US Forecast: July 26, 2019 |

Forecast Period2019:3--2025:4 (27 quarters)
The forecast is based on the national income and product accounts (NIPA) data that were released on July 26, 2019.
For purposes of this forecast the US model has been reestimated through
2019:1. These estimates and the complete specification of the model are
presented in
Appendix A: The US Model:
July 26, 2019.
A complete discussion of this January 28, 2018, version of the US model is in
Unless otherwise noted, the flow variables in the model are presented in this memo and on the site at quarterly rates. This is a change from versions dated July 31, 2011, and back, where the flow variables were presented at annual rates. To convert quarterly rates to annual rates, just multiply by four.
The table below gives the growth rates that were assumed for the key exogenous variables in the model along with the actual growth rates between 2010:1 and 2019:2. Growth Rates (annual rates) Forecast Actual Assumptions 2010:1-2019:2 TRGHQ 4.0 0.4 COG 2.0 -0.4 JG 1.0 0.3 TRGSQ 2.0 1.1 TRSHQ 2.0 7.2 COS 1.0 0.3 JS 1.0 0.0 EX 3.0 3.2 PIM 1.0 -0.1 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. The forecast assumptions for the federal government spending variables incorporate the provisions of the expenditure bill that was passed in February 2018, using primarily estimates from the Congressional Budget Office. All tax rates were taken to remain unchanged from their 2019:2 values except for the personal income tax rate, D1G, and the corporate profit tax rate, D2G, which are affected by the tax bill that was passed at the end of 2017. Estimates from the Joint Committee on Taxation, December 18, 2017, were used to guide the choice of values for D1G and D2G. Also, TRFG, which is the value of transfers from firms to the federal government, was changed to reflect the Committee's estimates of the revenue gained from the repatriation of profits from abroad. And TBGQ, taxes paid by the financial sector to the federal government, was changed to incorporate the effects of the tax bill. So four variables were changed to reflect the tax-law changes. You can examine these variables as discussed below. The above assumptions regarding the state and local government variables result in roughly balanced budgets over time in the forecast. No assumption is needed about monetary policy for the forecast because monetary policy is endogenous. Monetary policy is determined by equation 30, an estimated interest rate reaction function or rule.
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 current version" after "US Model," 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.
The current forecast is thus that real growth is slightly above 2 percent between now and the U.S. election and that the unemployment rate is roughly constant. This forecast assumes no bad financial shocks. The growth predictions would be worse if there are. Possible bad shocks are financial market reactions to the growing federal government deficit and debt. 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, 100 points, you should drop the equation for CG and change CG by about -$1,000 billion. See the discussion in Section 7.2 of The US Model Workbook, July 26, 2019. This will have a negative effect on real output growth because of a negative wealth effect. |