## Abstract

What accounts for the Australian business cycle, what caused the economic slumps and what factors contributed to the decades’ buoyancy? To understand these questions, the current paper decomposes the Australian business cycle into its sources for the period 1980–2014. Our main finding is that the efficiency wedge and the investment wedge are the major forces behind Australian output variations. The efficiency wedge is behind the growth slowdown that emerged in the mid 2000s.

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## Notes

See the

*Melbourne Institute’s*phases of classical cycles.The business cycle accounting decomposition asks a very different question from the traditional decomposition of the style used in the SVAR literature. The business cycle accounting decomposition asks what are the effects on economic aggregates of the sum of the movements induced by all primitive shocks on a wedge. The VAR-style decomposition asks what are the effects on economic aggregates of a particular primitive shock operating through all of the wedges. Hence, one is not required to identify shocks via restrictions to the SVAR etc—accounting is more of a guidance about how shocks affect the economy and its aggregate (and the ability to feed wedge movements back into the artificial economy allows to see the effects in isolation and in a fully-specified theoretical model—unlike, say, variance decompositions).

Jaaskela and Nimark (2011) estimate an New Keynesian model of the Australian economy and also find technology shocks to be of most importance (however they do not consider specific recession episodes but rather look at a variance decompostion).

The data in our study begins in 1980 due to some quarterly data not being available before that year.

The only significant difference of the latter is that the end point of Australia’s recent slump is 2008:IV instead of our finding of 2009:II.

In the model there is only one type of good and international trades only purpose is to smooth consumption through international risk sharing (and in addition risk sharing is perfect). One could extend the model to a version in which goods produced at home and abroad are different and thus subject to changes in relative price. We decided to not present this line of model here (and to follow Chari, Kehoe and McGrattan) and we reserve the topic for future work (see however, our discussion in Sect. 5.4. as well as 7).

Most Australian quarterly data begins in 1980 and this is the reason for the staring date.

The

*ABS*defines gross mixed income as the surplus or deficit accruing from production by unincorporated enterprises that consists of compensation of employees and operating surplus.Of course, if we allow all wedges to take on their measured values, the artificial economy would simply replicate the Australian data.

We would like to thank one referee for pointing this out to us.

Alternatively, efficiency wedge movements can also result from increasing returns and, thus, reflect endogenous responses of productivity to shocks unrelated to technology. We will briefly address this issue in Sect. 7.

Between 1991 and 1996 the Australian government also fully privatized the

*Commonwealth Bank*.They also point to the lack of a compelling fully-fledged model of such link.

Recall that the wedge is the sum of government spending and the net exports. Of these, net exports moved most.

See Kamihigashi (1996).

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## Acknowledgments

We would like to thank Michael Burda, Sharon Harrison, Jacob Wong, the editor of this Journal and two anonymous referees for very helpful comments and suggestions. Weder acknowledges generous financial support from the *Australian Research Council*.

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## Appendices

### Appendix 1: Model

### 1.1 Additional Information on Accounting

Efficiency, labor and government consumption wedges can be measured directly from the data. The investment wedge involves computation of the model’s rational expectation solution. To do this, the equilibrium conditions are log-linearized around the steady-steady while assuming that the wedges follow first-order autoregressive processes. Observable variables are \(y_{t}\), \(x_{t}\), \(h_{t}\) and \(g_{t}\) while the state variables are \(\log k_{t},\) \(\log A_{t},\) \(\tau _{ht},\) and \(\tau _{xt}\). Furthermore, \(\log k_{t+1}\) is a (policy) function of \(\log k_{t},\) \(\log A_{t},\) \(\tau _{ht},\) \(\tau _{xt},\) and \(\log g_{t}\). Then, the state space representation of the model is given by

and

where \(X_{t}=\left[ \log \widetilde{k}_{t},\log A_{t},\tau _{ht},\tau _{xt},\log \widetilde{g}_{t}\right] ^{\prime }\) and \(Y_{t}=\left[ \log \widetilde{y}_{t},\log \widetilde{x}_{t},\log h_{t},\log \widetilde{g}_{t}\right] ^{\prime }\). The vector \(\zeta _{t+1}\) contains shocks from the wedges and \(\omega _{t}\) stands for residuals. Variables with tilde represent detrended per working age series. Let \(s_{t}=[\log A_{t},\tau _{ht},\tau _{xt},\log g_{t}]^{\prime }\), thus,

We assume that \(\epsilon _{t}\) is independent and identically distributed and has a normal distribution with zero mean and covariance matrix \(\mathbf {V}\). \(\mathbf {V}\) is \(\mathbf {QQ}^{\prime }\) where \(\mathbf {Q}\) is the lower triangle matrix. Maximum likelihood is employed to estimate the stacked matrices \(\mathbf {P}_{0}\), \(\mathbf {P}\), and \(\mathbf {Q}\). Finally, the maximum likelihood estimates are used to obtain the investment wedge. This generates the following results. The means of the wedges are

the coefficient matrix \(\mathbf {P}\) on lagged state variables is

and the coefficient matrix \(\mathbf {Q}\) is

### 1.2 Small Open Economy Extension

The small open economy is described by

Following Schmitt-Grohé and Uribe (2003) we assume the existence of an upwardly sloping supply of funds. This is done to allow applying local solution methods to the model economy. The calibration involves \(\vartheta =0.0001\) and \(b/y=0.39\) which corresponds to the 1979–2004 average of Australia’s net foreign assets to (annual) GDP ratio (taken from Lane and Milesi-Ferretti 2007).

### Appendix 2: Data

The seasonally adjusted quarterly data used in our quantitative analysis are obtained from the Australian Bureau of Statistics (ABS) unless otherwise noted. The data are defined as follows.

*Output*: GDP minus sales tax plus services from consumer durable goods and depreciation of consumer durable goods.

*Sales tax*: Sales tax plus goods and services tax.

*Consumer durable goods*: Household final consumption expenditure on furnishings and household equipment and purchase of vehicles. As the ABS does not provide the data of services of consumer durable goods and depreciation of consumer durable goods, we use the details of these data in the literature. The services from consumer durable goods are equal to 17 % per annum as in Chari et al. (2007). The depreciation rate of consumer durable goods is 16 % per annum as suggested by the average ratio of durable goods depreciation to the stock of durable goods in the U.S. during 1960–2006. This is similar to the depreciation rate of durable goods in Baxter (1996) of which is 15.6 % per annum.

*Consumer non-durable goods*: Household final consumption expenditure excluding final consumption expenditure on furnishings and household equipment and purchase of vehicles.

*Consumption*: Consumer non-durable goods minus sales tax on consumer non-durable goods plus services from consumer durable goods and depreciation of consumer durable goods.

*Investment*: Private and public gross fixed capital formation plus changes in inventories and consumer durable goods minus sales tax on consumer durable goods.

*Government consumption*: Government final consumption expenditure.

*Net exports*: Exports minus imports.

*Price deflator*: Implicit price deflator.

*Real value*: Series are deflated by price deflator.

*Total hours worked*: the average weekly hours worked for wages and salary earners multiplied by employed persons and multiplied by 12 weeks.

*Population*: Civilian non-institutional population aged 15-64.

*Per capita*: Series are divided by population.

*Terms of trade*: Terms of trade provided by RBA Economic and Financial Statistics.

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Photphisutthiphong, N., Weder, M. Observations on the Australian Business Cycle.
*J Bus Cycle Res* **12**, 141–164 (2016). https://doi.org/10.1007/s41549-016-0007-0

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DOI: https://doi.org/10.1007/s41549-016-0007-0