2000-2001 Forecast:  U.S. Gross Domestic Product

Using Real Private Nonresidential Fixed Investment

                                              

BRENDA LUTHER

College of Education and Allied Professions  & College of Business

Western Carolina University

 

JILL FRANKLIN

College of Business

Western Carolina University

                                                 

Abstract

 

This is a forecast of U S Gross Domestic Product (GDP) for 2000 through 2001. (Lagged government expenditures and gross investment were chosen as a combined explanatory variable used to forecast future GDP.) The forecast is based on the Keynesian Aggregate Expenditure Model which relies on the accounting identity expressing that aggregate expenditure or GDP has the following components: consumption, investment, government expenditure, and net exports. The MPC and MPI are 56 percent and 23 percent, estimated from quarterly data.  This implies a historically low Keynesian autonomous expenditures multiplier of 1.52.  The interest elasticity of investment is 16 percent also estimated from the data used. Investment spending is projected into the future with a horizontal time trend, as all the other components of aggregate expenditures are held constant.  The GDP forecast predicts a gradual and steady, increase in the growth of the economy over the next two years. (This conclusion is drawn from the fact that GDP continues to steadily rise between the first quarter of 2000 and the fourth quarter of 2001.) The U S government and the Fed need to be concerned about the possibility of an increase in inflation rising in the future so they can begin counteractive measures.  If the Fed has a suspicion that an inflationary period may occur, it could take measures to slow the economy. (JEL: E120, H50)

 

Part 1. Introduction

 

This paper forecasts U S Gross Domestic Product for the years 2000 and 2001.  The key explanatory variable is real lagged private nonresidential fixed investment. The projection is based on the Keynesian Aggregate Expenditure Model using new estimates of marginal propensity to consume (MPC) and marginal propensity to import (MPI) based on data from 1993.1 – 1999.4. The Keynesian investment function is estimated, and this approach utilizes the role of private investment expenditures as a guiding force in the aggregate economy.

 

Real Gross Domestic Product is the most thorough measure of United States economic income and output. A growing GDP indicates a strong economy. An increase in GDP would result in continued growth for the nation’s economy. In an effort to minimize unforeseen factors, a two-year horizon was chosen. The forecast horizon is relatively short, in order to avoid overstating or understating future GDP.

 

The remainder of this paper is arranged in the following order: Part 2 presents the data gathered to forecast GDP; Part 3 explains the economic theory for which the forecast is based; Part 4 presents the GDP forecast for 2000 and 2001; Part 5 indicates the importance of GDP with regard to the U.S. economy; Part 6 details the conclusions for an economic policy.

 

Part 2. Data

 

The data used to predict U S GDP was taken from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED) web site. The FRED variables used are GDPC96, GCEC96 and GPDIC96, respectively. The variables are reported in billions of chained 1996 dollars, which have been seasonally adjusted at annual rates (SAAR) and are reported quarterly.  The forecast horizon is two years from the last reported data.

 

Gross Domestic Product is one of the most important measures of a country’s economic activity. The purpose of this forecast is to predict economic activity into the future, with lagged government expenditure and gross investment as the explanatory variables. Lagged government expenditures and gross investment were chosen to forecast GDP because they are key components of GDP. It is conceivable that government expenditures and gross investment should be simple and reliable variables to forecast future GDP.

 

Part 3. Economic Theory: Forecasting with the Keynesian Model

 

This forecast assumes the interest rate will remain constant for the next two years and will have no impact on investment spending or GDP. The annual growth rates for investment spending will be calculated over the sample period of the data. The average annual growth rate will be used to project investment spending into the future for the eight quarters of 2000 and 2001 to provide a short-term projection of investment. The values for projected future investment will be used to calculate GDP forecasts for each future quarter using the Keynesian autonomous expenditures multiplier:

DY = DI/[MPS + MPI]

 

This is a simple model appropriate for forecasting short-term GDP growth.  The forecast should be interpreted as trend GDP in the absence of a recession.  This model is probably not adequate to forecast a recession, especially one caused by unanticipated real aggregate supply shocks. The marginal propensity to save (MPS) is 0.43, from a Keynesian Consumption Function estimated from the quarterly data gathered. 

 

This forecast assumes the interest rate will remain constant for the next two years. It also assumes the interest rate will have no effect on government expenditures, gross investment, or GDP. Quarterly GDP will be calculated for two years into the future.  Government expenditures and gross investment data were taken from data reported from 1993.1 through 1999.4.

 

The Keynesian Aggregate Expenditure Model is an appropriate approach to use to forecast GDP because the model supports the notion that government expenditures and gross investment influence the amount of GDP the United States produces in a given year. One problem with using government expenditures and gross investment is they are independent of GDP because the government sets the amount of expenditures within the budget each year.  This means that GDP has little effect on the amount of government expenditures and gross investment, even though government expenditures directly influence GDP.

 

National consumption, investment, government expenditures, and net exports are aggregate components of GDP, and these components influence the level of GDP for any given year. These components actually are responsible for the amount of GDP produced each year. The accounting identity that supports this theory is written as:

 

GDP = AE = C + I + G + X,

 

where AE is aggregate expenditure, C is consumption, I is investment, G is government expenditure, and

X is net exports for a country.

 

In the forecast, government expenditure was taken from the above equation and used in a regression with past GDP figures to calculate future GDP for the United States. The above equation is not actually used in the forecast; it merely supports the theory that government expenditures and gross investment influence,

or drive future GDP.

 

The foundation of this forecast is based upon the regression and trend analysis. There has to be a relationship between GDP, government expenditures, and gross investment in order for the forecast to be plausible. The Keynesian model provides that support.

 

Lagging the right-hand-side by two years, removing the other variables, and allowing intercept and error term yields:

Yt = a + b (Gt-2) + c (It-2) + d (Yt-2) + et

 

This is the equation mentioned in Part 4, and it forms the basis of the forecast calculations.

                                                

Part 4. GDP Forecast for 2000 and 2001

 

Government expenditures, gross investment, and GDP are measured in billions of 1996-chained dollars, and the figures are seasonally adjusted at annual rates.

 

Table 1

Regression Estimate of GDP Forecasting Equation 1: 1993.1-1999.4

Explanatory Variable      

Estimated Coefficient

t-ratio

Intercept (a)

-2682.544

-2.542

G coefficient (b)

-1.307

-1.708

I coefficient (c)

-1.896

-4.961

GDP coefficient (d)

1.969

14.394

R Square = .996

F (zero slopes) = 1193.233

Probability (F)  0.000

 

Table 1 summarizes the regression used to forecast GDP. The intercept and the X variable were the variables used in the regression equation to forecast GDP. The R-square is .996, which means that approximately 99.6 % of the variation in GDP is explained by variation in government expenditures and gross investment. The t-statistic for the intercept is equal to –2.542, and this indicates rejection of the null hypothesis. The t-statistic for the coefficient of government spending and the F-statistic for zero slope (which is the square of the t-statistic when there is only one right-hand-side variable) are also listed above.

 

The first part of the projected two-year period of GDP is relatively stable and resembles the current GDP figures. Toward the end of the period, it appears as if there will be continued growth in the economy. GDP rises through the forecast period. The 2000 to 2001 GDP forecasts are shown in table 2.

 

Table 2

Forecast GDP 2000.1-2001.4

(Billions of Chained 1996 dollars, SAAR)

Quarter

Forecast

Annual % D

2000.1

9071.201

------------

2000.2

9165.629

0.010

2000.3

9242.496

0.008

2000.4

9390.192

0.016

2001.1

9493.633

0.011

2001.2

9593.378

0.009

2001.3

9703.173

0.013

2001.4

9847.040

0.015

 

This forecast predicts growth through 2001.4, since GDP activity is on an upswing.

 

Part 5. The Wrap-Up

 

The forecast GDP is favorable in the sample period. The Federal Reserve would have to implement some policies to counter-attack the effects of a rapidly growing economy, which could cause inflation. One measure the Federal Reserve may introduce is an increase in interest rates. This would be in an effort to slow down the economy. The Federal Reserve should be concerned with the possibility of growth occurring too rapidly, which could cause inflation to rise. It can have devastating effects on everyone in the economy. The past actual GDP and the forecast GDP are shown in the attached graph in order to demonstrate the prediction.

 

Part 6. Policy Conclusion

 

GDP is projected to continue to increase at a moderate rate over the next two years. GDP is predicted to increase in 2000. In 2001, GDP is projected to moderately increase, as well.  This was concluded based on government expenditures and gross investment as the explanatory variables.

                                      

Industries will continue to enjoy steady growth in the economy over the next two years. According to the forecast data, it appears GDP is going to moderately increase in the near future. This will have little effect on U.S. industries. The industries will have to wait and see what policies the government will implement during this election year.

 

The United States government needs to be concerned with the possibility of high inflation due to GDP rising too rapidly.  If inflationary problems are suspected, the Fed should consider increasing nominal interest rates to slow the economy.  This will allow the Fed to impact key components of GDP.

 

References:

 

Federal Reserve Bank of St. Louis, Federal Reserve Economic Data (FRED), http://www.stls.frb.org/fred/, 1 Mar 2000. 

 

Thomas, Lloyd B. Money, Banking, and Financial Markets. New York: McGraw-Hill Companies, Inc., 1997

 

 

 

 

 

 

 

 

 

 

 

 

 


GDP EXPLAINED BY LAGGED GDP, LAGGED GOVERNMENT, LAGGED INVESTMENT

 

 

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.998

 

 

 

 

 

 

 

R Square

0.996

 

 

 

 

 

 

 

Adjusted R Square

0.995

 

 

 

 

 

 

 

Standard Error

36.029

 

 

 

 

 

 

 

Observations

20

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

3

4646845.667

1548948.556

1193.233

0.000

 

 

 

Residual

16

20769.779

1298.111

 

 

 

 

 

Total

19

4667615.446

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-2682.544

1055.466

-2.542

0.022

-4920.030

-445.057

-4920.030

-445.057

GDP

1.969

0.137

14.394

0.000

1.679

2.260

1.679

2.260

G

-1.307

0.765

-1.708

0.107

-2.928

0.315

-2.928

0.315

I

-1.896

0.382

-4.961

0.000

-2.706

-1.086

-2.706

-1.086