Economics - Research Publications
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ItemNo Preview AvailableAre working hours complements in production?(Elsevier BV, 2023-08-01)
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ItemNo Preview AvailableThe effect of clean energy investment on CO2 emissions: Insights from a Spatial Durbin Model(Elsevier BV, 2023-10-01)
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ItemLikelihood Based Inference and Bias Reduction in the Modified Skew-t-Normal Distribution(MDPI, 2023-08)In this paper, likelihood-based inference and bias correction based on Firth’s approach are developed in the modified skew-t-normal (MStN) distribution. The latter model exhibits a greater flexibility than the modified skew-normal (MSN) distribution since it is able to model heavily skewed data and thick tails. In addition, the tails are controlled by the shape parameter and the degrees of freedom. We provide the density of this new distribution and present some of its more important properties including a general expression for the moments. The Fisher’s information matrix together with the observed matrix associated with the log-likelihood are also given. Furthermore, the non-singularity of the Fisher’s information matrix for the MStN model is demonstrated when the shape parameter is zero. As the MStN model presents an inferential problem in the shape parameter, Firth’s method for bias reduction was applied for the scalar case and for the location and scale case.
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ItemHow to proxy the unmodellable: Analysing granular insurance claims in the presence of unobservable or complex drivers(Institute of Actuaries, Australia, 2018)The estimation of claim and premium liabilities is a key component of an actuary's role and plays a vital part of any insurance company’s operations. In practice, such calculations are complicated by the stochastic nature of the claims process as well as the impracticality of capturing all relevant and material drivers of the observed claims data. In the past, computational limitations have promoted the prevalence of simplified (but possibly sub-optimal) aggregate methodologies. However, in light of modern advances in processing power, it is viable to increase the granularity at which we analyse insurance data sets so that potentially useful information is not discarded. By utilising more granular and detailed data (that is usually readily available to insurers), model predictions may become more accurate and precise. Unfortunately, detailed analysis of large insurance data sets in this manner poses some unique challenges. Firstly, there is no standard framework to which practitioners can refer and it can be challenging to tractably integrate all modelled components into one comprehensive model. Secondly, analysis at greater granularity or level of detail requires more intense levels of scrutiny as complex trends and drivers that were previously masked by aggregation and discretisation assumptions may emerge. This is particularly an issue with claim drivers that are either unobservable to the modeller or very difficult/expensive to model. Finally, computation times are a material concern when processing such large volumes of data as model outputs need to be obtained in reasonable time-frames. Our proposed methodology overcomes the above problems by using a Markov-modulated non-homogeneous Poisson process framework. This extends the standard Poisson model by allowing for over-dispersion to be captured in an interpretable, structural manner. The approach implements a flexible exposure measure to explicitly allow for known/modelled claim drivers while the hidden component of the Hidden Markov model captures the impact of unobservable or practicably non-modellable information. Computational developments are made to drastically reduce calibration times. Theoretical findings are illustrated and validated in an empirical case study using Australian general insurance data in order to highlight the benefits of the proposed approach.
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ItemOn the Impact, Detection and Treatment of Outliers in Robust Loss Reserving(Actuaries Institute, 2016)The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. For instance, it has been shown that the popular chain-ladder reserving approach is at significant risk to such aberrant observations in that reserve estimates can be significantly shifted in the presence of even one outlier. In this paper we firstly investigate the sensitivity of reserves and mean squared errors of prediction under Mack's Model. This is done through the derivation of impact functions which are calculated by taking the first derivative of the relevant statistic of interest with respect to an observation. We also provide and discuss the impact functions for quantiles when total reserves are assumed to be lognormally distributed. Additionally, comparisons are made between the impact functions for individual accident year reserves under Mack's Model and the Bornhuetter-Ferguson methodology. It is shown that the impact of incremental claims on these statistics of interest varies widely throughout a loss triangle and is heavily dependent on other cells in the triangle. We then put forward two alternative robust bivariate chain-ladder techniques (Verdonck and VanWouwe, 2011) based on Adjusted-Outlyingness (Hubert and Van der Veeken, 2008) and bagdistance (Hubert et al., 2016). These techniques provide a measure of outlyingness that is unique to each individual observation rather than largely relying on graphical representations as is done under the existing bagplot methodology. Furthermore the Adjusted Outlyingness approach explicitly incorporates a robust measure of skewness into the analysis whereas the bagplot captures the shape of the data only through a measure of rank. Results are illustrated on two sets of real bivariate data from general insurers.
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ItemNo Preview AvailableWhen Walras meets Vickrey(The Econometric Society, 2022-11)We consider general asset market environments in which agents with quasilinear payoffs are endowed with objects and have demands for other agents' objects. We show that if all agents have a maximum demand of one object and are endowed with at most one object, the VCG transfer of each agent is equal to the largest net Walrasian price of this agent. Consequently, the VCG deficit is equal to the sum of the largest net Walrasian prices over all agents. Generally, whenever Walrasian prices exist, the sum of the largest net Walrasian prices is a nonnegative lower bound for the deficit, implying that no dominant‐strategy mechanism runs a budget surplus while respecting agents' ex post individual rationality constraints.
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ItemNo Preview AvailableBilateral Trade with Multiunit Demand and Supply(INFORMS, 2022-05-02)We study a bilateral trade problem with multiunit demand and supply and one-dimensional private information. Each agent geometrically discounts additional units by a constant factor. We show that when goods are complements, the incentive problem—measured as the ratio of second-best to first-best social surplus—becomes less severe as the degree of complementarity increases. In contrast, if goods are substitutes and each agent’s distribution exhibits linear virtual types, then this ratio is a constant. If the bilateral trade setup arises from prior vertical integration between a buyer and a supplier, with the vertically integrated firm being a buyer facing an independent supplier, then the ratio of second-best to first-best social surplus is, in general, not monotone in the degree of complementarity when products are substitutes and is increasing when products are complements. Extensions to profit maximization by a market maker and a discrete public good problem show that the broad insight that complementarity of goods mitigates the incentive problem generalizes to these settings. This paper was accepted by Joshua Gans, business strategy. Funding: Financial support from the Samuel and June Hordern Endowment, the University of Melbourne Faculty of Business & Economics [Eminent Research Scholar Grant], and the Australian Research Council [Grant DP200103574] is acknowledged.
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ItemNo Preview AvailableOverriding in Teams: The Role of Beliefs, Social Image, and Gender(INFORMS, 2023-04)To shed light on the factors that affect who speaks up in teams in the workplace, we study willingness to speak up after someone has raised an opinion. We call voicing disagreement overriding and study this behavior in a laboratory experiment where participants answer multiple choice questions in pairs. In a control treatment, participants interact anonymously. In a photo treatment, both participants see the photo of the person they are matched with at the beginning of the group task. Using a series of incentivized tasks, we elicit beliefs about the likelihood that each possible answer option to a question is correct. This allows us to measure disagreement and to tease apart the role of disagreement versus preferences in the decision to override ideas in teams. Results show that anonymity increases overriding. This treatment effect is driven by social image costs. Analysis of heterogeneity in behavior by gender reveals no differences between the likelihood that men and women override. However, we find some evidence that men and women are treated differently; when participants disagree with their partner, they are more likely to override a woman than a man. Preferences seem to in part explain the differential treatment of men and women. Studying group performance, we find that overriding helps groups on average, while the gender composition of teams does not affect team performance. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the University of Melbourne and the Australian Research Council [Grant DE190100585]. J. Guo received a Kinsman Studentship from the University of Melbourne to conduct this research. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.4434 .
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ItemNo Preview AvailableDouble Markups, Information, and Vertical Mergers(SAGE Publications, 2022-09-01)In vertical contracting models with complete information and linear prices, double markups that arise between independent firms provide an efficiency rationale for vertical mergers since these eliminate double markups (EDM). However, the double markups vanish even without vertical integration if the firms are allowed to use two-part tariffs. Hence, the efficiency rationale for vertical mergers in models of complete information requires restrictions on the contracts that firms can use. In a sense, with complete information, two-part tariffs are simply too powerful. If instead one allows incomplete information and removes the restriction on contract forms, then vertical mergers continue to have an effect that is analogous to EDM, but they also have the potential to affect the overall efficiency of the market to the detriment of society. Consequently, the social surplus effects of vertical integration depend on the underlying market structure, and vertical mergers are, in and of themselves, neither good nor bad. We illustrate through an example that with incomplete information, the private benefits from vertical integration tend to be excessive; that is, vertical mergers remain profitable even when they are socially harmful.
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ItemNo Preview AvailableMonopoly Pricing, Optimal Randomization, and Resale(UNIV CHICAGO PRESS, 2022-03-01)