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Error indicators are introduced as part of a simple computational procedure for improving the accuracy of the finite element solutions for plate and shell problems. The procedure is based on using an initial (coarse) grid and a refined (enriched) grid, and approximating the solution for the refined grid by a linear combination of a few global approximation vectors (or modes) which are generated by solving two uncoupled sets of equations in the coarse grid unknowns and the additional degrees of freedom of the refined grid. The global approximation vectors serve as error indicators since they provide quantitative pointwise information about the sensitivity of the different response quantities to the approximation used. The three key elements of the computational procedure are: (a) use of mixed finite element models with discontinuous stress resultants at the element interfaces; (b) operator splitting, or additive decomposition of the finite element arrays for the refined grid into the sum of the coarse grid arrays and correction terms (representing the refined grid contributions); and (c) application of a reduction method through successive use of the finite element method and the classical Bubnov—Galerkin technique. The finite element method is first used to generate a few global approximation vectors (or modes). Then the amplitudes of these modes are computed by using the Bubnov—Galerkin technique. The similarities between the proposed computational procedure and a preconditioned conjugate gradient (PCG) technique are identified and are exploited to generate from the PCG technique pointwise error indicators. The effectiveness of the proposed procedure is demonstrated by means of two numerical examples of an isotropic toroidal shell and a laminated anisotropic cylindrical panel.

Aircraft structures are mainly connected by riveting joints, whose quality and mechanical performance are directly determined by vertical accuracy of riveting holes. This paper proposed a combined vertical accuracy compensation method for drilling and riveting of aircraft panels with great variable curvatures.

The vertical accuracy compensation method combines online and offline compensation categories in a robot riveting and drilling system. The former category based on laser ranging is aimed to correct the vertical error between actual and theoretical riveting positions, and the latter based on model curvature is used to correct the vertical error caused by the approximate plane fitting in variable-curvature panels.

The vertical accuracy compensation method is applied in an automatic robot drilling and riveting system. The result reveals that the vertical accuracy error of drilling and riveting is within 0.4°, which meets the requirements of the vertical accuracy in aircraft assembly.

The proposed method is suitable for improving the vertical accuracy of drilling and riveting on panels or skins of aerospace products with great variable curvatures without introducing extra measuring sensors.

A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.

Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.

The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.

This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Aerospace assembly demands high drilling position accuracy for fastener holes. Hole position error correction is a key issue to meet the required hole position accuracy. This paper aims to propose a combined hole position error correction method to achieve high positioning accuracy.

The bilinear interpolation surface function based on the shape of the aerospace structure is capable of dealing with position error of non-gravity deformation. A gravity deformation model is developed based on mechanics theory to efficiently correct deformation error caused by gravity. Moreover, three solution strategies of the average, least-squares and genetic optimization algorithms are used to solve the coefficients in the gravity deformation model to further improve position accuracy and efficiency.

Experimental validation shows that the combined position error correction method proposed in this paper significantly reduces the position errors of fastener holes from 1.106 to 0.123 mm. The total position error is reduced by 43.49% compared with the traditional mechanics theory method.

The position error correlation method could reach an accuracy of millimeter or submillimeter scale, which may not satisfy higher precision.

The proposed position error correction method has been integrated into the automatic drilling machine to ensure the drilling position accuracy.

The proposed position error method could promote the wide application of automatic drilling and riveting machining system in aerospace industry.

A combined position error correction method and the complete roadmap for error compensation are proposed. The position accuracy of fastener holes is reduced stably below 0.2 mm, which can fulfill the requirements of aero-structural assembly.

The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

The purpose of this paper was to examine the long-run causal relations between share price and dividend in the Indian market.

Panel vector error correction model is estimated to examine the long-run causal relations between share price and dividend. Prior to this, panel unit root tests and panel cointegration tests are carried out to test the unit root properties of the data and test for the existence of long-run cointegrating relationship between the variables, respectively.

The results of empirical investigation reveal that there exists bi-directional long-run causality between share price and dividends.

For the chosen sample, data on share price are available only for limited years. This limits the time dimension of the sample. Hence, in the future, the analysis can be extended to cover longer time series.

The interplay between share prices and dividends needs to be given due consideration by firms while framing their policies. A change in dividend policy would have an effect on the market value of the firm; hence, firms need to frame dividend policy in such a way that it would enhance their market value. Similarly, investors need to take into consideration the influence of share prices and dividends on each other. While making investment decisions, they need to consider the dividend history of shares, as better dividends would lead to better share prices.

To the best of the authors' knowledge, this study is the first attempt in the Indian market to examine the long-run causal relations between share price and dividend. The results of this study would be helpful to the investors in taking wise investment decisions. It would also enable firms in formulating appropriate dividend policies.

A simple and efficient re‐analysis procedure is presented for large‐scale structural systems. The procedure is based on using a mixed formulation with the fundamental unknowns consisting of both stress and displacement parameters. The other key elements of the procedure are: (a) lumping of the large number of design variables into a single tracing parameter; (b) operator splitting or additive decomposition of the different arrays in the finite element equations of the modified structure into the corresponding arrays of the original structure plus correction terms; and (c) application of a reduction method through the successive use of the finite element method and the classical Bubnov‐Galerkin technique. The finite element method is first used to generate a few approximation vectors (or modes). Then the amplitudes of these modes are computed by using the Bubnov—Galerkin technique. The re‐analysis procedure is applied to the linear static and free vibration problems of plate and shell structures. Changes in both the sizing and shape (configuration) design variables are considered. The high accuracy of the proposed technique, for sizable changes in the design variables, is demonstrated by means of numerical examples of composite plates and shells.

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters that characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.