Forecasting structural change and fat-tailed events in Australian macroeconomic variables

Cross, Jamie; Poon, Aubrey

doi:10.1016/j.econmod.2016.04.021

Cited by 48 publications

(38 citation statements)

References 32 publications

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“…Th e increase in taxes on goods and services or social security contributions has a more intense impact on per capita real GDP than the income tax increase. As Cross and Poon (2016) state in their study, the expected performance of Australian key macroeconomic variables, such as real GDP growth, CPI infl ation and a short-term interest rate, can be improved by whether the incorporating time variation and fat-tails into a suit of popular univariate and multivariate Gaussian distributed models. According to their results, it is evident that the VAR with the proposed features provides the best interest and infl ation forecasts.…”

Section: Literature Review Models For Economic Growthmentioning

confidence: 99%

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GDP Rate In The European Union: Simulations Based On Panel Data Models

Simionescu¹,

Dobeš²,

Brezina³

et al. 2016

Journal of International Studies

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Abstract. Th e objective of this paper is to provide more details regarding the evolution of real GDP growth in the countries of the European Union (EU-28) in the period from 2004 to 2015 based on a panel data approach. According to the estimations based on some dynamic panel data models, an any increase in the real GDP rate in the previous period by one percentage point, the real GDP rate in the current period will increase by 0.3 percentage points up to 0.5 percentage points. According to the Fixed Eff ects Model with time and individual eff ects, the real GDP growth is explained by the employment. According to simulations for 2016 and 2017 based on Dynamic Model and the Fixed Eff ects Model, the last model predicting higher GDP rates with respect to dynamic models. Th e result of this study is the estimation of the real GDP rates in EU-28 countries, which are based on presented econometrics models. Th e annual average of employment as the main factor of GDP growth is taken into account.

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Section: Literature Review Models For Economic Growthmentioning

confidence: 99%

“…Th e models used are, e.g. univariate autoregressive (AR) and multivariate vector autoregressive (VAR) models (Cross and Poon 2016;Ciccarelli et al 2016), Bayesian vector autoregressions (BVARs) model (Carriero et al 2015) or Matutinović et al (2016) who have developed a system model of autocatalytic growth and development.…”

Section: Introductionmentioning

confidence: 99%

GDP Rate In The European Union: Simulations Based On Panel Data Models

Simionescu¹,

Dobeš²,

Brezina³

et al. 2016

Journal of International Studies

View full text Add to dashboard Cite

show abstract

“…Section III describes empirical results from the time-varying parameter panel BVAR model, and Section IV discusses the results and concludes. 3 For related literature on the use of stochastic volatility in improving model fit and forecastability, I refer the reader to, for example, Clark (2011), Clark and Ravazzolo (2015) and Cross and Poon (2016) for outof-sample point and density forecasting, and Chan and Eisenstat (2016) for in-sample analysis. 4 The reason why the common indicator explains an extremely large share of fluctuation in investment is that the sample variance of the investment indicator is smaller than the sample variance of the common indicator.…”

Section: Introductionmentioning

confidence: 99%

“… For related literature on the use of stochastic volatility in improving model fit and forecastability, I refer the reader to, for example, Clark (), Clark and Ravazzolo () and Cross and Poon () for out‐of‐sample point and density forecasting, and Chan and Eisenstat (2016) for in‐sample analysis. …”

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confidence: 99%

Assessing the Synchronicity and Nature of Australian State Business Cycles

Poon

2018

Economic Record

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This paper assesses the synchronicity and nature of Australian state business cycles. To this end, I develop a time‐varying parameter panel Bayesian vector autoregression (BVAR) with a novel common stochastic volatility factor in the error structure, which is estimated in an efficient Markov chain Monte Carlo algorithm. The common stochastic volatility factor reveals that macroeconomic volatility was more pronounced during the Asian financial crisis than during the more recent global financial crisis. Next, the panel BVAR's common, state‐ and variable‐specific indicators capture several interesting economic facts. In particular, the fluctuations of the common indicator closely follow the trend line of the Organisation for Economic Co‐operation and Development composite leading indicators for Australia, making it a good proxy for nationwide business cycle fluctuations. Furthermore, despite significant co‐movements of Australian state and territory business cycles during times of economic contractions, the state indicators suggest that the average degree of synchronisation across the Australian state and territory cycles in the 2000s is only half of that in the 1990s. Given that aggregate macroeconomic activity is determined by cumulative activity of each of the states, the results suggests that the federal government should consider granting state governments greater autonomy in handling state‐specific cyclical fluctuations.

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“…Following the seminal work of Box and Jenkins (1970), autoregressive moving average (ARMA) models have become the standard tool for modeling and forecasting univariate time series. More recently, coefficient instability in macroeconomic time series models has been widely acknowledged (see, e.g., Stock and Watson, 1996;Ludbergh et al, 2003;Marcellino, 2004;Stock and Watson, 2007;Cross and Poon, 2016). For example, Stock and Watson (2007) show that US CPI inflation is best modeled by an unobserved components model in which both the transitory and trend equations allow for time-varying volatility.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Volatility Models with Arma Innovations an Application to G7 Inflation Forecasts

2018

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We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence which renders standard Kalman filter techniques not directly applicable. To overcome this hurdle we develop an efficient posterior simulator that builds on recently developed precision based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy and the US. AbstractWe introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence which renders standard Kalman filter techniques not directly applicable. To overcome this hurdle we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy and the US.

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Forecasting structural change and fat-tailed events in Australian macroeconomic variables

Cited by 48 publications

References 32 publications

GDP Rate In The European Union: Simulations Based On Panel Data Models

GDP Rate In The European Union: Simulations Based On Panel Data Models

Assessing the Synchronicity and Nature of Australian State Business Cycles

Stochastic Volatility Models with Arma Innovations an Application to G7 Inflation Forecasts

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