My passion lies in applied econometrics research, particularly in areas like productivity and efficiency analysis, time-series forecasting, and causal inference. My academic journey is complemented by real-world industry experience, which I garnered through various roles at New Zealand's Inland Revenue Department. From being a Customer Service Representative (2007-2012) to an Analyst (2014-2016), I've developed a practical understanding of the intricacies of fiscal policies and revenue systems, which enriches my research and teaching. I'm always open to exploring new avenues of study and collaborating with others who share these interests (find my publications below). Beyond research, I actively contribute to internationalization efforts at Ajman University's College of Business Administration. I firmly believe in the power of intercultural exchange and fostering educational partnerships and research collaborations. This passion extends to my teaching philosophy, where I strive to provide students with diverse global learning experiences through programs like articulation, exchange, and international study tours. Teaching is more than a profession for me; it's a lifelong pursuit fueled by the excitement and insights of my students. I view them as partners in my own growth and embrace the opportunity to guide and nurture the future leaders of our world. Outside academia, I find joy in my family life. Happily married with two daughters, they inspire me both personally and professionally. Whether you're a colleague seeking research collaboration or a student needing guidance, I'm eager to connect and create enriching academic experiences that advance knowledge and benefit society. Feel free to reach out!
This study explores the complex relationship between firm efficiency and stochastic volatility, focusing on how firms utilise inputs to generate sales and the impact of financial shocks on efficiency levels. Utilising a dataset of 476 U.S. firms across 23 sectors from 2010 to 2022, it integrates stochastic volatility into efficiency analysis, treating volatility as an evolving, unobserved process diverging from traditional methods. The research classifies sectors into various efficiency performance categories based on their response to economic fluctuations. Findings show divergent patterns across sectors, with some exhibiting consistent efficiency and others facing erratic performance due to market and technological changes. This analysis provides valuable insights into sectoral adaptability and resilience in fluctuating economic conditions, offering strategic implications for managers and policymakers.
Using Bayesian Structural Time Series analysis, this study examines the causal impact of loan-to-value (LTV) restrictions imposed by the Reserve Bank of New Zealand in October 2013. By incorporating state-space components, such as local linear trend, seasonality and regression, counterfactual values of house price indices are predicted. Surprisingly, the study reveals that the implementation of LTV restrictions had no significant effect on national house price indices, contradicting prior Central Bank studies that reported a nearly 3 percentage-point decrease in housing cost inflation. This contradictory evidence challenges existing perceptions of the effectiveness of LTV restrictions in curbing house price inflation.
Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.
Efficiency and productivity analysis have been critical in healthcare and economics literature. Despite the tremendous innovation in methodology and data availability, a comprehensive literature review on this topic has not been conducted recently. This article provides a three-part literature review of healthcare efficiency and productivity studies. It begins by reviewing the two primary empirical methods used in healthcare efficiency studies, emphasising the treatment of inefficiency persistence. Second, previous contributions to healthcare productivity research are discussed with a focus on methodology and findings. In the third section, various measures of outputs, inputs, and prices in health literature are explored to determine the extent of consensus in the literature. On the methodological front, the literature review shows that while the Data Envelopment Analysis and the Stochastic Frontier Analysis have been used extensively in healthcare productivity and efficiency studies, their application in the context of longitudinal data is limited. Further, no study currently undertakes to measure the TFP changes and its components that use both primal and dual approaches. There is also a considerable variation in the use of inputs, outputs, and price variables, suggesting that the use of variables in healthcare productivity and efficiency literature rests on the balance between data availability and the research scope.
This article introduces the Bayesian structural time series (BSTS) as a potential tool for forecasting in the tourism literature. Using data on Australian tourist arrivals in New Zealand, the forecasting accuracy of the estimated model is evaluated using a fixed partitioning approach. The MAPE of the fitted model is 3.11 per cent for the validation stage and 2.75 per cent for the test stage. The BSTS outperforms two other competing models both in the validation and test stage. In addition to forecasting, BSTS also estimates the trend, trend slope, and seasonality that change over time.
This study uses quarterly data from 2011 to 2018 to evaluate the technical efficiency of New Zealand District Health Boards (DHBs) in providing hospital services. It examines how efficiency is affected by various patient structures and contextual factors. An intertemporal data envelopment analysis and bootstrap approach are used to compute the bias-corrected technical efficiency scores, followed by highly flexible beta regression to assess the relationship between technical efficiency and related factors. The results indicate that the technical efficiency levels of New Zealand DHBs have not improved since 2011, and on average DHBs could increase their provision of hospital services by approximately 12%. Furthermore, most of the poor performing DHBs operate in the area of high socio-economic deprivation. The results from beta regression show that DHBs providing hospital services in highly deprived areas are associated with a decreasing level of technical efficiency as the proportion of surgical, acute, Māori and Pacific inpatient increases. However, an increase in capital to labour ratio improves the technical efficiency of these DHBs. Therefore, policymakers need to formulate comprehensive strategies involving a longer time horizon that facilitates capital investments in critical technology and capacity development to improve the long-run efficiency performance of DHBs operating in the area of high deprivation.
The majority of secondary and tertiary healthcare services in New Zealand are provided through public hospitals managed by 20 local District Health Boards. Due to data issues and ill-judged generic public perceptions, efficiency studies are insufficient in spite of the extensive empirical literatures available. This inevitably leads to criticisms about the perverse incentives which might be created by the National Health Targets designed to improve the performance of public health services. Utilizing a multifaceted administrative hospital dataset, this is the first case study to measure both the technical and cost efficiency of New Zealand public hospitals during the period of 2011–2017. More specifically, it deals with the question of how hospital efficiency varies with respect to activities accounted for by the National Health Targets. The empirical results show no evidence that these targets are achieved at the expenses of lowering the overall efficiency of hospital operations. The national technical efficiency is averaged at 86 percent over the period and cost efficiency is 85 percent. The results are derived by stochastic input distance function and cost frontier in order to accommodate multiple outputs and limited number of census observations. Efficiency ranking is sensitive to specifications of the inefficiency error term, but reasonably robust to the choice of functional form and different proxies for capital input.