US Forecast: April 30, 2022
2022.2--2025.4 (15 quarters)
The forecast is based on the national income and product accounts (NIPA) data that were released on April 28, 2022. Unless otherwise noted, the flow variables in the model are presented in this memo and on the site at quarterly rates. To convert quarterly rates to annual rates, just multiply by four.
For this forecast equations 25, 28, and 30 have been dropped, so the variables CGZ, LUB, and RS are exogenous,
Assumptions Behind the Forecast
The table below gives the growth rates that were assumed for some of the key exogenous variables in the model.
Assumed Growth Rates (annual rates) Forecast Assumptions TRGHQ see below COG 3.0 JG 0.0 TRGSQ see below TRSHQ see below COS 3.0 JS 1.0 EX 5.0 PIM 9.0
The first seven variables are the main government policy variables in the model aside from tax rates. TRGHQ is real federal government transfer payments to households, COG is real federal government purchases of goods, JG is federal government civilian employment, TRGSQ is real federal government transfer payments to state and local governments, TRSHQ is real state and local government transfer payments to households, COS is real state and local government purchases of goods, JS is state and local government employment, EX is real exports, and PIM is the import price deflator.
An issue for the current forecast is how to account for the remaining effects of the American Rescue Plan Act (ARPA) passed in March 2021. The Congressional Budget Office (CBO) and the Joint Committee on Taxation (JCT) have estimated the budget outlays from this act, and I have used these numbers. I have estimated that the extra spending in real terms from the act in each of the next 6 quarters (2022.2--2023.3) is 99, 99, 24, 24, 24, and 23 billion 2012 dollars respectively. (These numbers are at quarterly rates.)
Although some of this additional spending will take the form of increased real federal grants in aid to states (TRGSQ), increased subsidies (SUBG), and increased real transfer payments from states to households (TRSHQ), for purposes of the forecast I have put all the increased spending in real federal government transfer payments to persons (TRGHQ). The quarterly values for SUBG in 2022.2--2025.4 were each taken to be $20 billion, which is its value in 2020.1, the last quarter before the pandemic. If TRGSQ had grown at an annual rate of 3 percent from 2020.1, its value in 2022.1 would have been $150 billion (real terms). This value was used as a base and TRGSQ was taken to grow at an annual of rate of 3 percent for 2022.2--2025.4. The same procedure was followed for TRSHQ, where 3 percent was also used. Finally, the same procedure was followed for TRGHQ using 3 percent to get baseline values. Then the above increases were added to TRGHQ for the first 6 quarters. In other words, normal growth has been assumed for SUBG, TRGSQ, and TRSHQ, and all the additional spending is put in TRGHQ. The implicit assumption here is that the multiplier effects from the three variables are the same as the multiplier effects from TRGHQ.
Some of the other assumptions for the forecast are as follows (all growth rates are at annual rates): tax rates unchanged from their 2022.1 values, potential output (YS) growing at 3 percent, labor productivity (LAM) growing at 1.5 percent, and the relative price of housing (PSI14) growing at 5 percent.
Regarding monetary policy, the estimated Fed rule, equation 30, has been dropped and RS has been taken to be 1.5 in 2022.2, 2.0 in 2022.3, and then 2.5 from 2022.4 on. The Fed is thus assumed to raise the short term interest rate to 2.5 by the end of 2022.
Nothing was done about possible tax and spending changes from the infrastructure bill that was passed. Nor has anything been done about possible spending increases from future Build Back Better bills that might be passed. The current forecast is obviously a conditional forecast, conditional on nothing new done after ARPA and the Fed raising the short term interest rate to 2.5 over the year. As least for the first year or two ``nothing new'' is likely not a bad approximation since it will take time for the legislation to be passed if it is and for the beginning of the increased spending and taxes.
Forecasts of selected variables are presented in the following: Forecasts of selected variables---html, Forecasts of selected variables---pdf file. If you want more detail, click "SOLVE", create a data set, and then go immediately to "Examine the results without solving the model." You can then examine any variable in the model.
Real GDP Growth and the Unemployment Rate:
The forecast has real GDP growing in the next four quarters at 3.8, 5.8, 4.7 and 3.5 percent, respectively. (Growth rates are at annual rates.) The unemployment rates in the four quarters are 3.4, 3.2, 3.1, and 3.1 respectively. These robust growth rates are in part due to household wealth, which is large from past transfer payments saved and from past large increases in stock and housing prices. This has a large effect on household expenditures, including housing investment. The remaining spending from ARPA is also a factor. The growth rate slows considerably after 2022, and the unemployment rate begins to rise.
Inflation as measured by the growth of the GDP deflator in the next eight quarters is 5.1, 6.1, 6.3, 6.3, 6.1, 5.7, 5.4, and 5.1 percent, respectively. These high inflation forecasts are the result of the low unemployment rates. An interesting question is if this turns out to be the case, will the Fed increase interest rates more than is assumed for this forcasta, which is an increase only to 2.5 percent by the end of 2022.
Possible Experiments to Run
This forecast assumes no bad financial shocks. The growth predictions would be worse if there are. Possible bad shocks are financial market reactions to growing inflation. Below is a discussion of how to incorporate these shocks into the model.
The assumption of no bad shocks, which is used for the forecast, means that stock prices, housing prices, and import prices grow at historically normal rates. There are no negative wealth shocks through falling stock prices and housing prices. Changes in asset prices like stock prices, housing prices, exchange rates, and oil prices are essentially unpredictable. One can use the US model to analyize the effects of asset price shocks, but the shocks themselves cannot be predicted. The best one can do in a forecast is to assume some historically average behavior of asset prices, which has been done here.
To examine the effects of asset price shocks, experiments can be run using the model in which stock prices (variable CG), housing prices (variable PSI14), and import prices (variable PIM) are changed. This allows one to examine the sensitivity of the forecast to changes in these values.
To review, oil price shocks and exchange rate shocks are handled through changes in PIM. Housing price changes are handled through changes in PSI14. Changes in PSI14 change PKH relative to PD and thus change housing wealth, PIH*KH. This affects consumption expenditures through the total wealth variable AA (equations 1, 2, and 3). It affects housing investment through the housing wealth variable AA2 (equation 4). Regarding the stock market, each change in the S&\P 500 index of 10 points is a change in CG, the capital gains variable in the model, of about $100 billion. If you think that the S&\P index will fall, say, 500 points, you should CG by about -$5,000 billion. (The CG equation is dropped for the forecast.) See the discussion in Section 7.2 of The US Model Workbook, April 30, 2022. This will have a negative effect on real output growth because of a negative wealth effect.
Other possible experiments are to change the various exogenous government policy variables in the model. For example, you may want to change some of these variables based on the infrastructure bill that was passed, including possibe tax rate changes.