MAKE MOST OF THE KNOWLEDGE NETWORK, JOIN ACADEMIC RESEARCH FOUNDATION

The missing data problem pervasively exists in statistical applications. Even as simple as the count data in mortality projections, it may not be available for certain age-and-year groups due to the budget limitations or diculties in tracing research units, resulting in the follow-up estimation and prediction inaccuracies. To circumvent this data-driven challenge, we extend the Poisson log-normal Lee-Carter model to accommodate a more exible time structure, and develop the new sampling algorithm that improves the MCMC convergence when dealing with incomplete mortality data. Via the overdispersion term and Gibbs sampler, the extended model can be re-written as the dynamic linear model so that both Kalman and sequential Kalman lters can be incorporated into the sampling scheme. Additionally, our meticulous prior settings can avoid the re-scaling step in each MCMC iteration, and allow model selection simultaneously conducted with estimation and prediction.

Under standard assumptions, the average price change of a security caused by a materially important announcement is zero. Event studies, used to test whether announcements are materially important, must therefore “bin” announcements into above or below market expectations. Otherwise, the approach would have no power to reject a null hypothesis of a no announcement effect. In situations where market expectations are unknown, we show a Wald statistic (the square of the standardized cumulative abnormal return found in standard event studies) is an easily implementable and powerful approach to test whether an event affects securities prices. We also provide three examples of its applicability.

When modelling phenomena relating to the economy, dynamic panel models have proven to be a useful tool. Previous studies have modelled dynamic panel data using conventional methods of generalized method of moment, Instrumental variables and Maximum likelihood estimators among others. This study however focuses on modelling dynamic panel data using modern day approaches of deep-learning techniques. To this end, two macro-economic variables of Purchasing Power Parity (PPP) and Gross National Income (GNI) were employed to model the economic growth of twenty African countries. Dynamic panel information about these countries were sourced from UNESCO database between 1990 and 2019. Deep learning techniques of Long Term Short memory (LSTM), Bidirectional Long Short Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) were employed in the modelling process, and the findings revealed that LSTM having the least values of the adopted evaluation metrics, is the best and most suitable deep learning method for modelling dynamic panel data. Forecasts were also made for the next 20 years with the techniques, and the results show that LSTM gives the best predicting accuracy with its lowest Mean Absolute Error (MAE), MAPE, MSE and RMSE.

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the  concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.

A reinsurance contract should address the conflicting interests of the insurer and reinsurer. Most of the existing optimal reinsurance contracts only consider the interests of one party. This article combines the proportional and stop-loss reinsurance contracts and introduces a new reinsurance contract called proportional-stop-loss reinsurance. Using the balanced loss function, unknown parameters of the proportional-stop-loss reinsurance have been estimated such that the expected surplus for both the insurer and reinsurer are maximized. Several characteristics for the new reinsurance are provided.

The primary goal of this paper was to create new classes of models based on the existing Multivariate ARCH models. The MARCH models are used to create Upper and Lower Diagonal models. The models have upper and lower diagonal element parameter restrictions in the coefficient matrices and are found to have the same comparative advantage as MARCH models. Empirical evidence from the Nigerian Urban and Rural Consumer Price Indices has identified UDMARCH and LDMARCH as new classes of models suitable for the volatility of multivariate time series.

Keywords: UDMARCH, LDMARCH, MARCH, Autocorrelations and Cross-Autocorrelations.

Weibull Pareto distribution is considered. Bayesian method of estimation is employed in order to estimate the reliability function of Weibull Pareto distribution by using non-informative and beta priors. In this paper, the Bayes estimators of the reliability function have been obtained under squared error, precautionary and entropy loss functions.

Keywords: Weibull Pareto distribution, Reliability, Bayesian method, Non-informative and beta priors, Squared error, precautionary and entropy loss functions.