U.S. Tax Revenues and U.S. Tax Revenues and Policy ImplicationsPolicy Implications

A Time Series ApproachA Time Series ApproachGroup C:Group C:

Liu HeLiu HeGuizi LiGuizi Li

Chien-ju LinChien-ju LinLyle Kaplan-ReinigLyle Kaplan-Reinig

Matthew RouthMatthew RouthEduardo VelasquezEduardo Velasquez

OutlineOutline

The ObjectivesThe Objectives

The DataThe Data

The TechniqueThe Technique

The ResultsThe Results

The ConclusionsThe Conclusions

The ObjectivesThe Objectives

Model & forecast U.S. tax revenues Model & forecast U.S. tax revenues To better estimate future governmental To better estimate future governmental budget projectionsbudget projectionsTo understand tax policy implications To understand tax policy implications surrounding those projections.surrounding those projections.

The Data – Total RevenueThe Data – Total RevenueQuarterly data from 1988 – 2007Quarterly data from 1988 – 2007

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88 90 92 94 96 98 00 02 04 06

TOTAL

The Data – Total RevenueThe Data – Total Revenue

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Series: TOTALSample 1988:1 2007:4Observations 80

Mean 195622.7Median 187242.0Maximum 355964.0Minimum 101193.0Std. Dev. 62349.56Skewness 0.613265Kurtosis 2.609936

Jarque-Bera 5.521755Probability 0.063236

Histogram for Total Revenue

The Data – Total RevenueThe Data – Total RevenueCorrelogram & Unit Root Test for Total Revenue

The TechniqueThe Technique

Natural logging Natural logging Seasonal differencingSeasonal differencingAutoregressiveAutoregressiveMoving averageMoving averageAutoregressive conditional Autoregressive conditional heteroskedasticity (ARCH) test – not heteroskedasticity (ARCH) test – not significantsignificant

Final Estimated ModelFinal Estimated ModelDependent Variable: SD2LNTOTAL

Method: Least Squares

Date: 05/31/08 Time: 14:42

Sample (adjusted): 1989:1 2007:4

Included observations: 76 after adjusting endpoints

Convergence achieved after 11 iterations

Backcast: 1988:2 1988:4  Variable Coefficient Std. Error t-Statistic Prob.

C 0.027592 0.002803 9.842504 0.0000

AR(2) -0.989002 0.023627 -41.85847 0.0000

MA(1) 0.324823 0.113990 2.849577 0.0057

MA(2) 0.373330 0.116301 3.210037 0.0020

MA(3) 0.259891 0.115912 2.242136 0.0281

R-squared 0.917294 Mean dependent var 0.027532

Adjusted R-squared 0.912635 S.D. dependent var 0.084414

S.E. of regression 0.024951 Akaike info criterion -4.480293

Sum squared resid 0.044201 Schwarz criterion -4.326956

Log likelihood 175.2512 F-statistic 196.8656

Durbin-Watson stat 1.944467 Prob(F-statistic) 0.000000

Inverted MA Roots .11+.69i .11 -.69i -.54

Correlogram of Final ModelCorrelogram of Final Model

Government RevenueGovernment Revenue

Growth in Growth in revenue partly revenue partly from cuts in:from cuts in:• Top marginal Top marginal

income taxincome tax• Capital gains Capital gains

taxtax• Corporate taxCorporate tax

Corporate Income Tax Receipts as Corporate Income Tax Receipts as a Share of GDP, 1985–2007a Share of GDP, 1985–2007

Corporate Income Tax Cuts Boost Federal RevenuesCorporate Income Tax Cuts Boost Federal RevenuesThe economy has boomed since the 2003 tax cuts, The economy has boomed since the 2003 tax cuts, leading to the highest level of corporate tax receipts in leading to the highest level of corporate tax receipts in over 20 years.over 20 years.

The Data – Corporate TaxThe Data – Corporate TaxTrace for Corporate Income TaxTrace for Corporate Income Tax

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88 90 92 94 96 98 00 02 04 06

CORPINC

The Data – Corporate TaxThe Data – Corporate Tax

Histogram for Corporate Income TaxHistogram for Corporate Income Tax

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Series: CORPINCSample 1988:1 2007:4Observations 80

Mean 8464.513Median 7512.500Maximum 19748.00Minimum 4399.000Std. Dev. 3340.261Skewness 1.368877Kurtosis 4.962537

Jarque-Bera 37.82283Probability 0.000000

Correlogram for Corporate Income Tax

The Data – Corporate TaxThe Data – Corporate Tax

Forecast: SD2LNTOTAL, 2008:1 Forecast: SD2LNTOTAL, 2008:1 –– 2008:4 2008:4

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05:1 05:3 06:1 06:3 07:1 07:3 08:1 08:3

SD2LNTOTALFORECASTOT

FORECASTOT+2*SEFOTFORECASTOT-2*SEFOT

RECOLOR SD2LNTOTAL

Sample: 2007:03 2007:04Genr lntotalf = lntotalSample: 2007:03 2008:04Genr lntotalf = forecastot + lntotalf(-2) (undo seasonal difference)Genr totalforecast = exp(lntotalf) (undo natural logarithm)

The ResultsThe ResultsFour quarter forecast, with historical dataFour quarter forecast, with historical data

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TOTAL TOTALFORECAST

The ConclusionsThe Conclusions

The model provides a satisfactory account for The model provides a satisfactory account for the increasing tax revenues of the United States the increasing tax revenues of the United States government over the past twenty years. government over the past twenty years. Increasing variation is not significant and simply Increasing variation is not significant and simply represents the various fluctuations from a represents the various fluctuations from a number of tax revenue generating sources. number of tax revenue generating sources. The spike in revenues following the 2001-02 The spike in revenues following the 2001-02 economic downturn can be explained by the economic downturn can be explained by the economic growth led by corporate expansion economic growth led by corporate expansion attributed to the cutbacks in capital gains tax as attributed to the cutbacks in capital gains tax as well as the corporate tax rate. well as the corporate tax rate.