Choice behavior is typically evaluated by assuming that the data is generated by one latent decision-making process or another. What if there are two (or more) latent decision-making processes generating the observed choices? Some choices might then be better characterized as being generated by one process, and other choices by the other process. A finite mixture model can be used to estimate the parameters of each decision process while simultaneously estimating the probability that each process applies to the sample. We consider the canonical case of lottery choices in a laboratory experiment and assume that the data is generated by expected utility theory and prospect theory decision rules. We jointly estimate the parameters of each theory as well as the fraction of choices characterized by each. The methodology provides the wedding invitation, and the data consummates the ceremony followed by a decent funeral for the representative agent model that assumes only one type of decision process. The evidence suggests support for each theory, and goes further to identify under what demographic domains one can expect to see one theory perform better than the other. We therefore propose a reconciliation of the debate over two of the dominant theories of choice under risk, at least for the tasks and samples we consider. The methodology is broadly applicable to a range of debates over competing theories generated by experimental and non-experimental data.

We run an eye-tracking experiment to investigate whether players change their gaze patterns and choices after they experience alternative models of choice in one-shot games. In phase 1 and 3, participants play 2 × 2 matrix games with a human counterpart; in phase 2, they apply specific decision rules while playing with a computer with known behavior. We classify participants in types based on their gaze patterns in phase 1 and explore attentional shifts in phase 3, after players were exposed to the alternative decision rules. Results show that less sophisticated players, who focus mainly on their own payoffs, change their gaze patterns towards the evaluation of others’ incentives in phase 3. This attentional shift predicts an increase in equilibrium responses in relevant classes of games. Conversely, cooperative players do not change their visual analysis. Our results shed new light on theories of bounded rationality and on theories of social preferences.

Pests and diseases like citrus greening that threaten agricultural productivity also pose a risk to consumers. Reductions in food supply due to outbreaks and spread could increase food prices. We model U.S. household fruit demand using a Quadratic Almost Ideal Demand System and data from Circana’s 2020 and 2021 household panel. Price and income elasticity estimates reveal how household behavior might adjust with shocks to citrus and other fruit prices. Shocks to retail fruit prices can be from either citrus greening or other phenomena such as adverse weather. We also use compensating variation to estimate the impact that changes in fruit prices could have on consumer welfare.

The paper builds a parsimonious US business cycle SVARMA model, establishing identification conditions for independent monetary shocks. The SVARMA model, utilizing Divisia M3 and Divisia M4, is compared to the simple sum M2. The monetary rule with Divisia M3 yields theoretically consistent results marked by the absence of the usual price and liquidity puzzles. As the Federal Reserve (Fed) took a more hawkish approach to curb inflation, significant increases in US interest rates and declines in monetary aggregates were largely influenced by the Fed’s reaction function, which incorporates the Divisia M3 monetary rule. Findings emphasize the monetary impact on the business cycle, highlighting the significance of Divisia monetary aggregates. Historical and variance decompositions reveal diverse, dynamic effects of monetary shocks on macroeconomic variables. The SVARMA model with Divisia M3 and M4 demonstrates superior performance over simple sum M2 in capturing the time path of monetary shocks.

Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

High-cardinality categorical features are pervasive in actuarial data (e.g., occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings.

In this work, we present a novel Generalised Linear Mixed Model Neural Network (“GLMMNet”) approach to the modelling of high-cardinality categorical features. The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models. Further, its flexibility to deal with any distribution in the exponential dispersion (ED) family makes it widely applicable to many actuarial contexts and beyond. In order to facilitate the application of GLMMNet to large datasets, we use variational inference to estimate its parameters—both traditional mean field and versions utilising textual information underlying the high-cardinality categorical features.

We illustrate and compare the GLMMNet against existing approaches in a range of simulation experiments as well as in a real-life insurance case study. A notable feature for both our simulation experiment and the real-life case study is a comparatively low signal-to-noise ratio, which is a feature common in actuarial applications. We find that the GLMMNet often outperforms or at least performs comparably with an entity-embedded neural network in these settings, while providing the additional benefit of transparency, which is particularly valuable in practical applications.

Importantly, while our model was motivated by actuarial applications, it can have wider applicability. The GLMMNet would suit any applications that involve high-cardinality categorical variables and where the response cannot be sufficiently modelled by a Gaussian distribution, especially where the inherent noisiness of the data is relatively high.

By examining the econometric interrelationships of rice export prices of India, Thailand, Vietnam, Pakistan, the U.S., Argentina, Brazil, and Uruguay, this study examines the most influential rice-exporting countries in the international rice export market. The sampled countries are the source of more than 80% of the global rice exports. In the process, this study relies on online data portals of the United Nations and country-level sources for aromatic and non-aromatic rice export price information from India, Pakistan, and Thailand. This study employed the Prais-Winsten and Autoregressive Distributive Lag (ARDL) estimation processes with the error correction procedure. Combining the findings from both models, this study concludes that in terms of interactions, the non-aromatic long-grain rice price of the U.S., and the aromatic rice price of Pakistan are the most correlated prices in the international rice market. Considering the number of countries influenced by the price of a given country, our results indicate that Vietnam is the price leader in the non-aromatic long-grain market, followed by Argentina, Brazil, and India. India, the largest exporter, is the fourth top price leader. In the case of aromatic rice, Thailand is the absolute leader. Many countries in Asia and Africa rely on rice imports to meet domestic demand, where the food security situation is already precarious. Based on the findings, this study suggests closely monitoring rice production and export trends of Vietnam, Argentina, Brazil, India and Thailand to provide early warning when necessary to better manage supply shocks in the international rice market.

The paper is concerned with common shock models of claim triangles. These are usually constructed as linear combinations of shock components and idiosyncratic components. Previous literature has discussed the unbalanced property of such models, whereby the shocks may over- or under-contribute to some observations. The literature has also introduced corrections for this. The present paper discusses “auto-balanced” models, in which all shock and idiosyncratic components contribute to observations such that their proportionate contributions are constant from one observation to another. The conditions for auto-balance are found to be simple and applicable to a wide range of model structures. Numerical illustrations are given.

We investigate the impact of five types of subsidies granted under the European Union Common Agricultural Policy on the persistent and transient inefficiency of Polish dairy farms. Our research shows that coupled and environmental subsidies reduce transient technical inefficiency, while the opposite is true for Less Favoured Areas (LFA) and other rural subsidies. Simultaneously, environmental, LFA, and other rural subsidies increase persistent technical inefficiency. These results imply that the impact of each type of subsidy on technical efficiency can be different and that the effect of the particular type of subsidy can vary between transient and persistent technical inefficiency.

A recent contribution to research on age and well-being asserts that the impact of age on happiness is ‘u-shaped’ virtually everywhere. I evaluate that finding for European countries, considering whether it is robust to alternative methodological approaches. The analysis excludes control variables that are affected by age (noting that those variables are not antecedents of age) and explores the relationship via models that do not impose a quadratic functional form. The article shows that these alternative approaches do not produce a u-shape ‘everywhere’: u-shapes are evident for some countries, but for others, the pattern is quite different.

Innon-life insurance, the payment history can be predictive of the timing of a settlement for individual claims. Ignoring the association between the payment process and the settlement process could bias the prediction of outstanding payments. To address this issue, we introduce into the literature of micro-level loss reserving a joint modeling framework that incorporates longitudinal payments of a claim into the intensity process of claim settlement. We discuss statistical inference and focus on the prediction aspects of the model. We demonstrate applications of the proposed model in the reserving practice with a detailed empirical analysis using data from a property insurance provider. The prediction results from an out-of-sample validation show that the joint model framework outperforms existing reserving models that ignore the payment–settlement association.

Spatial data are a rich source of information for actuarial applications: knowledge of a risk’s location could improve an insurance company’s ratemaking, reserving or risk management processes. Relying on historical geolocated loss data is problematic for areas where it is limited or unavailable. In this paper, we construct spatial embeddings within a complex convolutional neural network representation model using external census data and use them as inputs to a simple predictive model. Compared to spatial interpolation models, our approach leads to smaller predictive bias and reduced variance in most situations. This method also enables us to generate rates in territories with no historical experience.

Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.

When studying the Federal Open Market Committee’s (FOMC’s) interest rate rule, some authors, such as Gonzalez-Astudillo [(2018) Journal of Monetary, Credit, and Banking 50(1), 115–154.], find evidence for changes in inflation and output gap responses. Others, such as Sims and Zha [(2006) American Economic Review 96(1), 54–81.], only find evidence for a change in the variance of the interest rate rule. In this paper, I develop a new two-regime Markov-switching model that probabilistically performs variable selection and identification of parameter change for each variable in the model. I find substantial evidence that there have been changes in the FOMC’s response to the unemployment gap and in the volatility of the rule. When the FOMC responds strongly to the unemployment gap, I find a bimodal density for the inflation response coefficient. Despite the bimodal density, there is a low probability that there have been changes in the FOMC’s response to inflation.

We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫmx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

In 2008 European Commission launches the open access infrastructure for research in Europe project (OpenAIRE), supporting open access (OA) in scientific information and research output. In this paper, we assess the economic sustainability of the OpenAIRE project. The empirical strategy is developed through a Cost–Benefit Analysis framework to evaluate and compare the costs and benefits of OpenAIRE services to provide recommendations on the project’s economic efficiency and sustainability, a non-market valuation method based on the results of a “Choice Experiment” to calculate the Total Economic Value generated by OpenAIRE and a full preference ranking approach. Findings indicate that stakeholders prefer interoperability between research platforms and output, better access to scientific results and compliance to OA mandates. Furthermore, net social benefits for the basic services for 15 years are at least five times higher than costs’ present value while the potential R&D effect from research suggests even larger benefits in the long run. Subscriptions based on the estimated willingness to pay and cost, institutional subsidies and public awareness are the main recommendations for the sustainable operation of OpenAIRE. This study contributes to the literature on monetary valuation of the benefits and costs of OA to scientific knowledge.

Variable annuity (VA) policies are typically issued on mutual funds invested in both fixed income and equity asset classes. However, due to the lack of specialized models to represent the dynamics of fixed income fund returns, the literature has primarily focused on studying long-term investment guarantees on single-asset equity funds. This article develops a mixed bond and equity fund model in which the fund return is linked to movements of the yield curve. Theoretical motivation for our proposed specification is provided through an analogy with a portfolio of rolling horizon bonds. Moreover, basis risk between the portfolio return and its risk drivers is naturally incorporated into our framework. Numerical results show that the fit of our model to Canadian VA data is adequate. Finally, the valuation of VAs is illustrated and it is found that the prevailing interest rate environment can have a substantial impact on guarantee costs.

Reproductive externalities are important for fertility behavior in Kenya. We identify from anthropology structural forms of social interaction operating across individuals belonging to different ethnic and religious groups on the number of children ever born. We use the 1998 Demographic and Health Survey, combined with primary meteorological data on Kenya, and GMM methods, to show that social interaction effects by ethnicity are important over and above an individual's characteristics such as their religion to explain variations in fertility. Our findings have implications for policy debates in Kenya and in other developing countries about ethnic, religious, and other differences in fertility behavior.

The addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.