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This paper aims to investigate the determinants of bilateral exports of India to the USA by taking the non-linearity issue in export demand equations which is neglected so far in the empirical work. The study tries to know the reaction of change in exports to exchange rate changes in a non-liner fashion. For this purpose, non-linear autoregressive distributed lag (NARDL) bounds testing approach of Shin et al. (2011) has been used. This approach allows testing for non-linearities both in the short and long run, which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The overall results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The assumed linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations delivers plausible results. If exchange rate non-linearities are detected, it would seem that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The paper uses the newly developed non-linear ARDL framework of Shin et al. (2011) to investigate whether there are non-linearities with respect to the exchange rate for India’s exports to the USA. One of the important features of this framework is that it is free from unit root pre-testing and can be applied regardless of whether variables are I(0) or I(1). In addition, ARDL and NARDL technique efficiently determines the cointegrating relation in small sample. The short-run and long-run parameters with appropriate asymptotic inferences can be obtained by applying OLS to NARDL with an appropriate lag length. Following is the NARDL representation of equation 4(a) and 4(b). For brevity, this is illustrated for 4(a) only, where is the first difference operator, P is the drift component and it is the white noise residual, the coefficients ?_1 to ?_4 represent the long-run relationship, whereas remaining expressions with summation sign represent the short-term dynamics of the model. This equation nests the linear ARDL model presented in Pesarean et al. (2001) for the case of d_k^+=d_k^-and ?_2=?_3for all k. Thus, equation is less restrictive than a linear model. For this test, as its distribution is non-standard, Pesarean et al. (2001) tabulate the critical values. The bound test is used to examine the existence of the long-run relationship among the variables in the system. This test is based on Wald/F-statistic and follows a non-standard distribution. To check whether a cointegrating relationship exists, one has to test the null hypothesis ?_1=?_2=?_3=?_4 = 0 in the equation. Pesarean et al. (2001) provide two sets of critical values in which lower critical bound assumes that all the variables in the ARDL are I(0) and upper critical bound assumes I(1). The null hypothesis of cointegration is rejected if the calculated F-statistics is greater than the upper bound critical values. If the F-statistics is below than the lower critical bound, then null hypothesis cannot be rejected; this indicates no cointegration among the variables. If it lies within the lower and upper bounds, the result is inconclusive. After examining the cointegration, long-run coefficients are calculated by estimating the model with the appropriate lag orders based on the Schwarz Information Criteria (SIC). Further, the short-run dynamics of the model is also analyzed by using unrestricted error correction model based on the assumption made by Pesarean et al. (2001). Thus, the error correction version of the NARDL model pertaining to the central export equation can be expressed as: 10; 10, where ? is the speed of adjustment parameter, and EC is the residuals that are obtained from the estimated cointegration model of equation 4(a). The EC term is expressed as 10; 10, where are the OLS estimators obtained from the equation (5a). The coefficients of the lagged variables provide the short-run dynamics of the model covering the equilibrium path. The error correction coefficient ( ) is expected to be less than zero, and its significant value implies the cointegration relation among the variables. Finally, various tests such as serial correlation, functional form, normality and heteroskedasticity have been conducted to check the performance of the model.

Many empirical studies have estimated the elasticities of different final export demand components with respect to the exports because of their importance in trade policy formulation. But all the work has accounted only linearity in the exchange rate in export demand equation. Hence, in this paper, we tried to estimate non-linearities in export demand equation. The study fills the gap in the literature by improving on existing literature with the incorporation of the newly developed NARDL approach of Shin et al. (2011). This approach allows testing for non-linearities both in the short- and in the long run which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The bound test shows that there exists cointegration among the variables. Results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The long-run coefficients have got the expected sign and are of reasonable magnitude and statistically significant. Regarding non-linearities, the results show that assuming linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations deliver plausible results. If exchange rate non-linearities are detected, it seems that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The originality of this paper lies in the fact that it applies NARDL approach to Indian trade data (export demand) and analyzes the asymmetrical and non-linear impact of exchange rate changes on Indian exports.

The smart image sensor (SIS) which integrated with both sensor and smart processor has been widely applied in vision-based intelligent perception. In these applications, the linearity of the image sensor is crucial for better processing performance. However, the simple source-follower based readout circuit in the conventional SIS introduces significant nonlinearity. This paper aims to design a low-power in-pixel buffer circuit applied in the high-linearity SIS for the smart perception applications.

The linearity of the SIS is improved by eliminating the non-ideal effects of transistors and cancelling dynamic threshold voltage that changes with the process variation, voltage and temperature. A low parasitic capacitance low leakage switch is proposed to further improve the linearity of the buffer. Moreover, an area-efficient SIS architecture with a sharing mechanism is presented to further reduce the number of in-pixel transistors.

A low parasitic capacitance low leakage switch and a gate-source voltage pre-storage method are proposed to further improve the linearity of the buffer. Nonlinear effects introduced by parasitic capacitance switching leakage, etc., have been investigated and solved by proposing low-parasitic and low-leakage switches. The linearity is improved without a power-hungry operational amplifier-based calibration circuit and a noticeable power consumption increment.

The proposed design is implemented using a standard 0.18-µm CMOS process with the active area of 102 µm². At the power consumption of 5.6 µW, the measured linearity is −63 dB, which is nearly 27 dB better than conventional active pixel sensor (APS) implementation. The proposed low-power buffer circuit increase not only the performance of the SIS but also the lifetime of the smart perception system.

This paper aims to address empirical analyses of the association of the VAIC and its components with firms’ market and financial performance, demonstrating that the ill definition of variables and constructs is responsible for a non-linearity concealed in the VAIC formula between two of its components (the Structural Capital Efficiency and the Human Capital Efficiency).

Through a conceptual analysis the paper identifies and formalises the non-linearity concealed in the VAIC formula and clarifies the relevant issues through an empirical analysis of a sample of Italian listed companies.

The paper finds that the non-linearity hidden in the VAIC formula should lead scholars to completely revise the ways they test the association of the VAIC and the market and financial performance of the firm. Useful insights are also provided for scholars interested in investigating the role of human capital, for those involved in analysing the interrelations among capitals through the introduction of interaction terms in their regression models and for researchers proposing modified versions of the VAIC. Practitioners could benefit from the paper as the non-linearity here discovered leads to a substantial of the decision-making based on the VAIC.

The paper offers new insights into analyses using the VAIC as it uncovers a non-linearity hidden in the VAIC, which has hitherto not been reported in the literature. The existence of this non-linearity has substantive implications for previous and future research in this domain.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

There is an unbalanced magnetic pull between the rotor and stator of the cage induction motor when the rotor is not concentric with the stator. These forces depend on the position and motion of the centre point of the rotor. In this paper, the linearity of the forces in proportion to the rotor eccentricity is studied numerically using time‐stepping finite element analysis. The results show that usually the forces are linear in proportion to the rotor eccentricity. However, the closed rotor slots may break the spatial linearity at some operation conditions of the motor.

The purpose of this paper is to re-examine the weak form efficiency of five African stock markets (South Africa, Nigeria, Egypt, Ghana and Mauritius) using various tests to assess the impact of non-linearity effect and thin trading which are prevalent in African markets on market efficiency.

The weekly returns of S&P/IFC return indices for five African countries over the period 2000-2013 were obtained from DataStream and analyzed. The study adopted the newly developed Non-Linear Fourier unit root test advanced by Enders and Lee (2004, 2009) which allows for an unknown number of structural breaks with unknown functional forms and non-linearity in data generating process of stock prices series to test the Random Walk Hypothesis (RWH) for the five markets, and an augment regression model.

In light of the empirical evidence the author(s) using Non-linear Fourier Unit Root Test only fail to reject the RWH for South Africa, Nigeria and Egypt leading to the conclusion that these markets follow the RWH and weak-form efficient whilst Ghana and Mauritius are weak-form inefficient. Besides, evaluating non-linear models without adjusting for thin trading effect shows that, South Africa and Ghana markets are weak-form efficient while Nigeria, Egypt and Mauritius are not. However, after accounting for thin trading effect, the author(s) find that South Africa and Egypt markets follow the RWH. The findings imply that market efficiency results depend on the methodology used.

This paper provides further evidence on stock market efficiency in emerging markets. The finding suggests that thin trading and non-linearity effect influences markets efficiency tests in African stock markets. Thus, recent structural adjustment and liberalization policies have not enhanced stock market operations in Africa. This paper therefore has implications for policy makers and international investors.

We present a new procedure for nonparametric Bayesian estimation of regression functions. Specifically, our method makes use of an idea described in Frühwirth-Schnatter and Wagner (2010) to impose linearity exactly (conditional upon an unobserved binary indicator), yet also permits departures from linearity while imposing smoothness of the regression curves. An advantage of this approach is that the posterior probability of linearity is essentially produced as a by-product of the procedure. We apply our methods in both generated data experiments as well as in an illustrative application involving the impact of body mass index (BMI) on labor market earnings.