About a month ago I produced my first forecasts of the US economy. This posts updates those forecasts given the new data available. The time-series model has several novel components. The most important of these components is aggregated expectations. Using expectations data from several sources I construct a new measure of expectations for real gdp, inflation and the federal funds rate. Instead of using the expectations directly (treating them as exogenous) I instead treat them as endogenous variables that are concurrently forecast with the VAR model. That is, I am forecasting the forecasts.
Another novel component of my forecasts are constructed variables that measure the cycles within various "sectors" of the economy. In addition, to the four sectors in my previous forecasts (manufacturing, labor market, housing market, and financial market), I have also included an international sector. This might be an important addition given the recent discussions about tariffs and potential trade wars. The first step in the process creates cycles for each of the data series in each sector. Then, using dynamic factor analysis, I extract the common factor (or two) for each sector and use those in my forecasting model.
The following three graphs display the quarterly forecasts of real GDP, CPI Inflation, and the unemployment rate. Below I dive into some detail to show the importance of expectations and the sectors in this forecasting model.
Unemployment change significantly since last month indicating that we are currently in the trough and that unemployment will remain more or less stable until mid 2019. Inflation is expected to be quite high in the first quarter, but drops of pretty dramatically to the Fed's 2 percent target. Real GDP projections for the first quarter of 2018 dropped off quite a bit since last month. In addition, the forecast for unemployment is very pessimistic.
One might think that these changes are likely because of the recent jobs report. An alternative hypothesis is that it reflects the changes of the real GDP expectations variable (see my analysis of the April WSJ Forecasts). Below I test those hypotheses:
It seems as though the model with out the labor market data would still have more or less the same result in terms of GDP forecasts. However removing GDP expectations data had a huge impact on Q1 forecast of GDP. Recall that the model does not use the expectations data directly, but instead treats them as endogenous variables that can help explain GDP and other variables.
Turning to inflation, there is less of a difference between the models in the short run, however, in the long run the lack of labor market data suggest inflation rates closer to the Fed's target.
Finally, what I find the most interesting graph, though the most subtle. It seems that the recent labor market data has a strong impact on the dynamic in the unemployment rate. Without the labor market data we see a trough occurring in mid 2019. This suggests that, while the jobs report doesn't have a large impact on GDP, it should change our expectations about unemployment. In addition, expected GDP appears to have a dramatic impact on short-run forecasts of GDP.