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Since the seminal works by Granger and Joyeux (1980) and Hosking (1981), estimations of long-memory time series models have been receiving considerable attention and a number of parameter estimation procedures have been proposed. This paper gives an overview of this plethora of methodologies with special focus on likelihood-based techniques. Broadly speaking, likelihood-based techniques can be classified into the following categories: the exact maximum likelihood (ML) estimation (Sowell, 1992; Dahlhaus, 1989), ML estimates based on autoregressive approximations (Granger & Joyeux, 1980; Li & McLeod, 1986), Whittle estimates (Fox & Taqqu, 1986; Giraitis & Surgailis, 1990), Whittle estimates with autoregressive truncation (Beran, 1994a), approximate estimates based on the Durbin–Levinson algorithm (Haslett & Raftery, 1989), state-space-based maximum likelihood estimates for ARFIMA models (Chan & Palma, 1998), and estimation of stochastic volatility models (Ghysels, Harvey, & Renault, 1996; Breidt, Crato, & de Lima, 1998; Chan & Petris, 2000) among others. Given the diversified applications of these techniques in different areas, this review aims at providing a succinct survey of these methodologies as well as an overview of important related problems such as the ML estimation with missing data (Palma & Chan, 1997), influence of subsets of observations on estimates and the estimation of seasonal long-memory models (Palma & Chan, 2005). Performances and asymptotic properties of these techniques are compared and examined. Inter-connections and finite sample performances among these procedures are studied. Finally, applications to financial time series of these methodologies are discussed.

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

This chapter re-examines the Fama–French (FF) five-factor asset pricing model proposed by Fama and French (2015), since this model has a failure to capture the lower average returns on small stocks and its performance could not fully satisfy the original definitions of those considered factors. From the viewpoint of the econometrics analysis, we consider the inferior performance could be potentially caused by the spurious effect in the five-factor model, which could mislead the statistical inference and yield biased empirical results. We thus employ the CO-AR estimation by Wang and Hafner (2018) to prove the usefulness of the FF five-factor model. Empirical results demonstrate with the CO-AR estimation, the five-factor model indeed properly captures the lower average returns on small stocks and illustrate the sustainability of efficiency of the market, which is in contrast to the findings of Fama and French (2015). However, we propose a new perspective on the seminal five-factor model.

The purpose of this paper is to present a fast, economical and practical method for mathematical modeling of aerodynamic characteristics of rectangular wing in ground (WIG) effect.

Reynolds averaged Navier–Stokes (RANS) equations were converted to Bernoulli equation by reasonable assumptions. Also, Helmbold’s equation has been developed for calculation of the slope of wing lift coefficient in ground effect by defining equivalent aspect ratio (ARe). Comparison of present work results against the experimental results has shown good agreement.

A practical mathematical modeling with lower computational time and higher accuracy was presented for calculating aerodynamic characteristics of rectangular WIG effect. The relative error between the present work results and the experimental results was less than 8 per cent. Also, the accuracy of the proposed method was checked by comparing with the numerical methods. The comparison showed fairly good accuracy.

Aerodynamic surfaces in ground effect were used for reducing wetted surface and increasing speed in high-speed marine and novel aeronautical vehicles.

The proposed method is useful for investigation of aerodynamic performance of WIG vehicles and racing boats with aerodynamic surfaces in ground effect.

The proposed method has reduced the computational time significantly as compared to numerical simulation that allows conceptual design of the WIG crafts and is also economical.

In this study, the authors investigate methods of sequential analysis to test prospectively for the existence of a unit root against stationary or explosive states in a p-th order autoregressive (AR) process monitored over time. Our sequential sampling schemes use stopping times based on the observed Fisher information of a local-to-unity parameter. In contrast to the Dickey–Fuller (DF) test statistic, the sequential test statistic has asymptotic normality. The authors derive the joint limit of the test statistic and the stopping time, which can be characterized using a 3/2-dimensional Bessel process driven by a time-changed Brownian motion. The authors obtain their limiting joint Laplace transform and density function under the null and local alternatives. In addition, simulations are conducted to show that the theoretical results are valid.

The paper aims to analyze the application areas of augmented reality (AR) in the construction industry to enhance its usage level. The objectives are to assess the level of awareness of hardware and software devices of AR, examine the application areas of AR and reveal lagging areas in the construction industry.

A survey of construction professionals engaged in the built environment was sent a well-structured questionnaire in the study area. The professionals involved are project managers, architects, engineers, builders and quantity surveyors. The sampling technique used in selecting those professionals was convenience sampling. Descriptive and inferential statistics were used in analyzing the retrieved data.

The five most applied areas of Augmented Realities are Visualization and simulation of construction works; Project documentation; Project planning, monitoring and modification. The other two areas include on-site real-time information retrieval and health and safety measures. The findings also revealed the difference in the opinions of professionals among the variables. The results showed that there is significant difference in the opinions of the professionals regarding the application areas except one, that is automated measurement. Automated measurement has the same converging opinions of all professionals engaged in the study.

The study gives deep insight into possible areas where AR can be used in construction. The awareness level of hardware and software devices of AR was revealed by showing that those devices are upcoming in their usage. The areas of application of AR in construction are in their nascent stage. When there is proper implementation of this technology it will improve management in construction, minimize health and safety issues, and enhance the efficiency of workers through visualization and simulation.

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.

Provides a framework for analysing the financing of state pensions with a wide range of policy options. Special attention is given, however, to two special cases: the first involves a means‐tested pension similar to the Australian scheme, while the second is similar to the basic pension (the first tier) in the UK. Emphasis is given to the implications of population ageing for pension finance in each scheme; a range of policies can be considered using specially designed computer programs.