Yesterday, Matt Cowgill put up an interesting post on the West Australian economy, which appears to be slowing. The ABS doesn’t publish state accounts quarterly, so despite our sincerest desire to know, it’s tough to tell whether a recession—two quarters of negative growth—is going on in any given state.
Cowgill’s solution is to back out an estimate of growth using data that is closely related to growth but released more frequently, like labour-force data. This technique is used widely among applied economists working with time series. For a blog-post, the ‘Okun’s rule-of-thumb’ estimate is probably sufficient to get an idea about the ballpark rate of economic growth in WA. But how would someone with more at stake go about forecasting the figure?
Firstly, I have a small concern with how unemployment maps onto output. If the stories are correct, Western Australian mining is transitioning from an investment activity to a volume activity. Building mines, railways, and pipelines is labour intensive, while operating them is not. Old relationships between growth and unemployment may not be the best way of forecasting future changes.
One solution is to incorporate more high-frequency series into our estimates of WA’s economic growth. I did this using WA’s unemployment, domestic demand, investment, Perth’s CPI, global iron-ore prices, and Australian mining exports. I use a canned forecasting routine from the R package ‘forecast’ to push forward the series to the end of the financial year, then annualised them, stuck them into a basic error correction model, and used the estimates to forecast WA’s year-on-year GSP growth.
Relative to a basic model using just unemployment to describe GSP growth, this model does significantly better, with a root mean squared error of 0.74 per cent, significantly less than the 1.1 per cent that using unemployment alone gives. R squared is about 69 per cent versus 29 per cent for the unemployment model. In all, it’s not too bad for within-financial year forecasting.
My growth estimate for this financial year is quite high, at a little under 4 per cent, with the 95 per cent confidence region going down to two per cent. In the plot below, the black line is the actual history, the green line is the model output, and the yellow bands are the 95% confidence region.
So this tells us something about the annual forecast—growth has been slower but not terrible. How about the quarterly picture? Of course, we don’t have quarterly GSP to use as a dependent variable, so we need to take the parameters estimated in the model above, and apply it to annualised quarterly data. This gives my estimate of quarterly year-on-year GSP growth for Western Australia:
So most of the four per cent of economic growth I forecast for this financial year are from the periods of high growth in the first two quarters of this financial year. With these y.o.y. estimates, it’s entirely possible that current growth in WA is zero or negative.
Data and code are here.