A tale of two primaries

The above graph is the primary votes of each party in the 12 months leading up to the election as determined by Newspoll. This assumes of course that Howard will call an election in November.

But what about the claims that Labor always does this – they get ahead and then Howard reels them in as we approach the election:

Business as usual?

Twaddle.

Election Prediction Model 1

I plan to have 3 or 4 election prediction models by the first few days into the campaign and as we approach I’ll release them in draft form and continually make modifications as Judgement Day nears. Some will be pure economics, some will be pure opinion polls, some a mix and one will be out of left field.

Here’s the first draft of Election Prediction Model 1.

Note, all political variables come from Newspoll

For the Coalition:

GOVPRIMARY = dependent variable

GOVPRIMARY(-1) = the governments primary vote in the previous month

PMSAT = Satisfaction rating of the PM

IPDI-IPDI(-24) = interest payments to disposable income as a percentage minus the same measure 24 months previous.

INT = interest rates defined as the standard bank variable loan rate

GST is a dummy variable equaling 1 for each period when the GST has been in and zero otherwise.

RUDD = equals the “RUDD EFFECT” dummy variable equaling 1 when Rudd has been ALP leader and zero otherwise

CAMP = campaign dummy variable that measures 1 in the period the month before the election and zero at all other times.

The regression results tested over the Howard government period were:

 

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/25/07 Time: 17:41
Sample: 1996M03 2007M05
Included observations: 135
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	12.94913	2.645991	4.893867	0.0000
GOVPRIMARY(-1)	0.239182	0.063741	3.752377	0.0003
PMSAT	0.269616	0.028716	9.389111	0.0000
IPDI-IPDI(-24)	-0.493343	0.225858	-2.184312	0.0308
INT	1.126645	0.230097	4.896392	0.0000
GST	-1.293712	0.439106	-2.946242	0.0038
RUDD	-3.517838	0.836297	-4.206447	0.0000
CAMP	1.808813	0.917797	1.970820	0.0509
R-squared	0.730078	Mean dep var		43.110
Adjusted R-squared	0.715201	S.D. dep var		3.3607
S.E. of regression	1.793543	Akaike		4.0636
Sum squared resid	408.5332	Schwarz		4.2358
Log likelihood	-266.2993	F-statistic		49.072
Durbin-Watson stat	2.130125	Prob(F-statistic)		0.000000

For the ALP Primary Vote:

 

The variables are:

Labor = The Dependent Variable

Labor(-1) = The ALP primary vote in the month previous.

OPSAT-OPDISAT = Oppositions satisfaction rating minus their dissatisfaction rating

IPDI-IPDI(-12) = the interest payments to disposable income percentage minus that same measure 12 months previous.

GOVPRIMARY = governments primary vote.

The regression results tested over the Howard government period were:

 

 

Dependent Variable: LABOR
Method: Least Squares
Date: 05/25/07 Time: 17:10
Sample: 1996M03 2007M05
Included observations: 135
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	33.59647	3.990479	8.419157	0.0000
LABOR(-1)	0.514651	0.059088	8.709883	0.0000
OPSAT-OPDISAT	0.050997	0.008650	5.895986	0.0000
IPDI-IPDI(-12)	0.335900	0.215453	1.559045	0.1214
GOVPRIMARY	-0.335420	0.051636	-6.495840	0.0000
R-squared	0.747402	Mean dep var		39.986
Adjusted R-squared	0.739630	S.D. dep var		3.3356
S.E. of regression	1.702045	Akaike		3.9378
Sum squared resid	376.6042	Schwarz		4.0454
Log likelihood	-260.8063	F-statistic		96.163
Durbin-Watson stat	1.915282	Prob(F-statistic)		0.0000

In order to forecast this into November, I first had to forecast into November any variables whose values these two primary vote models are reliant upon.

For “interest payments to disposable income” I used a conservative Holt-Winters method non-seasonal forecast to arrive at a final November forecast of 12.03 for this variable.

Interest rates I assumed to remain the same between here and November.Likewise PM satisfaction ratings I assumed would remain the same as they are now.Opposition satisfaction ratings I assumed would slowly decay starting from 68 today and ending at 60 by November, likewise the opposition dissatisfaction rating was assumed to slightly solidify starting from 18 today and increasing to 24 by November.These assumptions for satisfaction were based around previous movements of these elections variables in previous elections which I modelled separately.

Having ascertained the forecast values for my variables, I then forecast these two primary vote models into November using these values and the above regressions.

The results were:

Coalition on primary vote of 39.7

ALP primary vote of 45.6

I then assumed a preference distribution of 45/55 Coalition/ALP, which is slightly lower than usual, but chosen because of the higher ALP primary vote.

The final two party preferred forecasts became:

ALP 53.7 Coalition 46.3

Using Bryan of OzPolitics fames’ Election Calculator, the end results assuming a uniform swing would be the ALP picking up 86 seats and forming government.

To explain how the dummy variables work, the CAMP variable measures and accounts for the Coalitions better performance during election campaign in terms of improving their primary vote during the campaign.RUDD is a variable that measures and accounts for the observable boost that the Rudd leadership has on the ALPs primary vote.The GST variable measures and accounts for the consistent long term decay in the Coalitions primary vote since the implimentation of the GST.

Now for all you econometric type nit pickers out there, yes there is a small serial correlation in the error terms. This will be ARCHed out later closer to the election. Likewise, there are VAR opportunities, this model may well go up that route.

So what does the model forecast each month until November? 2 Party Preferred scale is on the right, Primary Vote scale is on the left:

Why Howard is rooted in one simple graph

What this graph highlights is the Oppositions primary vote as determined by Newspoll and the percentage of household disposable income that goes towards interest payments from 1986 to today.Notice how they move together?

Their intimacy is fundamentally important to the electoral fortunes of both parties at the coming election.

There are three things that individually or in combination increase the interest payments to disposable income percentage.

1. Interest rate increases.
2. Asset prices increase (meaning the debt needed to be taken out to purchase those assets increases)
3. Disposable income decreases.

For every household in Australia that has a mortgage, at least two, but in some cases all three of these things have occurred in the last 5 years.

Interest rates have increased four times since the last election. Asset prices (but importantly to us here, house prices) have dramatically increased over the last 7 years, thus the size of the debt required to purchase a house has increased, and these two things combined have dramatically increased the servicing costs of that debt you need to buy a house. That is how the size of the interest payment in the ‘interest payment to disposable income’ ratio has increased.

The final part, decreasing disposable income, is where Workchoices is impacting directly upon some households, and where there is a perception that it is threatening to impact upon some other households.

Because the government won’t release the AWA data, we are working in an information poor environment here. However, some things have become apparent.

Some lower income earners are getting reduced take home pay as a result of overtime and penalty rates being replaced with small increases in the basic hourly rate, a key consequence of Workchoices. For many two income households, the second income earner falls squarely into this group. That reduction in take home pay has reduced their disposable income. The interest payments to disposable income ratio is increasing as a result of all three effects hitting them.

This is fundamentally important to households, because households judge “the state of the economy” by what they experience first hand. It is even more fundamentally important to median income households where they don’t enjoy much wiggle room to begin with when it comes to their disposable income.

When interest payments start taking up larger proportions of their disposable income, the actual income that households have left available for discretionary spending decreases. That discretionary spending is what defines the lifestyle quality for many many people. Reducing the money available for discretionary spending effectively reduces the self-perceived standard of living for those households.

This is why Howard’s claims of superior economic management have, and will continue to fall on deaf ears for a large part of the electorate, and it’s why the more he uses that claim, the more alienated that part of the electorate will become.

They don’t see superior economic management, they see reduced discretionary spending, they see their lifestyle becoming unaffordable, they see their costs of living becoming larger as a percentage of their discretionary income, and they see lower living standards (even if its only self perceived) as a result.

They see interest rate rises being a broken promise, house price increases being a pain in the arse and Workchoices being not only an assault on their living standards, but probably the final straw.

When governments tell these people that “they’ve never had it so good” when their personal experience runs to the contrary – they not only stop listening to the government, they become hostile too it.

And this group is large enough to not only change government come election time, they are well and truly large enough to create landslides. If you add higher petrol prices into this mix, the likelihood of a political bloodbath at the next election is almost a foregone conclusion.

Seats with large proportions of middle income, two income households with 1.5 jobs will topple like dominoes.

The interest rate payments to disposable income raw data came from the RBA here:http://www.rba.gov.au/Statistics/Bulletin/B21hist.xls

The Opposition primary vote data came from http://Newspoll.com.au

UPDATE : Thanks to Andrew for pointing out that I cant add up.The graphs are now much more reflective of observable reality.

 

Here’s how the Government Primary Vote looks against Interest Payments to Disposable Income, with the interest payments scale inverted.

UPDATE II:

The brilliant Simon Jackman has a delicious stats based piece on housing using a clever mortgage stress variable in a regression on 2004 election outcomes over HERE. Well worth a read if you’re that way inclined

 

 

Askimet got shirty

If your comments were lost, sorry about that folks.The Askimet anti-spam filter hasn’t been taking its meds.

A real Murray Darling Water Plan

This whole back of the envelope, “lets piss $10 billion up the wall on the Murray without having a clue what we’re doing” business is…welll. Words don’t do justice to the broad general stupidity of it.

Let’s create a real Murray Darling water plan that deals with appropriate allocation, prevents over-allocation, guarantees that water flows to its highest value use, is properly market priced and builds sustainable environmental flows into the system as a foundation stone.

First off, you need two bodies.

The first body, some independent agency similar to the Reserve Bank of Australia that decides on the actual quantity of water to be allocated in the Murray-Darling

Basin. Let’s call this body the Murray-Darling Water Authority (MDWA)

The second body, some water trading statutory monopoly that can act as a secondary trading market for that water, akin to the ASX. Let’s call this body the Murray Darling Water Exchange (MDWE)

The key point to maximising the commercial value that can be generated by a finite resource, in this case water, is to let it be priced according to what users are willing to pay for it. et water flow to it’s highest value use.

This is easily achieved. Every year the MDWA could determine how much water can be pulled out of the basin for commercial use based on the prevailing science. This then allows for water release to be based on a very simple formula:

Commercial water volume = Total River Flow – Environmental Flow – Town Water Requirements.

The rights to this commercial water can be sold each year, via two primary market sales.

The first is the sale of rights, by megalitre, for use in the following year.

The second is the sale of rights, by megalitre per year, for use in the next three years.

This provides a primary market for short-term use (the single year rights sale) and also for longer term use (the three year rights sale) which gives the water market a

capability to pay a premium price for longer term water security. It may even be possible to sell water allocations as a 5 year right as well.

The key to making this work though is the process by which these rights are sold.

Using some simple (and and grossly understated numbers) as an example. Let us say that the MDWA concludes that 6 gigalitres of water can be used for commercial purposes in the next year. So the authority keeps a gigalitre for themselves in their reserve, and tenders the remaining 5 gigalitres to the public for commercial use in the basin.

The way to run the tender is simple, each year everyone who wants to buy water issues a tender to the MDWA saying how much per megalitre they are willing to pay, and how many megalitres they wish to buy.

At the close of the tender, the MDWA then allocates the available water from highest per-megalitre tender to lowest until the available water allocations are exhausted. So let us say that the market put in tenders to buy 9 gigalitres of water, but there are only 5 gigalitres available for sale.

So if the highest price someone put in their tender was $1000 per megalitre, and they wanted 2000 megalitres, then they get 2000 megalitres of the available 5 gigalitre issue at a price of $1000 per ML.

If the next price was $950 per ML and they wanted 1000 ML, then they too get their full desired amount for the price and so the tender continues from the highest per ML tender, right through until all of the 5 gigalitres of water available for issue has been purchased.

Let us say that this process had continued until there was only 30,000 megalitres left in the allocation and the next priced tender was $200 per ML and they wanted 50,000 ML. That last tender gets 30,000ML at $200 per ML and everyone else, the other 4 gigalitres worth of tenders who bid too low miss out. They have no commercial water allocations from the primary market for the year.

The same thing can be done for 3 and possibly even 5 year water allocations.

But all is not lost for those that missed out.

The other body, the Murray Darling Water Exhange, allows farmers that haven’t used, or dont plan to use all of the allocation they purchased, to sell their surplus on a secondary market.If a farmer purchased 1000ML for next year, but heavy rain was experienced in February, reducing their need for that water down to only 700ML, 300ML could be put up for sale by the farmer on the Exchange so that others who originally missed out on water allocations could purchase some on the secondary market, or allow others who underestimated their needs to top up their allocation.

It also would allow the Authority to buy and sell some or all of their reserves on the secondary market according to the needs of environmental flow requirements or instances of demand/supply volatility that could interfere with normal business practices.

It would also allow places like Adelaide or other interested parties to purchase water rights and not use them, essentially paying to increase the flow of the Murray-Darling Basin.

The same system could be used for groundwater and surface water, and would of course, have to be done within catchments – the original water allocations would be sold with catchment specific requirements. People at the upper end of a tributary feeder river for the basin couldn’t buy rights from someone with Darling River frontage, but they could certainly sell their rights to them if they are further down the river system.

This way, not only would water flow to its highest value use, there are mechanisms that allow proper secondary trading, options to pay for water guarantees longer term, a mechanism to allow the Authority to accommodate environmental effects like floods and droughts as well as to be able to smooth out damaging volatility in the spot price.

The beauty of such a system, apart from letting commercial water use flow to its highest value use, is that it also allows for the development of water futures markets. That would become, over time, essentially an important water insurance and risk market that would let the widespread information that water users have be be disseminated into price signals.

The key issue though is back at the beginning with the body that determines the amount of water to be released through primary allocations – the Murray Darling Water Authority. It needs to be independent, free from the intervention of the agrarian socialist lobby come election time. The money raised from the primary water auctions would allow the Authority to be self funded, as well as allow the authority to fund efficiency measures in terms of water infrastructure development within the catchment. The key role of the MDWA would actually be environmental flow determination and determining the allocation for town water use by towns along the catchment, as the commercial allocation is simply a function of the total flows minus these two requirements.As long as environmental flow determination was done as an open process, with public hearings, submission mechanisms and well funded science – we would be able to solve water overallocation once and for all.

The only problem is the requirement to buy back every single license in the catchment first, and the funding of some structural adjustment program to assist people to get out of the industry, or relocate to where water is less scarce. But that is inherently “doable”, it’s just a matter of political will.

Farmers Dog Update

Some of you may have heard the story of my 68 year old farming neighbour. We got talking about Howard a month or so back and he said (quote):

“I had a dog like Howard once, you wouldn’t trust it as far as you could kick it but it rounded up the cattle. Then it got old so I shot it and got a new one”.

 

If this is a metaphor on the governments’ current predicament and the fortunes of Rudd in the mind of the electorate, things have taken another nasty turn. Yesterday I went over to my neighbours’ house and caught the new dog asleep on the floor of his lounge room. I asked him “Hey, I didn’t think you let your dogs inside”.

His reply: “I’ve kinda got attached to this one”

 

Todays music to blog by archive

This is where I’m going to stick all the previous Todays Music to Blog By links.So if you missed one, or forgot the name of one of them, this is where you’ll find them.

Britt Black: Dead Vanity:

The Halo Friendlies The Eyeliners

The Cocktail Slippers Tsunami Bomb

The Dollyrots The Donnas

The Wreckers Miranda Lambert

Paramore Nitocris

Fabulous Disaster Rocket

The Unlovables Rebel Girl Rock

Gore Gore Girls

Beltway Theory or JTSAU?

 

Paul Kelly tells us that there are duelling banjo’s realities in the electorate, there are he tells us, “inside the beltway” theories suggesting that the government must win because the Canberra press gallery says so the economy is so good, and there is the “outside the beltway” shmucks…. that’d be you and me, who seem to be ignoring the journos wisdom and giving the Newspoll folks incorrect answers.

So let’s test the “Beltway Theory”.

Let’s create an index of economic strength so we can test it against voting behaviour as defined by Newspoll.

So to start, lets create a basic index that represents GDP growth, the interest rate as defined by the standard variable home loan rate and unemployment. GDP growth is easy, as it increases the index will move up, as it decreases the index will move down. All we need to do is weight it.

Interest rates and unemployment are different, as they go down the economic index must go up, so let’s weight the inverse of each of those. Then we add up the three weighted values and we’ll have a measurement that moves up with a good economy and down with a bad one.

To show how this all works, let’s use the first month of the series, December 1985.

GDP growth for this month was 0.141338%. That’s about 1.7% growth for the year.

So the first component of the index is W1*GDP where W1 is a weight we will give the GDP series. Let this weight W1=20 so that W1*GDP= 2.826756

The second component is the interest rate level which in Dec 1985 was 13.5%.

Taking the inverse of this: 1/13.5 = 0.074074. So for our index we need to weight this number. Let’s give it a weight of 100 so that the weighted value for the interest rate component in the index is 7.407407

Finally let’s do the same for unemployment. In Dec 1985 the unemployment rate was 7.8%.The inverse of 7.8 is 0.128205.If we give the inverse unemployment a weight of 100, the unemployment component of the index is 12.82051.

So the total index value for Dec 1985 is 2.827+7.407+12.82= 23.05468

Are these weights reflective of reality? Mostly. The highest value attained for each of the weighted values (GDP, Interest rates, unemployment) is 13.8, 16.5 and 22.7 respectively while the minimum values are -6.8, 5.8 and 9.1.

The highest index value was 22.7 which is its value today; the lowest was 9.1 in December 1992.

That’s not perfect, but it will do for the purposes of this test because that index moves up with good economic times and down with bad economic times. We are more concerned with the movement rather than the actual values.

So now we have our index, regress the governments’ primary vote since 1985 on its own lagged value and this economic index (we’ll call this ECONINDEX)

If the “inside the beltway” theory holds, then the coefficient for ECONONINDEX will be positive. The positive changes in the index should walk hand in hand with increases in the governments primary vote as estimated by Newspoll.

If the economic condition of the country seriously influences voter behaviour, then the coefficient for ECONINDEX should be a relatively large value AND be statistically significant i.e. have a Prob value less than 0.1, preferably less than 0.05.

So let’s test the theory by doing the regression.

 

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/19/07 Time: 14:59
Sample (adjusted): 1986M01 2007M05
Included observations: 257 after adjustments
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	8.929002	1.725481	5.174790	0.0000
GOVPRIMARY(-1)	0.758701	0.040235	18.85692	0.0000
ECONINDEX	0.041177	0.020988	1.961928	0.0509
R-squared	0.602035	Mean dep var		42.437
Adjusted R-squared	0.598901	S.D. dep var		3.8455
S.E. of regression	2.435473	Akaike		4.6297
Sum squared resid	1506.608	Schwarz		4.6711
Log likelihood	-591.9246	F-statistic		192.12
Durbin-Watson stat	2.206406	Prob(F-statistic)		0.0000

The variable is significant enough, but its value of 0.04 suggests that the difference between the economy of December of 1992 and the economy of today is only worth 0.04*( 22.7-9.1) = 0.5576% to the governments primary vote.

Now that’s clearly twaddle by any yardstick. I created a couple of other economic measurements as well incorporating business expectations, and various weighting mechanisms and they all turned out to be in the same ballpark.

So lets look at this regression just for the Howard government period:

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/19/07 Time: 15:17
Sample: 1996M03 2007M05
Included observations: 135
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	12.87587	3.829054	3.362677	0.0010
GOVPRIMARY(-1)	0.699339	0.067491	10.36189	0.0000
ECONINDEX	0.001680	0.048245	0.034830	0.9723
R-squared	0.473274	Mean dep var		43.110
Adjusted R-squared	0.465293	S.D. dep var		3.3607
S.E. of regression	2.457537	Akaike		4.6581
Sum squared resid	797.2126	Schwarz		4.7227
Log likelihood	-311.4264	F-statistic		59.302
Durbin-Watson stat	2.044193	Prob(F-statistic)		0.0000

Lo and behold, its absolute twaddle. The index is irrelevant to the Howard government primary vote. Its value is so small its meaningless and the thing couldnt be more statistically insignificant if it tried.

Now let’s do it in a disaggregated way, where we’ll junk the index and look at the components of the index and we’ll throw consumer confidence and the yearly %change in the value of the ASX in there as well: And we’ll do it for the Howard era.

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/19/07 Time: 15:23
Sample (adjusted): 1996M03 2007M03
Included observations: 133 after adjustments
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.745883	4.243908	0.175754	0.8608
GOVPRIMARY(-1)	0.554957	0.070452	7.877036	0.0000
GDP	1.291302	1.080009	1.195640	0.2341
INT	0.677360	0.240362	2.818083	0.0056
UNEMP	0.475960	0.200412	2.374910	0.0191
CONCONF	0.093489	0.031964	2.924811	0.0041
ASX	0.120735	0.063475	1.902078	0.0594
R-squared	0.536205	Mean dep var		43.212
Adjusted R-squared	0.514120	S.D. dep var		3.2799
S.E. of regression	2.286292	Akaike		4.5429
Sum squared resid	658.6184	Schwarz		4.6950
Log likelihood	-295.1052	F-statistic		24.278
Durbin-Watson stat	1.967456	Prob(F-statistic)		0.0000

So we have GDP that’s not statistically significant to the governments primary vote, we have interest rates working opposite to the beltway theory whereby interest rate increases walk hand in hand with increases in the governments primary vote, likewise with unemployment, the higher it gets, the higher the government primary vote can be expected to be, with only consumer confidence and stock market capitalisation experiencing increases that walk hand in hand with increases to the governments primary vote – but by very small amounts.

So what do we have in the end?

Well we have a “beltway theory” that reckons governments live and die by the state of the economy, and we have the reality where GDP is meaningless to the governments primary vote, and interest rates and unemployment act completely contrary to the beltway theory. Strike 3 for Beltway – it’s just Journo’s Talking Shit As Usual.

Leading Vote Indicators

Could the ratio of total interest payments to disposable income have become a leading indicator of the government primary vote?

Below the governments primary vote is on the left axis, the ratio of interest payment to disposable income as a percentage is on the right axis as an inverse scale and the interest payments to disposable income was taken as the year to the month

 

 

 

 

 

Budgets bounce like dead cats

I thought I’d use the newspoll results back to 1985 to see if the journo cliche of choice come May – “the budget bounce” was nothing more than a fantasy driven by people that have nothing better to say.

Surprise surpise, that’s the case.

So I also tested the excuse of choice for journo’s and governments alike when the polls dont react the way they apparently ought to – “its a delayed bounce, it hasnt happened yet, but it will happen”.

Surprise, surprise….. that’s twaddle too.

Using the series GOVPRIMARY (which is the newspoll measurement of the governments primary vote) as the dependent variable, I regressed it against 3 seasonal dummy variables S5, S6,and S7 representing the months of May, June and July respectively.If budgets actually do cause a bounce in the government vote in the polls, those seasonal dummy variables would be expected to have a positive coefficient.If those bounces happen as regularly as the commentariat believes, they should also be statistically significant i.e. the Prob value should be less than 0.1, preferably less than 0.05.

So let us see the results (a usual constant C was also added to the mix – just ignore it)

Again, using the quaint little Eviews to do the ugly math:

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/17/07 Time: 17:05
Sample(adjusted): 1986:01 2007:05
Included observations: 257 after adjusting endpoints
Convergence achieved after 5 iterations
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	42.37000	0.688365	61.55166	0.0000
S5	-0.085694	0.502248	-0.170621	0.8647
S6	-0.686960	0.583794	-1.176716	0.2404
S7	-0.429398	0.511557	-0.839394	0.4020
AR(1)	0.774224	0.039983	19.36391	0.0000
R-squared	0.598817	Mean dep var		42.437
Adjusted R-squared	0.592449	S.D. dep var		3.8455
S.E. of regression	2.454982	Akaike		4.6533
Sum squared resid	1518.788	Schwarz		4.7224
Log likelihood	-592.9593	F-statistic		94.035
Durbin-Watson stat	2.216458	Prob(F-statistic)		0.0000

Lo and behold, all of the seasonal dummy variables are utter rubbish.They have no statistical significance and some of the coefficients are negative.Budgets dont bounce polls.

What about just over the Howard term of government.:

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/17/07 Time: 10:43
Sample: 1996:03 2007:05
Included observations: 135
Convergence achieved after 3 iterations
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	43.11803	0.745668	57.82469	0.0000
S5	0.334513	0.688051	0.486174	0.6277
S6	-1.320608	0.805286	-1.639925	0.1034
S7	-0.321919	0.711838	-0.452237	0.6519
AR(1)	0.713235	0.063750	11.18798	0.0000
R-squared	0.494724	Mean dep var		43.110
Adjusted R-squared	0.479177	S.D. dep var		3.3607
S.E. of regression	2.425422	Akaike		4.64622
Sum squared resid	764.7476	Schwarz		4.7538
Log likelihood	-308.6200	F-statistic		31.821
Durbin-Watson stat	2.030240	Prob(F-statistic)		0.0000

Again – utter twaddle.

Budgets dont give governments a bounce in the polls.It is not in any way an expected voter behaviour

UPDATE :GriffithGuy raised an interesting question.Do budgets bounce in election years when the pork is flying?

Lets test the theory again, but this time using dummy variables representing only budgets in election years.First for all governments since 1986:

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/18/07 Time: 01:17
Sample(adjusted): 1986:01 2007:05
Included observations: 257 after adjusting endpoints
Convergence achieved after 6 iterations
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	42.27549	0.684618	61.75049	0.0000
S5E	0.466934	0.832840	0.560653	0.5755
S6E	-0.653364	0.995129	-0.656562	0.5121
S7E	-0.318264	0.880077	-0.361632	0.7179
AR(1)	0.774742	0.040092	19.32387	0.0000
R-squared	0.598543	Mean dep var		42.437
Adjusted R-squared	0.592170	S.D. dep var		3.8455
S.E. of regression	2.455822	Akaike		4.6540
Sum squared resid	1519.827	Schwarz		4.7231
Log likelihood	-593.0472	F-statistic		93.928
Durbin-Watson stat	2.228653	Prob(F-statistic)		0.0000

Again, no budget bounce as the coefficients are small and mostly negative, and they are statistically insignificant.
But what about Howard from 1996-2007?

Dependent Variable: GOVPRIMARY
Method: Least Squares
Date: 05/18/07 Time: 00:59
Sample: 1996:03 2007:05
Included observations: 135
Convergence achieved after 5 iterations
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	43.04898	0.755544	56.97745	0.0000
S5E	1.098800	1.051503	1.044980	0.2980
S6E	-2.281707	1.285617	-1.774795	0.0783
S7E	-0.939306	1.151982	-0.815383	0.4163
AR(1)	0.723267	0.063517	11.38701	0.0000
R-squared	0.504229	Mean dep var		43.110
Adjusted R-squared	0.488975	S.D. dep var		3.3607
S.E. of regression	2.402501	Akaike		4.6272
Sum squared resid	750.3613	Schwarz		4.7348
Log likelihood	-307.3381	F-statistic		33.054
Durbin-Watson stat	2.065642	Prob(F-statistic)		0.0000

Again no bounce as the coefficients are insignificant, but at the 10% level of significance Howard actually loses over 2% off his primary vote in the June following an election year budget.

Ouch.

Regression model for the coming election

 

 

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.