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Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that adequately approximate the states yielding superior accuracy over methods that do not. We also document numerically that over small out-of-sample evaluation periods the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy. Nevertheless, in certain settings, and over a longer out-of-sample period, these predictive discrepancies can become meaningful. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis. Supplementary materials for this article are available online.

Data from the Household Income and Labour Dynamics in Australia Survey is used to estimate distributions for the level of educational attainment for Australian indigenous and nonindigenous populations for the years 2001, 2006, 2014 and 2017. Bayesian inference is used to analyse how these ordinal categorical distributions have changed over time and to compare indigenous and nonindigenous distributions. Both the level of educational attainment and inequality in educational attainment are considered. To compare changes in levels over time, as well as inequality between the two populations, first order stochastic dominance and an index of educational poverty are used. To examine changes in inequality over time, two inequality indices and generalised Lorenz dominance are considered. Results are presented in terms of posterior densities for the indices and posterior probabilities for dominance for the dominance comparisons. We find some evidence of improvement over time, especially in the lower parts of the indigenous distribution and that inequality has significantly increased from 2001 to 2017.

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are ‘optimal’ according to a given score and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility, however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios and using both artificially simulated and empirical data.

Despite the large body of research on missing value distributions and imputation, there is comparatively little literature on how to make it easy to handle, explore, and impute missing values in data. This paper addresses this gap. The new methodology builds upon tidy data principles, with a goal to integrating missing value handling as an integral part of data analysis workflows. New data structures are defined along with new functions (verbs) to perform common operations. Together these provide a cohesive framework for handling, exploring, and imputing missing values. These methods have been made available in the R package naniar.

This published article reviews 4 different statistical software systems for utility as tools for introductory statistical education. The paper has been published as: Abbasnasab Sardareh, S., Brown, G. T. L., & Denny, P. (2021). Comparing four contemporary statistical software tools for introductory data science and statistics in the social sciences. Teaching Statistics, 43(S1), S157-S172. https://doi.org/10.1111/test.12274

This thesis explores three different but interconnected issues relating to the identification of fiscal policy shocks. Grounded on several extensions of the structural vector autoregressive (SVAR) framework, the three essays provide empirical analyses of the macroeconomic impacts of fiscal policy shocks. They also shed light on the role of nonlinearity as well as its implications for the transitional dynamics of the U.S. economy during the financial crisis and the zero effective lower bound of interest rate. Beyond its contribution to the literature, this thesis also provides meaningful insights for effective policy targeting from a practical perspective.

The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, high-dimensional models, and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular -- and more generally those interested in adopting a Bayesian approach to empirical work -- distinguish between different approximate techniques; understand the sense in which they are approximate; appreciate when and why particular methods are useful; and see the ways in which they can can be combined., Comment: arXiv admin note: text overlap with arXiv:2004.06425

This thesis investigates three topics in stochastic frontier models using panel data via Bayesian simulations. First, the marginal posterior density of inefficiencies is approximated by a conditional density. The simulation result shows that the predictive inefficiency distribution derived from this model can capture information about inefficiencies very well. Further, we propose a data-driven adaptive bandwidth kernel estimator for the unknown frontier function. The application and simulation results imply that the adaptive bandwidth model outperforms the fixed bandwidth model. Finally, we present two sampling algorithms to model the unobserved grouped heterogeneity in the stochastic frontier mode with a latent class structure. Using the algorithms, we find evidence supporting the grouped heterogeneity in the cost frontier function.

This thesis presents dimension reduction methods for empirical probability distributions to explore the underlying structure of high-dimensional data, and these methods are applied to detect households with anomalous electricity usage patterns. The first main chapter deal with the non-Euclidean distance estimation when the data objects are probability distributions and the associated computational efficiency. The second main chapter shows how to correct the distortion from dimension reduction when estimating the density of the underlying structure. And the third main chapter explores the change in the distribution of electricity consumption brought by COVID-19 in Melbourne.

In this thesis we analyse the effect on random sampling variation on distributional forecasts and values extracted from those distributional forecasts, in a particular statistical context. Particular focus was paid to financial applications, particularly in banking, where we analysed how random sampling variation affects estimation of the size of a 1-in-100-day loss required to be calculated daily by banking institutions, and whether sampling variation alone can lead to failure of tests of models imposed by financial regulators and on the interplay between sampling variation and model selection on statistical model performance.

A forecast combination is produced by taking a weighted average of forecasts from different sources, such as different statistical models. This thesis proposes a novel method for the production of forecast combinations, and shows that this method has superior predictive accuracy relative to the traditional method most commonly used today. This finding is driven primarily by the relationship between sampling variability and predictive accuracy. A systematic exploration and comparison of these two methods is undertaken based on statistical theory, Monte Carlo simulation, and empirical data, with several implications arising for the use of forecast combination techniques in practice.

Fundamental changes have been happening in healthcare organizations and delivery in these decades, including more accessible physician information, the low-cost collection and sharing of clinical records, and decision support systems, among others. Emerging information systems and technologies play a signification role in these transformations. To extend the understanding and the implications of information systems on healthcare, my dissertation investigates the influence of information systems on enhancing healthcare operations. The findings reveal the practical value of digitalization in indicating healthcare providers' cognitive behaviors, responding to healthcare crises, and improving medical performance. The first essay investigates the unrevealed value of a special type of user-generated content in healthcare operations. In today's social media world, individuals are willing to express themselves on various online platforms. This user-generated content posted online help readers get easy assess to individuals' features, including but not limited to personality traits. To study the impact of physicians' personality traits on medicine behaviours and performance, we take a view from the perspective of user generated content posted by their supplier side as well as using physician statements which have been made available in medical review websites. It has been found that a higher openness score leads to lower mortality rates, reduced lab test costs, shorter time usage in hospitals treated by physicians with greater openness scores. Furthermore, taking these personality traits into consideration in an optimization problem of ED scheduling, the estimation of counterfactual analysis shows an average of 11.4%, 18.4%, and 17.8% reduction in in-hospital mortality rates, lab test expenditures, and lengths of stay, respectively. In future operation of healthcare, physicians' personalities should be taken into account when healthcare resources are insufficient in times of healthcare pandemics like COVID-19, as our study indicates that health service providers personality is an actual influence on clinical quality. In the second essay, we focus on the influences of the most severe healthcare pandemic in these decades, COVID-19, on digital goods consumption and examine whether digital goods consumption is resilient to an individual’s physical restriction induced by the pandemic. Leveraging the enforced quarantine policy during the COVID-19 pandemic as a quasi-experiment, we identify the influence of a specific factor, quarantine policy, on mobile app consumption in every Apple app store category in the short and long terms. In the perspective of better responding in the post-pandemic era, the quantitative findings provide managerial implications to the app industry as well as the stock market for accurately understanding the long-term impact of a significant intervention, quarantine, in the pandemic. Moreover, by using the conditional exogenous quarantine policy to instrument app users’ daily movement patterns, we are able to further investigate the digital resilience of physical mobility in different app categories and quantify the impact of an individual’s physical mobility on human behavior in app usage. For results, we find that the reduction in 10% of one’s physical mobility (measured in the radius of gyration) leads to a 2.68% increase in general app usage and a 5.44% rise in app usage time dispersion, suggesting practitioners should consider users’ physical mobility in future mobile app design, pricing, and marketing. In the third essay, we investigate the role of an emerging AI-based clinical treatment method, robot-assisted surgery (RAS), in transforming the healthcare delivery. As an advanced technique to help diminish the human physical and intellectual limitations in surgeries, RAS is expected to but has not been empirically proven to improve clinical performance. In this work, we first investigate the effect of RAS on clinical outcomes, controlling physicians' self-selection behavior in choosing whether or not to use RAS treatment methods. In particular, we focus on the accessibility of RAS and explore how physician and patient heterogeneity affect the adoption of the RAS method, including learning RAS and using RAS. Investigating the decision-making process on RAS implementation in both the learning and using stages, we show the synergy of RAS implementation in alleviating healthcare racial disparity. Ultimately, the mechanism analysis will be conducted to reveal the underlying mechanism that induces the enhancement of surgical outcomes. For instance, the estimations tend to reveal that, more than surging clinical performance, RAS tends to increase standardization in time and steps when applying the treatment procedures.

A forecast combination is produced by taking a weighted average of forecasts from different sources, such as different statistical models. This thesis proposes a novel method for the production of forecast combinations, and shows that this method has superior predictive accuracy relative to the traditional method most commonly used today. This finding is driven primarily by the relationship between sampling variability and predictive accuracy. A systematic exploration and comparison of these two methods is undertaken based on statistical theory, Monte Carlo simulation, and empirical data, with several implications arising for the use of forecast combination techniques in practice.

This paper develops a time--varying coefficient spatial autoregressive panel data model with individual fixed effects to capture the nonlinear effects of the regressors, which vary over the time. To effectively estimate the model, we propose a method that incorporates local linear estimation and concentrated quasi-maximum likelihood estimation to obtain consistent estimators for the spatial autoregressive coefficient, variance of error term and nonparametric time-varying coefficient function. The asymptotic properties of these estimators are derived as well, showing regular the standard rate of convergence for the parametric parameters and common standard rate of convergence for the time-varying component, respectively. Monte Carlo simulations are conducted to illustrate the finite sample performance of our proposed method. Meanwhile, we apply our method to study the Chinese labor productivity to identify the spatial influences and the time--varying spillover effects among 185 Chinese cities with comparison to the results on a subregion--East China.

Forecasting plays a critical role in the development of organisational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability. We refer to such forecasting practices as Forecasting for Social Good (FSG) where the benefits to society and the environment take precedence over economic and financial outcomes. We conceptualise FSG and discuss its scope and boundaries in the context of the "Doughnut theory". We present some key attributes that qualify a forecasting process as FSG: it is concerned with a real problem, it is focused on advancing social and environmental goals and prioritises these over conventional measures of economic success, and it has a broad societal impact. We also position FSG in the wider literature on forecasting and social good practices. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area. Finally, we highlight that FSG: (i) cannot be distilled to a prescriptive set of guidelines, (ii) is scalable, and (iii) has the potential to make significant contributions to advancing social objectives., Comment: 28 pages, 6 figures

The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This paper proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating this structure is that the model parameters require identifying restrictions. We identify the parameters by a trace-restriction on the covariance matrix, which is imposed through a reparametrization of the factor structure. We specify interpretable prior distributions on the model parameters and develop an MCMC sampler for parameter estimation. The proposed approach significantly improves performance in large choice sets relative to existing multinomial probit specifications. Applications to purchase data show the economic importance of including a large number of choice alternatives in consumer choice analysis., Comment: 39 pages, 12 figures. We corrected an error in coding in the previous version of the paper. The overall conclusions of the paper did not change after correction of the error

This paper proposes a novel forecast reconciliation framework using Bayesian state-space methods. It allows for the joint reconciliation at all forecast horizons and uses predictive distributions rather than past variation of forecast errors. Informative priors are used to assign weights to specific predictions, which makes it possible to reconcile forecasts such that they accommodate specific judgmental predictions or managerial decisions. The reconciled forecasts adhere to hierarchical constraints, which facilitates communication and supports aligned decision-making at all levels of complex hierarchical structures. An extensive forecasting study is conducted on a large collection of 13,118 time series that measure Swiss merchandise exports, grouped hierarchically by export destination and product category. We find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to substantial improvements in forecast accuracy. The use of state-space methods is particularly promising for optimal decision-making under conditions with increased model uncertainty and data volatility. © 2020 Elsevier B.V., European Journal of Operational Research, 291 (2), ISSN:0377-2217, ISSN:1872-6860

We use the jackknife to bias correct the log-periodogram regression(LPR) estimator of the fractional parameter in a stationary fractionally integrated model. The weights for the jackknife estimator are chosen in such a way that bias reduction is achieved without the usual increase in asymptotic variance, with the estimator viewed as `optimal' in this sense. The theoretical results are valid under both the non-overlapping and moving-block sub-sampling schemes that can be used in the jackknife technique, and do not require the assumption of Gaussianity for the data generating process. A Monte Carlo study explores the finite sample performance of different versions of the jackknife estimator, under a variety of scenarios. The simulation experiments reveal that when the weights are constructed using the parameter values of the true data generating process, a version of the optimal jackknife estimator almost always out-performs alternative semi-parametric bias-corrected estimators. A feasible version of the jackknife estimator, in which the weights are constructed using estimates of the unknown parameters, whilst not dominant overall, is still the least biased estimator in some cases. Even when misspecified short run dynamics are assumed in the construction of the weights, the feasible jackknife still shows significant reduction in bias under certain designs. As is not surprising, parametric maximum likelihood estimation out-performs all semi-parametric methods when the true values of the short memory parameters are known, but is dominated by the semi-parametric methods (in terms of bias) when the short memory parameters need to be estimated, and in particular when the model is misspecified., Comment: 57 pages

We study the estimation of heterogeneous effects of group-level policies, using quantile regression with interactive fixed effects. Our approach can identify distributional policy effects, particularly effects on inequality, under a type of difference-in-differences assumption. We provide asymptotic properties of our estimators and an inferential method. We apply the model to evaluate the effect of the minimum wage policy on earnings between 1967 and 1980 in the United States. Our results suggest that the minimum wage policy has a significant negative impact on the between-inequality but little effect on the within-inequality.

In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite-sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate rainfall, temperature and sunshine data of U.K. respectively. Overall, we find the weather of winter has changed dramatically over the past fifty years. Changes may vary with respect to locations for the other seasons.

The present special issue features a collection of papers presented at the 2017 International Panel Data Conference, hosted by the University of Macedonia in Thessaloniki, Greece. The conference marked the 40th anniversary of the inaugural International Panel Data Conference, which was held in 1977 at INSEE in Paris, under the auspices of the French National Centre for Scientific Research. As a collection, the papers appearing in this special issue of the Journal of Econometrics continue to advance the analysis of panel data, and paint a state-of-the-art picture of the field. (c) 2020 Elsevier B.V. All rights reserved.

This paper presents a new approach to hypothesis testing based on a vector of statistics. It involves simulating the statistics under the null hypothesis and then estimating the joint density of the statistics. This allows the p-value of the smallest acceptance region test to be estimated. We prove this p-value is a consistent estimate under some regularity conditions. The small-sample properties of the proposed procedure are investigated in the context of testing for autocorrelation, testing for normality, and testing for model misspecification through the information matrix. We find that our testing procedure has appropriate sizes and good powers.

The size of China’s financial system raises the possibility that the liberalization of its capital account could have a large effect on the global financial system. This paper provides a counterfactual scenario analysis that estimates what the size and direction of China’s overseas portfolio investments would have been in 2015 if China had had no restrictions on these outflows. In such a scenario, China’s holdings of overseas portfolio assets would have been between US$1.5 trillion and US$3.2 trillion (13 to 29 per cent of Chinese GDP), or 5 to 12 times its actual holdings of US$281 billion. Our model estimates that these additional holdings would have been predominantly directed to the world’s deepest financial markets, especially the United States, while emerging-market economies would have received little additional portfolio investment. These results suggest that the liberalization of Chinese portfolio outflows may not prove disruptive to the global financial system, although it could have important implications for China., Vu la taille du système financier chinois, la libéralisation du compte de capital du pays pourrait avoir une grande incidence sur le système financier mondial. Cette étude s’appuie sur une analyse contrefactuelle pour estimer ce qu’auraient été la taille et la destination des placements étrangers de la Chine en 2015 si ces sorties de capitaux n’étaient soumises à des restrictions. Selon ce scénario, le portefeuille d’actifs étrangers de la Chine aurait été compris entre 1,5 et 3,2 billions de dollars US (de 13 à 29 % du PIB du pays), soit de 5 à 12 fois la taille du portefeuille actuel de 281 milliards. D’après les estimations de nos modèles, ces avoirs supplémentaires auraient surtout été investis dans les marchés financiers les plus profonds du monde, en particulier le marché des États-Unis. Les pays émergents, pour leur part, n’auraient reçu qu’une fraction limitée de ce surcroît. Ces résultats semblent montrer que la libéralisation des placements étrangers de la Chine ne perturberait pas le système financier mondial, mais pourrait avoir d’importantes conséquences pour le pays.

We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for: (i) X dominates Y, (ii) Y dominates X and (iii) neither Y nor X dominates. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the p-values of some sampling theory tests. Resume: Evaluation bayesienne des dominances stochastiques et de Lorenz. Dans cet article, nous presentons une approche bayesienne pour evaluer les dominances stochastiques et de Lorenz. Pour deux distributions de revenus estimees par la methode de Monte-Carlo par chaines de Markov, X et Y par exemple, nous decrivons la fac¸on de calculer les probabilites a posteriori lorsque (i) X domine Y, (ii) Y domine X et (iii) ni Y ni X ne sont dominants. Nous avons applique l’approche proposee a la distribution des revenus en Indonesie en utilisant une variete de densites gamma pour garantir une modelisation flexible. Des courbes de probabilite illustrant la probabilite de dominance sur chaque proportion de population sont utilisees pour expliquer les changements de probabilite de dominance sur des fourchettes restreintes necessaires a l’evaluation des niveaux de pauvrete. Ces courbes permettent egalement d’expliquer les resultats apparemment contradictoires des valeurs p de certains tests theoriques en matiere d’echantillonnage.