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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The purpose of this paper is to examine the impact of earnings smoothing on the underpricing of seasoned equity offerings (SEOs). It aims to investigate whether earnings smoothing can add value to firms by reducing the degree of SEO underpricing.

The sample of US common stock seasoned equity offerings (SEOs) by non‐regulated firms during 1989‐2009 was used to conduct various cross‐section, univariate, and multivariate tests, using several proxies for earnings smoothing, in order to confirm the impact of earnings smoothing on the degree of SEO underpricing. Three‐stage least square estimation was used to address the possible endogeneity of pricing and earnings smoothing.

Smooth earnings performance resulting from discretionary accruals is negatively related to SEO underpricing and improves earnings informativeness. Consistent with risk management and signaling theories, managers' efforts to produce smooth earning reports may add value to their firms. Based on the mean values for SEOs, such smoothing reduces underpricing by $0.33 per share offered and increases the value of the average offering by $1.65 million. Smoothed earnings also conveys information about the firms' future performance, as firms with a long historical pattern of smooth earnings prior to SEOs significantly outperform, for at least three years after the SEO, those with more volatile earnings, with respect to stock returns and operating performance.

The paper contributes specifically to the current literature on earnings smoothing by demonstrating that high quality firms that expect larger quantity of cash flows in the near future are more likely to actively smooth earnings via discretionary accruals before SEOs to reduce underpricing. The paper contributes generally by showing that firms can signal their quality to outside investors by showing smooth earnings over a long period of time and such firms are more likely to experience a lower degree of underpricing through SEO episodes.

When modeling heat propagation in biological bodies, a non-negligible relaxation time (typically between 15-30 s) is required for the thermal waves to accumulate and transfer, i.e. thermal waves propagate at a finite velocity. To accommodate for this feature that is characteristic to heat transfer in biological bodies, the classical Fourier's law has to be modified resulting in the thermal-wave model of bio-heat transfer. The purpose of the paper is to retrieve the space-dependent blood perfusion coefficient in such a thermal-wave model of bio-heat transfer from final time temperature measurements.

The non-linear and ill-posed blood perfusion coefficient identification problem is reformulated as a non-linear minimization problem of a Tikhonov regularization functional subject to lower and upper simple bounds on the unknown coefficient. For the numerical discretization, an unconditionally stable direct solver based on the Crank–Nicolson finite difference scheme is developed. The Tikhonov regularization functional is minimized iteratively by the built-in routine lsqnonlin from the MATLAB optimization toolbox. Both exact and numerically simulated noisy input data are inverted.

The reconstruction of the unknown blood perfusion coefficient for three benchmark numerical examples is illustrated and discussed to verify the proposed numerical procedure. Moreover, the proposed algorithm is tested on a physical example which consists of identifying the blood perfusion rate of a biological tissue subjected to an external source of laser irradiation. The numerical results demonstrate that accurate and stable solutions are obtained.

Although previous studies estimated the important thermo-physical blood perfusion coefficient, they neglected the wave-like nature of heat conduction present in biological tissues that are captured by the more accurate thermal-wave model of bio-heat transfer. The originalities of the present paper are to account for such a more accurate thermal-wave bio-heat model and to investigate the possibility of determining its space-dependent blood perfusion coefficient from temperature measurements at the final time.

This article considers that one way to help the small‐ and medium‐sized enterprise (SME) to survive is to offer it a robust but simple monitoring and control technique that would help it manage the business effectively and this, in turn, should help to increase its chances of survival. This technique should also be of interest to all people involved with monitoring or advising a large number of small enterprises or business units within a larger organization. For example, a bank manager or a small business consultant responsible for a portfolio of firms. The authors utilize process control techniques more often used in production and inventory control systems to demonstrate how one might monitor the marketing “health” of small firms.

This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted.

Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones.

The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error.

Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation.

In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

The purpose of this paper is to explore the dynamics of the relationships between sales and internal and external environmental drivers in a retail company using a systems perspective in order to support retail management decisions with nonlinear methods.

The research and results are presented in two parts: the collection and explorative analysis of the data; and discussion of the managerial implications following a systems perspective. The exploratory analysis is conducted using a statistical comparison of linear and nonlinear models of sales data from a retail company. The data, which comprise two data sets, come from 45 retail stores located in different regions of the USA.

Specifically, nonlinear models provided a better explanation of variation in retail activity (R²=46 per cent) than linear models (R²=16 per cent). In such a situation, the nonlinear analysis captures the influence of internal and external environmental drivers on retail sales.

With a limited variety of external and internal drivers, the exploratory analysis aims to describe a general situation in which retailers are managing activities in complex environments as opposed to reflect on a particular retail chain.

The systems perspective is used to interpret the managerial implications of the nonlinear analysis fits, particularly in cases where retail decision-makers are adapting, transforming and restructuring sources of competitive advantage in complex environments.

The paper provides an alternative perspective (the systemic one) of how retailers could interpret the relationships between internal and external variables in the dynamic environment of the retail chains with nonlinear models.

This study aims to trace the impact of corporate governance and its mechanisms in preventing companies from turning to fraudulent financial reporting.

For this purpose, using the systematic elimination pattern, the information of 187 listed companies on the Tehran Stock Exchange over six years from 2013 to 2019 were collected, and the hypotheses were examined using a linear regression model. To measure fraudulent financial reporting, the adjusted model of Beneish (1999) was used to evaluate corporate governance. Its mechanisms based on nine corporate governance mechanisms, including board independence, board remuneration, CEO financial expertise, expertise in CEO industry, board financial expertise, board industry expertise, board effort, CEO duality and managerial ownership, have been examined. These mechanisms are calculated as a combined index of corporate governance.

The findings indicate that robust corporate governance significantly reduces companies’ intention toward fraudulent financial reporting. In the same way, a negative and significant relationship was observed between each of the nine corporate governance mechanisms, except for board compensation and fraudulent financial reporting.

This study’s findings provide valuable insight into the importance of strengthening companies to prevent companies’ managers from engaging in fraudulent financial reporting activities. Hence, it is suggested that professional references bodies more seriously follow the rules to dictate to companies for using and empowering their corporate governance.