1. Government Revenue Forecasting Perspectives
Tennessee Government Finance Officers Association, Memphis, Tennessee
March 16, 2018
Richard D. Evans, Ph.D., Emeritus Professor of Economics and Real Estate
Director of Revenue Forecasting and Co-Director of the Center for Real Estate Research
Sparks Bureau of Business and Economic Research,
The University of Memphis

2. Revenue Forecasting Perspectives
Government financial officers face serious revenue uncertainties that put many people in jeopardy.
A revenue forecaster can reduce uncertainties--partially.

3. Revenue Forecasting Perspectives
Categories of uncertainty
Revenues at the end of the current fiscal year.
Revenues in the next year, the one that is being budgeted for now.
Long-term trends.
Known and unknown size and timing of changes in tax rates, sharing rules and other political decisions.
How do economic variables drive revenue? But, does information on their current values come too late to use to forecast revenue?
Do forecasting errors for one source of revenue offset errors for others--Or do they compound each other?

4. Revenue Forecasting Perspectives
Suggestions for reducing and describing uncertainty
Make individual forecasts across a set of largest sources of revenue--then sum those for a forecast of overall growth rate in revenue.
It is true that total revenue or total for the set of largest sources is a smoother, more simple forecasting job than many of the individual sources. But, then using the sum of the individual forecasts to forecast the sum actually gives less remaining uncertainty.
Revenue sources excluded from the set do not have the individual power to move the overall growth rate, and their fluctuations offset each other.
Forecast errors on individual revenue sources partially offset each other.

5. Revenue Forecasting Perspectives
Suggestions for reducing and describing uncertainty
Do economic variables drive revenue? YES.
Does information come too late to use to forecast revenue? YES.
Retail Sales Taxes are the most timely data available for short-term forecasts. Employment dominates medium and long-term forecasts.
In tax revenue forecasting “Everything is important.”
We can spot a major fluctuation in an individual revenue source’s data history, then study what happened in that period. We find something--that an obviously powerful variable had shown its strength. But then, that powerful factor remains quiet for almost all of the other periods. Other large fluctuations have other explanations.
Statistical models have trouble measuring the size of the power.
Future changes in the powerful variables are, themselves, difficult forecasting jobs.
Time series statistical models do not explain, but they make plausible forecasts and descriptions of risk.

6. Revenue Forecasting Perspectives
Usually need three different forecasting methods for
1 end-of-year this year, 2 one-year-out, 3 long-term trend.
Uncertainty in an end-of-year forecast is decreases if you can reduce the problem to forecasting “rest-of-year” once you have data on “year- to-date”. Year-to-date data plus a forecast for rest-of-year gives an end- of-year forecast.
Year-to-date upticks predict rest-of-year upticks for some revenue sources; but some upticks cannibalize rest-of-year; some do not help forecast rest-of-year.
But rest-of-year is a smaller target to forecast than end-of-year. Overall uncertainty decreases.

7. Revenue Forecasting Perspectives
Usually need three different forecasting methods or models for end-of-year, 2 one-year-out, 3 long-term trend.
Measure of uncertainty grows for one-year-out forecast, perhaps the year that you are working on a budget -- it is a forecast based on a forecast.
Long-term forecast has so much uncertainty that it is usually embarrassing to even show the measure of uncertainty, the standard error of the forecast, SEF. However, the time series model’s measure of the explosion of uncertainty is realistic. More than three years out, “anything that has happened can happen again, something unheard of could change the world.”

8. Revenue Forecasting Perspectives
Suggestions for reducing and describing uncertainty
Discretionary changes in tax rates and sharing arrangements, annexations and reassessments dominate the histories of the main sources of revenue.
Model the size and timing of past discretionary changes to get the underlying trend, net of those discretionary changes.
Forecast for that trend, plus known future occurrences.
Make a strong and prominent forecasting caveat that future discretionary changes will occur, but the long-term projection does not presume to know their size and timing.

9. Revenue Forecasting Perspectives
Suggestions describing uncertainty
Make descriptions of remaining uncertainty useful.
The SEF yields a high/low interval around a median.
Standard “prediction intervals” or “confidence intervals,” 90%, 95% or 99% intervals, give what will not happen.
Use an interval that makes downside risk realistic.
Low Forecast: probability is 0.25 that end-of-year revenue will be less than or equal to the low forecast.
High Forecast: probability is 0.75 that it will be less than or equal to high forecast.

10. Revenue Forecasting Perspectives
Suggestions describing uncertainty
Make descriptions of remaining uncertainty useful.
Use the SEF to estimate the probability that end-of-year revenue will be short of a specified dollar figure – one that would constitute downside risk that had come to roost.
The probability that revenue from that source or from the group will be below actual revenue the prior year.
The probability that revenue will be below the number used in budgeting calculations.

11. Revenue Forecasting Perspectives
What if your data history is flawed or too short for a statistical estimation?
Make subjective forecasts, with subjective high/low intervals and subjective probabilities for downside risk.
Subjective probability statements have full mathematical credibility as a “degree of belief.”
Make a subjective forecast, even if you have good data.
If my statistical model disagrees, I would consider it evidence against my statistical model.

12. Long-Term Job Growth Drives Revenue
Count job growth locally over 4-5 year across important industry sectors.
Account for each sector’s growth as coming from
growth in the national economy overall and
that individual sector’s differential in national growth relative to overall growth.
“What is special” about the local area is the difference between a sector’s total growth and the part from national growth norms.

14. Long-Term Job Growth Drives Revenue
If the sum of the sector specific addition to the national growth is positive (middle column), then the local economy turned out to have had a favorable mix in the first year.
You Add: Why did that happen? Is that going to be true in the future?
If the Special to the Local (second to the right) figure for a sector is positive, then the local area had a comparative advantage over other counties in the nation for that sector.

15. Long-Term Job Growth Drives Revenue
Property Tax growth is obviously tied to Construction employment and growth.
Sales Tax growth is closely tied to Retail employment.
All revenue in the long-term comes from job growth, even the capacity to raise tax rates.

16. Long-Term Job Growth Drives Revenue
Explanations of Shift Share Analysis
Helen Thompson October 2016
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Kimberly Goodwin February 2018
analysis/
“Free” calculator from esri