An Electoral Vote Forecast Formula: Simulation or Meta-analysis Not Required
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Richard Charnin
Oct 31, 2011, 10:22:37 PM10/31/11
to Election Integrity
An Electoral Vote Forecast Formula: Simulation or Meta-analysis Not
Required
Richard Charnin (TruthIsAll)
Oct.31, 2011
It’s very surprising that election forecasting blogs and academics who
use the latest state polls as input to their models don’t apply basic
probability, statistics and simulation concepts in forecasting the
electoral vote and corresponding win probabilities.
http://www.richardcharnin.com/ProjectingEV.htm
A meta-analysis or simulation is not required to calculate the
expected electoral vote. Of course the individual state projections
will depend on the forecasting method used. But the projection method
is not the main issue here; it’s how the associated win probabilities
are used to calculate the expected EV, win probability and frequency
distribution.
Calculating the expected electoral vote is a three-step process:
1. Project the 2-party vote share V(i) for each state(i) as the sum of
the poll share PS(i) and the undecided voter allocation UVA(i):
V (i) = PS(i) + UVA(i)
2. Calculate the probability P(i) of winning state (i) given the
margin of error (95% confidence):
P (i) = NORMDIST (V(i), 0.5, MoE/1.96, true)
3. Calculate the total expected electoral vote EV as the sum:
EV = ∑ P(i) * EV(i), for i = 1,51
The 2004 Election Model allocated 75% of the undecided vote to Kerry
and projected that he would have 337 electoral votes (99% win
probability) with a 51.8% two-party vote share. The unadjusted,
pristine state exit poll aggregate provided by exit pollsters Edison-
Mitofsky 3 months after the election indicated that Kerry won 52.0% of
the vote with an identical 337 electoral votes.
The challenger is expected to win the majority (60-90% UVA) of the
undecided vote, depending on incumbent job performance. Gallup
allocated 90% of undecided voters to Kerry in their final projection,
pollsters Zogby and Harris: 75-80%. The National Exit Poll indicated
that 65% of undecided voters broke for Kerry. Bush had a 48% approval
rating on Election Day 2004.
After calculating the individual state probabilities, we can calculate
the EV win probability. The best, most straightforward method is Monte
Carlo simulation. This technique is widely used in many different
applications when an analytical solution is prohibitive and is
perfectly suited for calculating the EV win probability. The Election
Model uses a 5000 election trial simulation. The win probability is
the total number of winning election trials/5000.
The average electoral vote is calculated for the 5000 election trials.
Of course, the average will only be an approximation to the
theoretical value based on the summation formula. But the Law of Large
Numbers (LLN) applies: the EV average and median are usually within
one or two electoral votes of the theoretical mean. The close match
between the Monte Carlo EV simulation average, median and theoretical
expected mean is proof that 5000 election trials are more than
sufficient. Meta-Analysis is an unnecessarily complex method and
overkill for calculating the expected Electoral Vote when it can be
calculated by the simple summation formula given above.
Professor Wang had it right in his 2004 Meta model when he projected
that Kerry would win over 300 electoral votes and had a 98% win
probability. But he was wrong to suggest after the election that Bush
won the undecided vote – and that explained why his final forecast was
“wrong”. Just like AAPOR, the media pundits and political scientists,
he never considered that Election Fraud was the cause. But
overwhelming statistical and other documented evidence indicates that
the election was stolen, just like it was in 2000.
The 2008 Election Model includes a sensitivity (risk) analysis of
five Obama undecided voter (UVA) scenario assumptions ranging from
40-80%, with 60% as the base case. This enables one to view the
effects of the UVA factor variable on the expected electoral vote and
win probability. Electoral vote forecasting models which do not
provide a risk factor sensitivity analysis are incomplete.
Required
Richard Charnin (TruthIsAll)
Oct.31, 2011
It’s very surprising that election forecasting blogs and academics who
use the latest state polls as input to their models don’t apply basic
probability, statistics and simulation concepts in forecasting the
electoral vote and corresponding win probabilities.
http://www.richardcharnin.com/ProjectingEV.htm
A meta-analysis or simulation is not required to calculate the
expected electoral vote. Of course the individual state projections
will depend on the forecasting method used. But the projection method
is not the main issue here; it’s how the associated win probabilities
are used to calculate the expected EV, win probability and frequency
distribution.
Calculating the expected electoral vote is a three-step process:
1. Project the 2-party vote share V(i) for each state(i) as the sum of
the poll share PS(i) and the undecided voter allocation UVA(i):
V (i) = PS(i) + UVA(i)
2. Calculate the probability P(i) of winning state (i) given the
margin of error (95% confidence):
P (i) = NORMDIST (V(i), 0.5, MoE/1.96, true)
3. Calculate the total expected electoral vote EV as the sum:
EV = ∑ P(i) * EV(i), for i = 1,51
The 2004 Election Model allocated 75% of the undecided vote to Kerry
and projected that he would have 337 electoral votes (99% win
probability) with a 51.8% two-party vote share. The unadjusted,
pristine state exit poll aggregate provided by exit pollsters Edison-
Mitofsky 3 months after the election indicated that Kerry won 52.0% of
the vote with an identical 337 electoral votes.
The challenger is expected to win the majority (60-90% UVA) of the
undecided vote, depending on incumbent job performance. Gallup
allocated 90% of undecided voters to Kerry in their final projection,
pollsters Zogby and Harris: 75-80%. The National Exit Poll indicated
that 65% of undecided voters broke for Kerry. Bush had a 48% approval
rating on Election Day 2004.
After calculating the individual state probabilities, we can calculate
the EV win probability. The best, most straightforward method is Monte
Carlo simulation. This technique is widely used in many different
applications when an analytical solution is prohibitive and is
perfectly suited for calculating the EV win probability. The Election
Model uses a 5000 election trial simulation. The win probability is
the total number of winning election trials/5000.
The average electoral vote is calculated for the 5000 election trials.
Of course, the average will only be an approximation to the
theoretical value based on the summation formula. But the Law of Large
Numbers (LLN) applies: the EV average and median are usually within
one or two electoral votes of the theoretical mean. The close match
between the Monte Carlo EV simulation average, median and theoretical
expected mean is proof that 5000 election trials are more than
sufficient. Meta-Analysis is an unnecessarily complex method and
overkill for calculating the expected Electoral Vote when it can be
calculated by the simple summation formula given above.
Professor Wang had it right in his 2004 Meta model when he projected
that Kerry would win over 300 electoral votes and had a 98% win
probability. But he was wrong to suggest after the election that Bush
won the undecided vote – and that explained why his final forecast was
“wrong”. Just like AAPOR, the media pundits and political scientists,
he never considered that Election Fraud was the cause. But
overwhelming statistical and other documented evidence indicates that
the election was stolen, just like it was in 2000.
The 2008 Election Model includes a sensitivity (risk) analysis of
five Obama undecided voter (UVA) scenario assumptions ranging from
40-80%, with 60% as the base case. This enables one to view the
effects of the UVA factor variable on the expected electoral vote and
win probability. Electoral vote forecasting models which do not
provide a risk factor sensitivity analysis are incomplete.
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