Are these things correlated enough that one is useful in forecasting the other? If so, Granger Causality can be established. In time-series econometrics, we can seldom hope to show ‘real’ causality. Then, once causality has been established (or not), policy prescriptions might be suggested, perhaps to encourage (or not) the provision of credit. In that case, the econometrician will try to convince you whether or not access to credit really caused the change in outcome. An econometrician might then come along and test if greater access to credit leads to X, Y, or Z – like college enrollment, or mechanization on small-holder farms, etc. A typical story-line of that type might be something like the following: an unpredictable policy change gave a random subset of people more access to credit. Granger Causality is a different kind of causality than one typically runs into in cross-section econometrics, where you might have some kind of natural experiment. Finally, forecast error variance decomposition (FEVD) estimates how much of your forecast error can be attributed to unpredictability in each variable in the VAR. Impulse response functions show how one variable might react to sudden changes in the other variable. Stated more simply in our context, it tests whether lags of SPY returns are helpful in forecasting GS returns, and vice versa. Granger Causality is most commonly implemented by an F-test on the lags of the other variable on the variables of interest. Instead, in practice the researcher will usually end up looking at the following three things that are derived from the fitted VAR model: Granger Causality, Impulse Response Functions, and Forecast Error Variance Decomposition, that reveal something about the nature of how these markets move together (or not). You can use a VAR for forecasting, like we did with the ARIMA and GARCH models, but as we found with those, the forecasts are usually not precise enough to be all that informative from a practical standpoint. The VAR model is used to determine the relationship among several variables. SPY_t &= \beta^įitting a VAR with two lags to SPY and GS returns yields the following. GSRet <- log(GS$GS.Adjusted) - log(lag(GS$GS.Adjusted))Ī VAR model that estimates the relationship between SPY and GS looks like the following. GetSymbols(c('SPY', 'GS')) # "SPY" "GS" SPYRet <- log(SPY$SPY.Adjusted) - log(lag(SPY$SPY.Adjusted))
#SVAR EVIEWS 10 INSTALL#
# If you are following along, uncomment the next lines and run once to install the required packages Also for simplicity, we will continue as in the first post using SPY (the S&P 500 exchange traded fund) and GS (Goldman Sachs) prices. The next post will cover VECM which estimates how a group of variables move together in equilibrium. We will start with the Vector Auto-regression model, because it is the simpler one. In time-series analysis, there are two basic models typically used to estimate and evaluate the relationships between multiple variables over time. In finance, security prices for companies in a similar sector might be related because of common economic conditions driving profitability (e.g., Bank of America and J.P. In ag econ there are commodities whose prices are inherently related because of substitution or complementary effects in production and/or consumption (e.g., corn and soybeans), or because of production processes (e.g., soybeans, soybean oil, and soybean meal). This approach has natural applications in agricultural economics and finance. In this post I explain how one goes about estimating the relationship among several variables over time.
#SVAR EVIEWS 10 CODE#
If you aren’t into R, just ignore the code blocks and the intuition will follow. As in the first post, the code producing these examples is provided for those who want to follow along in R. As a reminder, this post is intended to be a very applied example of how use certain tests and models in a time-sereis analysis, either to get someone started learning about time-series techniques or to provide a big-picture perspective to someone taking a formal time-series class where the stats are coming fast and furious. Click the link to check out the first post which focused on stationarity versus non-stationarity, and to find a list of other topics covered.
#SVAR EVIEWS 10 SERIES#
This post is the third in a series explaining Basic Time Series Analysis.
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