US Forecast: April 29, 2015 |

Forecast Period2015:2--2022:4 (32 quarters)
The forecast is based on the national income and product accounts (NIPA) data that were released on April 29, 2015.
For purposes of this forecast the US model has been reestimated through
2015:1. These estimates and the complete specification of the model are
presented in
Appendix A: The US Model:
April 29, 2015.
A complete discussion of the version of the US model dated November 11, 2013,
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 1989:4 and 2007:4 (before the stimulus measures beginning in 2008). Growth Rates (annual rates) Forecast Actual Assumptions 2007:4-1989:4 TRGHQ 4.0 4.0 COG 1.0 2.3 JG 1.0 -0.7 TRGSQ 4.0 5.3 TRSHQ 8.0 5.7 COS 1.0 3.9 JS 1.0 1.5 EX 6.0/7.0 6.0 PIM 0.0 1.2 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 value 6.0/7.0 for EX means that EX was assumed to grow at an annual rate of 6.0 percent for the last three quarters of 2015 and then at an annual rate of 7.0 percent after that. All tax rates were taken to remain unchanged from their 2015:1 values. No attempt has been made to guess what changes in tax rates might be made in the future. Nor has any attempt been made to guess what changes in government spending might be made in the future. The present forecast is a forecast conditional on no future tax changes and on the assumptions about government spending listed above. It is a base forecast from which experiments can be run regarding spending changes and tax changes. 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 main message from the current forecast is that if there are no bad shocks, no further tax increases, and no government spending cuts, the economy grows well enough to stabilize the unemployment rate at about 6.0 percent. (One reason the unemployment rate is not forecast to be any lower is that the model is predicting some increase in the labor force as the economy expands---discouraged workers moving back in---and some increase in the number of people holding two jobs.) 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, April 29, 2015. This will have a negative effect on real output growth because of a negative wealth effect. Regarding the federal government spending variables, the key variables are TRGHQ and COG. The key personal income tax rate is D1G. The employee payroll tax rate is D4G. |