You have four options:
- Simple statistics (i.e. use lagged values to predict future values)
- Complex statistics (e.g. VARs)
- Model the economy and get forecasts from the model
- Use the average from lots of models (e.g. ask the experts and take an average)
Surprisingly, (1) almost always beats (2). If you wanna do (1), it is pretty straight forward to do in Excel’s analysis toolkit.
Some models, like large DSGEs, do better than others, like old-school macroeconometric models. This Fed working paper compares the forecasting methods used by the Fed. It finds that, at least for real variables like GDP, a DSGE model does better than staff forecasts (method 4), does better than an old fashion ad-hoc model and it does better than sophisticated multi-variate statistical methods.
That said the DSGE model doesn’t do much better than simple statistics (method 1). This implies, of course, simple statistical methods forecast better than the Fed staff. In other words, (1) weakly dominates the other three “more sophisticated” forecasting methods. This line from the paper kills me, “[A] comparison to existing methods at the Federal Reserve [i.e. staff forecasts and the macroeconometric models] is more policy relevant than a comparison to AR and VAR forecasts [i.e. the simple and more sophisticated statistical methods, respectively], in part because Federal Reserve forecasts have not placed much weight on projections from these types of models.” Even though they’re no better at forecasting than simple statistical techniques, experts are relied on exclusively.
In defense of DSGE models, though, even if they don’t forecast better than simple statistical models, because they tell an economic story, they’re more policy relevant.