Download What is public sector forecast

1. What is public sector forecast
1.
Users: Policy makers, planners, contractors, budget
officials
Level: Federal, State, Local
Policy Type:
Fiscal: Taxes (Income, Business,
Consumption/Sales, Property…)
Spending (Health, Education, Infrastructure,
Debt…)
Monetary: Inflation, Economic Growth
2.
Type of Forecast
Macroeconomic (National Economy, Local/Regional
Economy, Price, Labor Market, Financial Market…)
Micro-simulation (Income Distribution, Individual
Consumption behavior, Demographics…)
Impact Analysis (Policy Changes and Economic
Impact)
Specific Forecasts (Tax, Fee, Lottery, Health,
Education, Tax Expenditures)
3.
Techniques
a. Time Series Model
b. Structural Econometric Model
c. Time Series Structural Model (VAR)
d. Panel Data Model
e. Micro Simulation Model
4.
Forecast Risks
Loss Function (what will happen when forecasts
are wrong)
Symmetrical vs. Asymmetric loss functions
The key issue when evaluate forecasting errors
5.
Differences between private and public forecasts
From a technical point view (i.e., understanding
data generating mechanism, fitting models,
diagnostic checking ) forecasters are the same, but
differences exist
a. User/subjects
b. Forecasting interval
c. Loss Function
d. Transparency
2. Importance of the government sector
PowerPoint presentation
1. Government provides important services:
Health Care, Social Security and Insurance, Education, Roads/Bridges,
Public Safety, National Security/Defense…
2. Look at the size
a. Government revenue and spending as share of GDP
i. Increase over time
ii. Cyclical patterns (why?)
b. Revenue Level
Trend up, declined only when there were law changes or
economic downturn
3. Spending Level
Going up, up, up… (A technical issue: neither mean nor
variance is constant)
4. State and Local Government: Spending vs. Revenue
Balanced in general
5. Federal Government Spending Forecast by CBO
6. Employment Share
3. Experts forecast vs. model-based forecast
Define a forecast
Suppose we have observed a macro data series over time (say government
spending), and we can write is as
xt where t  1, 2,...T , T denotes the current period (or the last observation).
Say we have information set T , so we can write a forecast for xt as
xT i | T i  1,2,...n
Expert forecast: Experience-based. Given T to guess what to come: For
example, during good times xt will grow by 1.5% while during downturns
xt will decline by 1.0%.
Advantage: Quick (rule of thumb), low cost
Disadvantage: Limited, hard to evaluate (for example, what went wrong.
animal spirit?)
Model-based forecast:
Advantages:
Systematic, making forecasts evaluation easier, can deal
with complex systems
Example:
Or
xt  1 xt 1   2 y1t   3 y2t   3 y3t  ...  ut
Federal Funds Rate ( r )
 r  0.0632( r r  )  0.0313 ( -  )  0.122  r t 1  0.354  r t 2  0.129  r t 3  0.0152  r t -4
t
t -1
t -1
(0.0425 )
(0.0692)
(0.0849 )
(0.0849 )
 0.360
(0.0946 )
GDP Deflator ( )
(0.0856 )
(0.0805 )
 0.0839  t -1  0.199  t -2  0.110  t -3  0.0773   t -4
(0.0858 )
( 0.0798 )
(0.0688 )
(0.0896 )


t -1
0.105
(0.139 )


 0.202
t -2
(0.136 )
t -3
 0.0435
(0.0949 )

t -4
  -0.0323 ( r r  )  0.0758 ( -  )  0.215  r t 1  0.00934  r t 2  0.0167  r t 3  0.0500  r t -4
t
t -1 (0.0834)
(0.0153)
t -1 (0.102)
 0.449 
(0.108 )
 0.0859
(0.102)
t -1

(0.114)
 0.346 
(0.103 )
 0.0145
t 1
(0.167 )
(0.103 )
t -2

 0.256 
t 2
(0.0970 )
(0.0963 )
t -3


0.0835
(0.163 )
 0.0509  
(0.0830 )
 0.0229 
t -3
(0.114 )
t 4
t 4
Percentage Output Gap (  )
 0.0485( -  )  0.109 
t 1 (0.0572)
t -1 (0.0703 )
 = 0.0393 ( r r  )
t
(0.0352)
 0.137
(0.0741)
 1.153
(0.0782)
r t 1 


t 1
t 1
0.314
(0.0702)
 r t 2  0.0779  r t 3  0.0985  r t -4
 0.120
(0.0710 )

 0.0244 
(0.115 )
(0.0666 )
(0.0708 )
t 2
t 2
 0.0444

(0.0660 )
0.195
(0.112)


t 3
t 3
 0.00889
(0.0569 )
 0.00411
(0.0785 )
Note: The subscript '  ' is used to indicate the end-point condition.
Disadvantages:
Overhead costs: computer, programmers, training
Limitations: the local vs. global trend issue


t 4
t 4