After gathering together the newspoll data from 1985 to the present and aggregating those into a monthly series, I thought I’d build a quick election model regression.The series used are a mix of newspoll series and economic series sourced from the RBA.These were:

GOVPRIMARY: The primary vote for the government according to the newspoll.

OPPRIMARY: The primary vote for the Opposition according to newspoll

PMDISAT:The dissatisfaction rating for the Prime Minister

PMDISAT(-1):The dissatisfaction rating for the Prime Minister lagged by one period.

OPSAT: The satisfaction rating of the Leader of the Opposition

GST: A dummy variable for the GST where it equals 1 for the period it has been operating and zero for the periods before it was introduced.

CAMP: A campaign dummy variable which equals 1 the month before the election and zero otherwise.

INT: The standard bank variable home loan interest rate.

AR(1):A first order serial correlation termm

MA(1):A first order moving average error component.

The model became:

GOVPRIMARY = 64.8207675 + 1.374899313*INT – 0.565567558*OPPRIMARY – 0.128756676*PMDISAT + 0.05746403116*OPSAT – 0.1212621617*PMDISAT(-1) – 1.720345869*GST + 2.518176146*CAMP + [AR(1)=0.8682231246,MA(1)=-0.6409529542,BACKCAST=1996:03]

Using the quaint little Eviews, the more important technical bits were:

 

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/17/07 Time: 00:40
Sample: 1996:03 2007:05
Included observations: 135
Convergence achieved after 36 iterations
Backcast: 1996:02
Variable Coefficient Std. Error t-Statistic Prob.
C 64.82077 3.467005 18.69647 0.0000
INT 1.374899 0.470391 2.922884 0.0041
OPPRIMARY -0.565568 0.075075 -7.533329 0.0000
PMDISAT -0.128757 0.028567 -4.507237 0.0000
OPSAT 0.057464 0.024617 2.334312 0.0212
PMDISAT(-1) -0.121262 0.027496 -4.410223 0.0000
GST -1.720346 0.743948 -2.312453 0.0224
CAMP 2.518176 0.735535 3.423599 0.0008
AR(1) 0.868223 0.067859 12.79458 0.0000
MA(1) -0.640953 0.115439 -5.552327 0.0000
R-squared 0.818619 Mean dependent var 43.11037
Adjusted R-squared 0.805559 S.D. dependent var 3.360798
S.E. of regression 1.481958 Akaike info criterion 3.695793
Sum squared resid 274.5250 Schwarz criterion 3.910998
Log likelihood -239.4660 F-statistic 62.68402
Durbin-Watson stat 1.847315 Prob(F-statistic) 0.000000

I spent alot of time checking for all sorts of relationships, but what was interesting was the CAMP result.The Coalition get a 2.5% boost to their primary vote in the month before the election and the GST hurt there primary vote and it has not yet recovered.

Another interesting phenomena that I came across was that the Coalition appears to get a boost in their primary vote when economic bad news is occurring, and a decline in their primary vote when the economic sunshine comes around.When the Coalition is in government:
– a 1% increase in unemployment has walked hand in hand with a 0.96% increase in their primary vote.Likewise a 1% decrease in unemployment has walked hand in hand with a 0.96% decrease in their primary vote.

– a 1% increase in the interest rate level has walked hand in hand with 1.3% increase in their primary vote.Likewise a 1% decrease in the interest rate level has walked hand in hand with 1.3% decrease in their primary vote.

When the Coalition is in opposition:

– a 1% increase in unemployment has walked hand in hand with a 1.6% increase in their primary vote.Likewise a 1% decrease in unemployment has walked hand in hand with a 1.6% decrease in their primary vote.

– a 1% increase in the interest rate level has walked hand in hand with 0.4% increase in their primary vote.Likewise a 1% decrease in the interest rate level has walked hand in hand with 0.4% decrease in their primary vote.

When times are tough, the people run to the Coalition and economic sunshine kills them.See for yourself:

 

Dependent Variable: COALITION
Method: Least Squares
Date: 05/17/07 Time: 01:03
Sample(adjusted): 1986:01 2007:05
Included observations: 257 after adjusting endpoints
Convergence achieved after 8 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 27.46739 3.446556 7.969518 0.0000
DGOVLIB*UNEMP 0.960052 0.419546 2.288308 0.0230
DGOVLIB*INT 1.325942 0.437369 3.031630 0.0027
DGOVALP*UNEMP 1.562745 0.296463 5.271298 0.0000
DGOVALP*INT 0.385121 0.166081 2.318868 0.0212
AR(1) 0.671602 0.047794 14.05187 0.0000
R-squared 0.600228 Mean dependent var 44.33366
Adjusted R-squared 0.592265 S.D. dependent var 3.318514
S.E. of regression 2.119009 Akaike info criterion 4.362844
Sum squared resid 1127.040 Schwarz criterion 4.445702
Log likelihood -554.6254 F-statistic 75.37167
Durbin-Watson stat 2.145189 Prob(F-statistic) 0.000000

I bet the government is praying for a recession.

 

 

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