u.s. tax revenues and policy implications a time series approach
DESCRIPTION
U.S. Tax Revenues and Policy Implications A Time Series Approach. Group C: Liu He Guizi Li Chien-ju Lin Lyle Kaplan-Reinig Matthew Routh Eduardo Velasquez. Outline. The Objectives The Data The Technique The Results The Conclusions. The Objectives. - PowerPoint PPT PresentationTRANSCRIPT
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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200000
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400000
88 90 92 94 96 98 00 02 04 06
TOTAL
The Data – Total RevenueThe Data – Total Revenue
0
2
4
6
8
120000 160000 200000 240000 280000 320000 360000
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
4000
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20000
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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10
4000 6000 8000 10000 12000 14000 16000 18000 20000
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
-0.2
-0.1
0.0
0.1
0.2
0.3
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
50000
100000
150000
200000
250000
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350000
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88 90 92 94 96 98 00 02 04 06 08
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.