://www.forecastingeconomics.com/... 🌐 Visit my website for more tutorials and econometric resources: 👉 https://www.forecastingeconomics.com/ 📺 Subscribe for new tutorials: 👉    / @forecastingeconomics   📧 Contact: [email protected] 📲 Follow me: https://juandamico.start.page/ --------------------------------------------------------------------------------------- 🕘 Timestamps 0:00 Introduction 1:28 Tutorial Overview 3:56 Stationarity 6:26 Johansen Cointegration Test 10:15 Estimation of VAR Model 11:48 Formal Representation 13:35 Cholesky Decomposition 16:35 VAR Stability Conditions 17:28 Autocorrelation Test 18:48 Granger Causality Test 20:38 Impulse Response Functions 23:58 Variance Decomposition 26:28 Forecast with Confidence Bands --------------------------------------------------------------------------------------- Juan D’Amico – Economist, Forecasting Economics #Stata #VAR #Econometrics #Forecasting #TimeSeries