What are the advantages and disadvantages of using vector autoregression for forecasting?

1 What is VAR?

VAR is a system of equations that expresses each variable as a linear function of its own past values and the past values of other variables. For example, if you want to forecast the inflation rate and the unemployment rate, you can use a VAR model that includes both variables and their lags. The coefficients of the VAR model can be estimated using ordinary least squares or maximum likelihood methods.

2 Why use VAR for forecasting?

One of the main advantages of using VAR for forecasting is that it can capture the dynamic interactions and feedback effects among multiple variables. For instance, if you want to forecast the impact of a monetary policy shock on the economy, you can use a VAR model to trace how the shock affects interest rates, output, inflation, and unemployment over time. Another advantage of VAR is that it does not impose any restrictive assumptions on the structure or the distribution of the data. You can use VAR for forecasting any time series variables that are stationary or cointegrated.

3 What are the challenges of using VAR for forecasting?

One of the main disadvantages of using VAR for forecasting is that it requires a large amount of data and a careful selection of the lag length. If you have too few observations or too many lags, you may overfit the model and produce inaccurate forecasts. If you have too many variables or too few lags, you may omit important information and produce biased forecasts. Another challenge of using VAR for forecasting is that it may not account for structural changes or nonlinearities in the data. You may need to test for stability, heteroscedasticity, and nonlinearity, and use alternative methods such as structural VAR, threshold VAR, or vector error correction models.

5 What are some examples of using VAR for forecasting?

VAR is employed in many areas, such as economics, finance, marketing, and ecology, for forecasting. For instance, a VAR model with exogenous variables or dummy variables can be used to forecast macroeconomic variables and policy responses in different scenarios. Similarly, a VAR model with GARCH errors or a multivariate GARCH model can be used to forecast stock market returns and volatility. Additionally, a VAR model with seasonal dummies or a vector autoregressive moving average model can be utilized to forecast sales and advertising effects. Lastly, a VAR model with spatial lags or a spatial vector autoregressive model can be employed to forecast population dynamics and environmental factors.

6 What are some resources for learning more about VAR for forecasting?

If you’re interested in learning more about VAR for forecasting, there are various resources available. Enders (2015) has a textbook on time series analysis and forecasting that covers the theory and applications of VAR models in detail. Pfaff (2008) has a tutorial on how to implement VAR models in R with examples and codes for estimating, testing, and forecasting. Tsay (2014) has a course on econometrics and forecasting that introduces the concepts and methods of VAR models as well as other advanced topics. Lastly, Lütkepohl (2020) has a website on VAR models with lectures, slides, exercises, and data sets.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?