Employee Profile

Jamie Cross

Postdoctoral Fellow - Department of Economics


Cross, Jamie; Hou, Chenghan & Trinh, Kelly (2021)

Returns, Volatility and the Cryptocurrency Bubble of 2017-18

Economic Modelling Doi: 10.1016/j.econmod.2021.105643

Cross, Jamie; Bjørnland, Hilde C & Aastveit, Knut Are (2021)

Inflation expectations and the pass-through of oil prices

Review of Economics and Statistics Doi: 10.1162/rest_a_01073

Guo, Na; Zhang, Bo & Cross, Jamie (2021)

Time-varying trend models for forecasting inflation in Australia

Journal of Forecasting Doi: 10.1002/for.2814

Cross, Jamie; Hou, Chenghan & Nguyen, Bao (2021)

On the China factor in the world oil market: A regime switching approach

Energy Economics, 95, s. 105- 119. Doi: 10.1016/j.eneco.2021.105119

We investigate the relationship between China's macroeconomic performance and the world oil market over the past two decades. Unlike existing studies, we allow for possible regime changes by utilizing a class of Markov-switching vector autoregression (MS-VAR) models. The model identifies key regime changes in the structural shocks when the oil market experiences low and high volatility. We find that demand shocks from China and the rest of the world have a larger impact on the real price of crude oil during periods of high volatility. Supply shocks, in contrast, have a large effect on the price in the low volatility regime. A similar state-dependent phenomenon is observed for the impact of oil price shocks on China economic activity, however the size of these responses is relatively small. Thus, despite China being a major player in international oil markets, we conclude that oil market shocks tend to have little impact on China's real GDP growth.

Zhang, Bo; Chan, Joshua & Cross, Jamie (2020)

Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

International Journal of Forecasting Doi: 10.1016/j.ijforecast.2020.01.004

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 results in standard Kalman filter techniques not being 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 U.S.

Cross, Jamie; Hou, Chenghan & Poon, Aubrey (2020)

Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity

International Journal of Forecasting Doi: 10.1016/j.ijforecast.2019.10.002

A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.

Cross, Jamie & Poon, Aubrey (2019)

On the Contribution of International Shocks in Australian Business Cycle Fluctuations

Empirical Economics Doi: 10.1007/s00181-019-01752-y - Full text in research archive

What proportion of Australian business cycle fluctuations are caused by international shocks? We address this question by estimating a panel VAR model that has time-varying parameters and a common stochastic volatility factor. The time-varying parameters capture the inter-temporal nature of Australia’s various bilateral trade relationships, while the common stochastic volatility factor captures various episodes of volatility clustering among macroeconomic shocks, e.g., the 1997/98 Asian Financial Crisis and the 2007/08 Global Financial Crisis. Our main result is that international shocks from Australia’s five largest trading partners: China, Japan, the EU, the USA and the Republic of Korea, have caused around half of all Australian business cycle fluctuations over the past two decades. We also find important changes in the relative importance of each country’s economic impact. For instance, China’s positive contribution increased throughout the mining boom of the 2000s, while the overall US influence has almost halved since the 1990s

Academic Degrees
Year Academic Department Degree
2017 The Australian National University PhD
Work Experience
Year Employer Job Title
2018 - Present BI Norwegian Business School Assistant professor