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.
Many of my posts have focused on the WSJ economic forecasting survey. However, I have decided to include my own projections of the US economy. For now I will focus on headline economic data, namely Real GDP, Inflation, and Unemployment, but I plan to add more macroeconomic indicators as I develop more models.
The unique aspect of my forecasting methodology is grouping some data series into few sectors (financial, manufacturing, housing, and labor) and including the aggregate measures of expectations I have previously discussed. Please see those previous posts for the methodology behind the aggregate expectations.
The sector data uses the cyclical components of several data series within a sector. For example, in the labor market sector I use payroll employment, initial claims, weekly hours, and mean unemployment duration. After generating those cycles I feed them through a dynamic factor model and extract a latent factor series, which I consider markers of broader cycle within the sector. The graph below shows those latent factors:
Combining those latent factors of those four sectors with aggregated expectations of inflation, real GDP, and the Federal Funds Rate, we run VARMA(4,0) and forecast 12 quarters out. The bar graph below presents the results for real GDP:
The model is optimistic about the second half of 2018. On the whole 2018 looks to be a quite robust year with 2019 showing signs of returning to the average long run growth rate.
Turning to unemployment, the model is off a little on the level of unemployment, but I suspect that the dynamics are more or less a good prediction. The trough in unemployment is expected fro the first quarter of 2019. This is a little earlier than the WSJ consensus, but It also seems in line with the slow down predicted in GDP.
The forecasts for inflation are a little more surprising. The model predicts low inflation (relative to the Fed's target of 2%) for 2018. If I remove the federal funds rate expectations from the professional forecasters then this drop off in 2018 disappears. Therefore, the impact seem all related to the planned monetary tightening by the Fed.
I plan to make many improvements to this forecasting model, but would be interested in your thoughts...
I have started a new project with the goal of generating several series that aggregate expectations of macroeconomic variables. This post describes the inflation expectations series. Below I have plotted the results relative to actual year-over-year CPI inflation.
This series combines six different inflation expectations series: Federal Reserve Greenbook forecasts, Survey of Professional Forecasters, NY Fed Survey of Consumer Expectations, WSJ Survey, Cleveland Fed Inflation Expectations, and the Michigan Survey of Consumers. There are typically multiple forecasts for any given month over many horizons. To construct the series I create an average weighted by the inverse of the forecast horizon. Then in order to strip away some of the seasonal shocks I pass the resulting data through and unobserved components model.
Here is the raw expectations data:
We can see that only the last few years of the series incorporate the NY Fed and the WSJ forecasts, and the Greenbook survey has a 6 year lag. As such the most interesting part of this new measure is the last several years of data:
While the low inflation in 2015 did impact expectations (with a lag) the broad trend shows that inflation expectations are quite stable and slightly above 2 percent. This is good news for the Fed, but this series is influenced by Fed forecasts, so I dropped both the Greenbook and the Cleveland Fed forecasts in the graph below:
Interestingly, there is an even larger impact due to the 2015 drop in inflation, and it appears as if consumers and professional forecasters actually have lower inflation expectations in general.
While these graphs are useful, the methodology used in constructing the aggregated inflation expectations necessarily lost a lot of information. In the future, I would like to not only have the series for each month, but also the forecasts over the preceding 3 year horizon. With that information we could not only see what was happening to expectations, but also how those expectations of a particular point in time were evolving.
Please let me know your thoughts...
The Fed raised rates recently keeping their promise of raising the rate in the face of positive economic data. The WSJ forecasts corroborate this rosy picture of the future economy and improving their forecasts from last month.
The consensus GDP growth forecast for the coming four quarters rose by a tenth of a percent or more, and all future unemployment numbers went down. It also looks like the forecast for the turning point in unemployment is being pushed out until mid 2019. The consensus probability of a recession in the next 12 months dropped half a percent to 14.1 percent.
Some of the details however do not indicate the same degree of certainty in the future economic position. The expected gap between the federal funds rate at ten year bonds continues to tighten signaling a flattening yield curve, which means the Fed may struggle normalize long rates in time for the next recession. Expected housing starts and housing prices dipped a little, but not enough to be overly concerning. Of course it is no surprise given political turmoil in Saudi Arabia, OPEC extension of production cuts, and now the North Sea pipeline problems, that expected oil prices increased significantly. Expected prices increased by 2 dollars or more through 2018.
All in all, there is a strong belief amongst the Fed and professional forecasters that board scope economic futures look robust, but there are still some potential weaknesses.
The most recent economic data continues to suggest a robust economy, and the WSJ Economic Forecasts reflect those facts. This post will present a brief overview of the changes in the average WSJ forecasts.
The main highlight from the GDP forecasts is a tenth of a percent increase in annual GDP growth for 2017, though only one quarter exhibited a decrease. Inflation forecasts also inched up in the near term (through 2018), but fell slightly post 2018. The positive employment reports have caused the predicted path of unemployment to shift down by almost a tenth of a percent as well, but the predict peak of employment is still around the end of 2018 or beginning of 2019. The probability of a recession in the next 12 months decreased by more than one percentage point to 14.6 percent.
The expected spread between the ten-year bond yields and the federal funds rate continue to tighten. This means that the yield curve may flatten out over the coming years. If that comes to pass monetary policy makers will not have as many tools at their disposal for the next recession since they will only be able to impact the short end of the yield curve.
End-of-year expected crude oil prices jumped up by over 3 dollars, however, longer term forecast rose more moderately. Housing price growth forecasts continue to increase. The end of the 2017 consensus is now expected to reach 6 percent. However, expected housing starts declined slightly through 2019.
All in all, the recent data has moved forecasters to have a generally bullish view on the future economy.
The new WSJ forecasts were released last Friday and it looks as if the recent data has caused the forecasters to be pessimistic about the short-term, but optimistic about the long-term. Forecasts for inflation and unemployment through 2018 worsened (unemployment ticked up, while inflation decreased), but consensus predictions for for both variables in 2019 improved. In addition, only the last two quarters of 2017 GDP growth were revised downward, and all subsequent quarters and annual projections rose.
These general macroeconomic indicator forecasts were somewhat at odds with the changes in specific indicators like the ten-year bond rates and crude oil prices. Bond rates were all revised downward, despite increases of the expected federal funds rate in 2019. Crude oil prices are still expected to rise slowly over the next two years, but only reaching the low 52 dollar mark, instead of 53 or 54 from a couple of months ago. Despite the lackluster September employment report, payroll forecasts for next year rose by over 10,000 to 16,080.
These numbers suggest that the recent data implies that the economy is sliding a little below the long-run growth path. As I pointed out while discussing the recent payroll report, long-run time-series dynamics seem to be dominating current forecasting (as opposed to structural modeling and forecasting). I believe these recent round of forecasts supports that idea, because this pattern of revision is consistent with the behavior we observe. To see what I mean look at Crude Oil Price forecasts:
The graph above shows forecasts at different points in time (light to dark indicates old to new). All we see are level shifts (the intercept) holding the dynamics (the slope) the same. That suggest the new data are not changing anything about the fundamentals, which would alter the trajectory, but instead only reveal changes in the starting point of a more or less unchanged dynamic system.
But is that good news or bad news? The good news: there really isn't any bad fundamental news. The bad news: models based on dynamic systems are correct on average, but since they are essentially data driven, it makes forecasters appear to be agreeing with each other. So the recent drop in forecast uncertainty (defined as the standard deviation amongst forecasters), does not necessarily indicate that we know a lot about where the economy is heading.
A logical analysis of expectations might argue that historical averages drive long-run forecasts with macroeconomic fundamentals and announced policies providing slight mitigation. In contrast, recent economic data drive short-run forecasts. WSJ expected inflation for 2017 provides a good example of this:
Almost 3 years in advance, the forecasts match the Fed's stated policy and historical averages. At the beginning of the 2017 it looked as though year over year CPI inflation (yellow line) and expectations were converging. However, the last few months of slow price growth have caused expectations to dip following, with a lag, the path of year over year inflation.
This post points out that this pattern of behavior need not exist for all variables.
Instead consider the federal funds rate. These expectations, more so than those for inflation, driven by mostly by policy statements and projections released by the Federal Reserve. It is therefore not surprising how high expectations were at the beginning of 2015 and how quickly they have fallen over the past 2 years. In 2015 the discussion was over normalization of policy or "the lift-off," but the data at the time did not support action at that time. As policy statements and Fed projections became more clear expectations dropped quickly. Notice also the drop in uncertainty, a point I have emphasized in a previous post on federal funds rate expectations.
The drivers of inflation and the federal funds rate have no direct impact on the bottom line of the forecasters or the firms for which they provide their forecasts. However, take ten-year government bond rates for example:
These expectations more closely follow the path of the actual bond rates even 2-3 years in advance of the realization. While bond rates should, in theory, be just as sensitive to monetary policy one would expect a similar pattern, however the gap observed at the beginning of this series does not appear as wide and the overall time-series movement between expectations and actual observations are remarkable similar.
How we think about long-run and short-run expectations depends critically on the relative importance of the outcome to our objectives.
The BLS recently released the new CPI and inflation statistics. This signals a weakening economy along the lines of Tim Duy's analysis. As Mark Thoma points out, the Fed does not have a lot room to defend the economy against a recession, and congress seems incapable of doing anything at the moment. The WSJ Economic Forecasters expectations indicate further trouble, since they are above actual inflation, but are dropping:
If lower inflation expectations exist in the rest of the economy we can expect slower growth in the coming months. Lower inflation expectations usually are a self fulfilling prophecy, since workers, firms, and households make decisions that reinforce the low inflation future. For example, firms might anticipate lower revenues and therefore lower prices in order to drive up sales.
How do these lower inflation expectations fit into the larger long term picture? Well, the same analysts forecast GDP growth at 2.38 and 1.94 in the 2018 and 2019, respectively. In addition, my previous post on Fed funds rate expectations uncertainty indicates that Federal Reserve credibility (at least in terms of future interest rates) has improved. Inflation expectations for 2017 are low, but expectations for 2018 and 2019 are both firmly above 2 percent. This suggests that analysts believe the economy will slow down in the next two years and that the Fed will take appropriate measures (with what little room they will have) to fend off or minimize a recession.
Bill McBride at Calculated Risk, commented on the strength of the recent employment report. Indeed, the news was much rosier than expected. Tim Duy's Fed Watch agreed with market expectations of approximately 170,000 jobs. The WSJ Economic Forecasters expectations were slightly lower at 165,000 and they have typically underestimated payroll employment:
However, the graph above and the graph below hint at the Federal Reserve's continuing conundrum. Payroll employment seems to be consistently beating expectations, but inflation is lower than expected. The WSJ Forecast below is for June 2017 year over year inflation, and the trajectory of actual inflation it seems unlikely that we will break the Fed's two percent target.
The upcoming release of the July WSJ Economic Forecasts may provide more insight to where market participants think the Fed is heading. It is likely that the jobs report will boost both GDP and fed forecasts, but that relies on forecasters optimistic outlook on inflation.