Category Archives: Economic Outlook

Updated forecasts of Seventh District GSP growth

Updated forecasts of Seventh District GSP growth

Each year, the Chicago Fed provides estimates of annual gross state product (GSP) growth for the five states in the Seventh Federal Reserve District.[1] While presenting last year’s projections, we proposed a new forecasting model incorporating the U.S. Bureau of Economic Analysis’s (BEA) quarterly GSP data. In this post, we again use our quarterly model to generate GSP growth forecasts for 2016 (whose actual values will become available next month) and compare our estimates to those produced by our previous (annual) model described in Brave and Wang.[2]

GSP growth and the MEI

The BEA releases annual GSP data for the prior year each May. However, in an effort to provide a more frequent reading on regional economic growth, the BEA has also been releasing preliminary quarterly estimates of GSP since 2014. While the lag time in the production of these estimates is not as substantial as it is for annual GSP, it can still be rather long. For example, through April 2017, the 2016:Q4 GSP data have not yet been released, and will not be released until May 11, 2017.

The Chicago Fed’s Midwest Economy Index (MEI) provides an even higher frequency reading on economic growth for the five states of the Seventh District.[3] A weighted average of 129 state and regional indicators measuring growth in nonfarm business activity, the monthly MEI (like GSP) is a broad-based measure of economic conditions. After aggregating its monthly values to obtain a quarterly reading, the MEI also correlates quite well historically with Seventh District GSP growth.[4] Figure 1 illustrates this relationship, featuring a sample correlation coefficient of 0.57. [5] For this reason, the MEI forms the basis of our forecasting model for Seventh District GSP growth described in the next section.figure-1

2016:Q4 projections

To project the 2016:Q4 annualized GSP growth rate for each of the Seventh District states, we use the following linear regression model:

equation

This model relates each state’s current quarter GSP growth rate to national (the current quarter’s annualized growth rate of national gross domestic product, or GDP), regional (the current quarter’s MEI and relative MEI, or RMEI, index values), and state factors (the current quarter’s annualized growth rate of real personal income, or PI, and the previous quarter’s annualized GSP growth rate).

National and regional economic conditions play an important role in capturing state-level growth for all five Seventh District states. However, some states respond differently to regional economic conditions depending on the health of the national economy. The relative MEI reflects Midwest economic conditions relative to those of the nation, such that its inclusion in our model is designed to capture the interaction of national and regional factors on state-level growth. The inclusion of lagged GSP and state PI is then intended to capture state-specific factors that our regional and national indicators fail to. This is particularly important for states such as Iowa, where a larger share of economic activity is in agriculture, as the MEI only considers nonfarm business activity.

The extent to which each of these factors contributes to explaining GSP growth in our forecasting model varies across the five states. National growth seems to be the most important factor for Iowa, Illinois, and Wisconsin; but state and regional factors dominate for Indiana and Michigan, respectively.

figure-2

Figure 2 shows our projections for 2016:Q4 for the five Seventh District states and the Seventh District states combined.[6] All five states featured fairly strong growth readings for 2016:Q3. As a result, the model predicts some mean reversion in Q4 for each state. Compared with the other four states, Iowa has a somewhat weaker GSP growth projection of 1.0%. This is explained in part by Iowa’s negative PI growth in Q4, whereas the other Seventh District states featured positive PI growth. On the other end of the spectrum, Indiana has a somewhat stronger projection of 2.3%.

2016 projections

Based on the quarterly GSP data for the first three quarters of 2016 and our projection for the fourth quarter, we can also project 2016 annual GSP growth for the Seventh District states. These estimates are shown in the first column of table 1. Our projection of 1.6% for annual GSP growth for the Seventh District states combined is identical to the national GDP growth rate. There is variation, however, in our annual forecasts across individual states, which can be largely explained by differences in observed growth in the first three quarters of the year (see figure 2). Michigan’s annual forecast is consistent with its strong growth readings in Q2 and Q3. Conversely, Iowa’s large negative growth rate in Q1 led to a negative annual growth rate forecast. Finally, Illinois’s and Indiana’s similar growth rates throughout the year yield similar annual forecasts; and Wisconsin’s weak first quarter weighs down its annual forecast despite relatively strong growth in Q2 and Q3.

table-1

We also present in the second column of table 1 projections from the annual GSP growth forecasting model described here. These projections are quite similar across the Seventh District states; but as we saw last year, the annual model tends to forecast higher growth than predicted by our quarterly model (the only exception being Michigan). In general, we believe the added information provided by considering GSP quarterly data should make our forecasts from the quarterly model more accurate than those from the annual model. However, some caution should be exercised while reviewing even these projections from the quarterly model. For instance, the readings shown in figure 2 may in fact be revised. At this time last year, Iowa had a similarly poor Q1 growth rate that was later revised up to a slightly positive value.

Conclusion

We will release our GSP growth forecasts for 2017:Q1 in June when state personal income data for the first quarter becomes available. These projections can be found in the MEI background slides.

[1] The Seventh District comprises all of Iowa and most of Illinois, Indiana, Michigan, and Wisconsin. Further details are available at https://www.chicagofed.org/utilities/about-us/seventh-district-economy and https://www.federalreserve.gov/aboutthefed/federal-reserve-system-chicago.htm.

[2] Scott Brave and Norman Wang, 2011, “Predicting gross state product growth with the Chicago Fed’s Midwest Economy Index,” Chicago Fed Letter, Federal Reserve Bank of Chicago, No. 293, December, https://chicagofed.org/publications/chicago-fed-letter/2011/december-293.

[3] The entirety of the five states that are part of the Seventh District is considered for the MEI.

[4] The MEI is constructed as a three-month moving average. Hence, to obtain the quarterly average values shown in figure 1, we use the MEI readings corresponding to the last month of each quarter.

[5] We aggregate the five state values to one value for all Seventh District states using their nominal GSP shares. The horizontal solid black line in figure 1 corresponds to the average GSP growth rate over the sample period.

[6] We arrive at the Seventh District forecasted value for 2016:Q4 by aggregating the state forecasts using nominal GSP shares from 2016:Q3.

Updated Forecasts of Seventh District GSP Growth

Several years ago, the Chicago Fed began providing estimates of annual gross state product (GSP) growth for each of the five states in the Seventh Federal Reserve District.1 The U.S. Bureau of Economic Analysis (BEA) releases annual GSP data for the prior year each June. This post discusses GSP projections for 2015 and presents an alternative forecasting model using quarterly GSP data from the BEA.2

The 2015 Growth Picture

To provide context for our projections, we first take a brief look at the main indicators of our model. Figure 1 shows annual U.S. gross domestic product (GDP) growth and GSP growth aggregated across the five states in the Seventh District from 2005 through 2014. Actual GSP data for 2015 will not be released for another month. However, we can get a sense of what this data is likely to show by comparing the recent histories in the figure. While growth in the Seventh District has lagged behind the nation in recent years, it has tended to follow a similar trend over longer periods. The U.S. maintained an annual growth rate of around 2.4% from 2014 to 2015, providing a reasonable starting point for our estimate of District growth in 2015.

fig1

The Chicago Fed’s Midwest Economy Index (MEI) then provides a useful link between national and regional growth that can give us a sense of the likely persistence of the recent shortfall between District and national growth. As figure 2 shows, the MEI indicated that the Midwest economy experienced growth that was somewhat above trend in the first half of 2015 and slightly below trend in the second half. Additionally, the relative MEI dipped below zero in the third quarter, suggesting that Midwest economic growth was further below its trend than national growth was in the second half of the year. Though the MEI does not explicitly pertain to GSP growth, historically a zero value for the index has been roughly consistent with 1.5% annual GSP growth for the District. In light of this, both the MEI and relative MEI suggest that District GSP growth in 2015 likely rebounded from its 2014 rate of 1.1% to somewhat above its trend rate of 1.5%, but still below the national growth rate of 2.4%.

fig2

Finally, we turn to annualized quarterly growth of state real personal income over 2015which provides an indication of how state-specific factors may have affected District GSP growth in 2015. Figure 3 shows that both Illinois and Michigan experienced strong income growth in the first quarter of 2015. Though the District states generally experienced weak income growth in the second quarter in comparison with the national average, growth rates in the remaining quarters of 2015 were of similar magnitudes to the national rate. Taken together, these data suggest some likely variation in GSP growth rates across the District states, but for the most part, they are consistent with the MEI and U.S. GDP growth data in figures 1 and 2.

fig3

Forecasts for 2015

Our forecasts for 2015 combine the information in the indicators discussed in the previous section to arrive at an estimate of annual GSP growth for the District states and the District as a whole. Since 2011, the Chicago Fed has used the following statistical model to estimate annual GSP growth:

formula

This model explains the annual GSP growth rate of each Seventh District state as a function of national GDP growth, regional economic conditions as captured by the monthly MEI and relative MEI, and state-specific conditions (specifically, quarterly real personal income growth and annual GSP growth in the previous year).3 We aggregate state projections into a District-wide forecast using each state’s respective share of nominal District GSP.

Figure 4 shows for each District state and the entire District their respective historical GSP growth (blue bars), in-sample fits (orange lines) of GSP growth obtained from our statistical model, and 2015 out-of-sample projections (green lines). With the exception of Iowa, the model predicts an increase in the GSP growth rate for the Seventh District states, as well as the District as a whole. Interestingly, this seems out of line with the national GDP data and the MEI and relative MEI data discussed earlier. It is of note, however, that for 2014 the model also estimated higher GSP growth than what was realized for each state.

fgi4

Motivated by our model’s recent shortcomings, we developed a similar model estimated using the experimental quarterly GSP data recently published by the BEA. Figure 4 also contains projections of annual GSP growth (red bars) obtained from this new model. At the time of writing, these data were available through the third quarter of 2015. To obtain a GSP growth projection for all of 2015 with these data, we need only estimate the fourth quarter’s value. Using the new statistical model to obtain this estimate and combining it with the data from the first three quarters, we arrive at an alternative forecast for 2015.

Figure 4 clearly shows a large difference between our two forecasting models. As table 1 further demonstrates, the projections from our new quarterly model (Q4 forecasted column) are below those of our original annual model in every instance, and often by quite large magnitudes. For the District as a whole, our quarterly model predicts more modest GSP growth of 1.6%, compared with 2.5% as forecasted by the annual model. This estimate from our quarterly model is also in line with the previous evidence suggesting growth was slightly above the historical trend of 1.5% but below the national growth rate of 2.4% for 2015.

Table 1. Annual GSP Growth Forecasts for 20154

table1_GSP

To illustrate the sources of the discrepancy between our model forecasts, we plot in figure 5 the annualized quarterly GSP growth data from the BEA (blue bars) for 2015, including our fourth quarter estimates (red bars) and fitted values from the model (orange lines). It is important to note that both our annual and quarterly models use the same 2015 data for the variables that they share in common, with the exception of the three quarters of GSP data that we have for 2015. These data show sharp contractions in GSP for every District state (with the exception of Illinois) in the first quarter. More than anything else, this feature of the quarterly data is the dominant source of the discrepancies between our annual and quarterly model projections. The model fits in figure 5 make this clear, as they demonstrate very large negative residuals in the first quarter and only small misses in the other quarters, reflecting the fact that the declines in GSP in the first quarter are not consistent with the indicators in our statistical model.

fig5

It is possible that the quarterly GSP data for 2015 will be revised upon the release of the annual figure next month. As noted previously, our model predicts weak first quarters for the District states, but not nearly as dramatically as what has been released by the BEA thus far. Considering the apparent inconsistency between the quarterly data and the model, we also generate a forecast for 2015 that uses the fitted values for the first three quarters of 2015 GSP growth instead of the quarterly data. These projections, presented in the Q1–Q4 forecasted column of table 1, are larger than the quarterly model’s estimates using the quarterly values for 2015, but are still below those of the annual model. Moreover, these projections suggest that at 2.0%, District GSP growth improved in 2015 and was closer to the national average, but still below it.

Conclusion

We will continue to monitor the performance of both our annual and quarterly forecasting models. However, based on the results presented here, we intend to report annual GSP growth rates for each District state from our new quarterly model combined with the available quarterly data for the 2015 forecast (as presented in the Q4 forecasted column in table 1). From now on, we will continue to report estimates from this model as long as the quarterly data from the BEA make it possible to do so.

  1. The Seventh District comprises parts of Illinois, Indiana, Michigan and Wisconsin, as well as all of Iowa.
  2. The quarterly GSP data provided by the BEA is still in an experimental phase. For more information, visit https://www.bea.gov/regional/index.htm.
  3. The model is explained in more detail in Brave and Wang (2012).
  4. To allow for “like-for-like” comparisons among District forecasts, we aggregate state-specific annual forecasts to the District level using annual nominal GSP shares. The 2015 projections were aggregated using the 2014 shares.

Recap of the Federal Reserve Bank of Chicago’s 29th Annual Economic Outlook Symposium

Please note: this is a cross-post from the Chicago Fed’s Michigan Economy blog.

On December 4, 2015, the Federal Reserve Bank of Chicago hosted its 29th annual Economic Outlook Symposium (EOS). The EOS allows economists, business leaders, financial analysts, and other experts to gather and share their respective views on the U.S. economy and individual sectors especially important to the Midwest economy. Also, EOS participants are given the chance to submit their respective projections for the year ahead. These projections are subsequently used to come up with a consensus (median) forecast for real gross domestic product (GDP) and related items.

This blog entry is a summary of what was presented at the latest EOS. For a more in-depth look into what was presented, please click here to read the Chicago Fed Letter for the event. Most of the presentations that were delivered during the EOS can be found here.

  • 2015 forecast review: Real GDP growth in 2015 was slightly weaker than expected in the consensus outlook from the previous EOS held in December 2014. Growth in real personal consumption expenditures was slightly higher than anticipated, partly because of stronger than expected growth in light vehicle sales. However, real business fixed investment grew at a significantly slower rate than predicted. New home construction just missed forecasted activity levels. The unemployment rate was lower than originally projected, while inflation (as measured by the Consumer Price Index) came in well below the predicted rate.
  • Outlook for consumer spending: According to Scott Brown (Raymond James & Associates), consumer spending is forecasted to slightly decelerate in 2016 in part because of headwinds from rising energy prices (he expected oil prices to average around $50 per barrel by year-end). The pace of job growth has been strong, but is expected to moderate this year.
  • Outlook for financial services: Brown also noted that credit conditions are fairly tight, but they should ease. The (then-anticipated) interest rate hike in December by the Federal Open Market Committee (FOMC)—the Federal Reserve’s monetary policymaking arm—shouldn’t dampen lending for a while, Brown said.
  • Auto industry outlook: According to Yen Chen (Center for Automotive Research), U.S. light vehicle sales and production are expected to peak in 2018 (at around 18.6 million units and 12.2 million units, respectively) before falling slightly. Auto loan debt is expected to surpass student loan debt as the highest form of household debt, excluding mortgage and home equity debt, over the coming years. Meanwhile, Mexican light vehicle production capacity is expected to increase by over 2 million units in the next seven years largely because of lower labor costs (thereby reducing the U.S. share of North American production).
  • Steel industry outlook: Robert DiCianni (ArcelorMittal USA) indicated that U.S. steel consumption is projected to modestly increase in 2016, based on his analysis of several steel-intensive sectors of the economy. For instance, the pace of growth in residential construction is expected to accelerate, while year-over-year growth in nonresidential construction is anticipated to level off. Moreover, both U.S. auto sales and North American auto production in 2016 should be similar to their respective levels in 2015. Global steel consumption is expected to increase slightly in 2016 after decreasing last year. The slowdown in Chinese steel consumption has been a major factor in the decelerating rate of global steel consumption in the past few years.
  • Heavy machinery outlook: Glenn Zetek (Komatsu America Corp.) stated that U.S. demand for earth-moving equipment is at healthy levels, though demand has slowed significantly in states where energy production had been intense over the past few years. Equipment demand for single-family residential and transportation projects is expected to increase in 2016. But heavy machinery demand for nonresidential projects should moderate this year; the prospects for equipment demand to complete such projects look more promising over the next couple of years, as nonresidential fixed investment is expected to move up moderately and equipment usage is near its mid-2000s peak. Equipment usage for mining, energy, and rental needs are predicted to decrease.
  • State and local government debt outlook: According to John Mousseau (Cumberland Advisors), municipal bond yields for the highest-rated securities with maturities greater than ten years are higher than comparable U.S. Treasury bonds—the opposite of what’s normal. Even with Detroit’s bankruptcy and other cities’ and states’ latest financial struggles, municipal bond quality generally remains higher than corporate bond quality. Interest rate increases won’t be terrible for issuers of municipal bonds because historically, municipal bond yield increases failed to match the size of federal funds rate increases.

Conclusion: 2016 economic outlook

According to the latest EOS consensus outlook, U.S. real GDP growth in 2016 is expected to increase slightly above its historical trend. Inventory levels are expected to rise at a slower pace. Residential investment is projected to rise at a strong pace, with slow and steady improvement predicted in new home construction. Growth in business fixed investment should continue at a decent pace, with moderate growth anticipated in industrial output. The dollar is estimated to slightly appreciate versus major currencies, which should increase the U.S. trade deficit to levels not seen in the past decade. Forecasters expect interest rates to rise, but remain at relatively historical lows. The unemployment rate is predicted to edge slightly below current levels. Inflation is expected to move up (closer to the FOMC’s inflation target) as oil prices strengthen slightly.