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IV estimation is examined when some instruments may be invalid. This is relevant because the initial just-identifying orthogonality conditions are untestable, whereas their validity is required when testing the orthogonality of additional instruments by so-called overidentification restriction tests. Moreover, these tests have limited power when samples are small, especially when instruments are weak. Distinguishing between conditional and unconditional settings, we analyze the limiting distribution of inconsistent IV and examine normal first-order asymptotic approximations to its density in finite samples. For simple classes of models we compare these approximations with their simulated empirical counterparts over almost the full parameter space. The latter is expressed in measures for: model fit, simultaneity, instrument invalidity, and instrument weakness. Our major findings are that for the accuracy of large sample asymptotic approximations instrument weakness is much more detrimental than instrument invalidity. Also, IV estimators obtained from strong but possibly invalid instruments are usually much closer to the true parameter values than those obtained from valid but weak instruments.

The aim of this study is to examine whether China’s exchange rate follows an equilibrium process and consequently to answer the question of whether or not China’s international competitiveness fluctuates in consistency with equilibrium.

The theoretical background of the paper relies on the Purchasing Power Parity (PPP) hypothesis, while the econometric methodology is mainly based on a nonlinear two-regime Threshold Autoregressive (TAR) unit root test.

The main finding is that China’s price competitiveness was not constantly following a disequilibrium process. The two-regime threshold model shows that PPP equilibrium was confirmed in periods of relatively high – compared to the estimated threshold – rate of real yuan appreciation. Moreover, it is implied that the fixed exchange rate regime cannot ensure external balance since it can neither establish equilibrium in the foreign exchange market, nor confirm that China’s international competitiveness adjustment follows an equilibrium process.

The results do not imply that China acts as a currency manipulator. However, a main policy implication of the paper is that China should continue appreciating the yuan to establish external balance.

This paper is the first which accounts for a nonlinear two-regime process toward a threshold, which is defined to be the rate of change in China’s international competitiveness. Consequently, the paper draws attention to the role of China’s international competiveness in accepting the PPP hypothesis.

The purpose of this paper is to describe a general theoretical and finite element implementation framework for the constitutive modelling of biological soft tissues.

The model is based on continuum fibers reinforced composites in finite strains. As an extension of the isotropic hyperelasticity, it is assumed that the strain energy function is decomposed into a fully isotropic component and an anisotropic component. Closed form expressions of the stress tensor and elasticity tensor are first established in the general case of fully incompressible plane stress which orthotropic and transversely isotropic hyperelasticity. The incompressibility is satisfied exactly.

Numerical examples are presented to illustrate the model's performance.

The paper presents a constitutive model for incompressible plane stress transversely isotropic and orthotropic hyperelastic materials.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

The recent financial and sovereign debt crises around the world have sparked a growing literature on models and empirical estimates of defaultable debt. Frequently households and firms come under default threat, local governments can default, and recently sovereign default threats were eminent for Greece and Spain in 2012–2013. Moreover, Argentina experienced an actual default in 2001. What causes sovereign default risk, and what are the escape routes from default risk? Previous studies such as Arellano (2008), Roch and Uhlig (2013), and Arellano et al. (2014) have provided theoretical models to explore the main dynamics of sovereign defaults. These models can be characterized as threshold models in which there is a convergence toward a good no-default equilibrium below the threshold and a default equilibrium above the threshold. However, in these models aggregate output is exogenous, so that important macroeconomic feedback effects are not taken into account. In this chapter, we (1) propose alternative model variants suitable for certain types of countries in the EU where aggregate output is endogenously determined and where financial stress plays a key role, (2) show how these model variants can be solved through the Nonlinear Model Predictive Control numerical technique, and (3) present some empirical evidence on the nonlinear dynamics of output, sovereign debt, and financial stress in some euro areas and other industrialized countries.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

The threshold regression framework is used to examine the effect of foreign direct investment on growth in Sub-Saharan Africa (SSA). The growth literature is awash with divergent evidence on the role of foreign direct investment (FDI) on economic growth. Although the FDI–growth nexus has been studied in diverse ways, very few studies have examined the relationship within the framework of threshold analysis. Furthermore, even where this framework has been adopted, none of the previous studies has comprehensively examined the FDI–growth nexus in the broader SSA. In this paper, within the standard panel and threshold regression framework, the problem of determining the growth impact of FDI is revisited.

Six variables are used as thresholds – inflation, initial income, population growth, trade openness, financial market development and human capital, and the analysis is based on a large panel data set that comprises 45 SSA countries for the years 1985–2013.

The results of this study show that the direct impact of FDI on growth is largely ambiguous and inconsistent. However, under the threshold analysis, it is evident that FDI accelerates economic growth when SSA countries have achieved certain threshold levels of inflation, population growth and financial markets development. This evidence is largely invariant qualitatively and is robust to different empirical specifications. FDI enhances growth in SSA when inflation and private sector credit are below their threshold levels while human capital and population growth are above their threshold levels.

The contribution of this paper is twofold. First, the paper streamlines the threshold analysis of FDI–growth nexus to focus on countries in SSA – previous studies on FDI-growth nexus in SSA are country-specific and time series–based (see Tshepo, 2014; Raheem and Oyınlola, 2013 and Bende-Nabende, 2002). This paper provides a panel analysis and considers a broader set of up to 45 SSA countries. Such a broad set of SSA countries had never been considered in the literature. Second, the paper expands on available threshold variables to include two new important macroeconomic variables, population growth and inflation which, though are important absorptive capacities but, until now, had not been used as thresholds in the FDI–growth literature. The rationale for including these variables as thresholds stems from the evidence of an empirical relationship between population growth and economic growth, see Darrat and Al-Yousif (1999), and between inflation and economic growth, see Kremer et al. (2013).

The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities. A comparison between a conventional SMPM, which has radially magnetized rotor, and a Halbach machine has been carried out.

The geometric parameters of the two machines have been optimized using genetic algorithm (GA) with looking Pareto. The performance of the machines’ geometry has been calculated by finite element analysis (FEA) software, and two parametric machine models have been realized in Matlab coupled with the FEA and GA toolboxes. Outer volume of the machine, thus copper loss per volume has been kept constant. The Pareto front approach, which simultaneously considers looks two aims, has been used to provide the trade-off between the torque and sensorless performances.

The two machines’ results have been compared separately for each loading condition. According to the results, the superiority of the Halbach machine has been shown in terms of sensorless capability compromising torque performance. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.

A Halbach machine design optimization has been presented using Pareto optimal set which provides a trade-off comparison between two aims without using weightings. These are sensorless performance and torque capability. There is no such a work about sensorless capability of the Halbach type SMPM in the literature.

There can be activities that cannot reduce times by conventional single minute exchange of die (SMED) tools. In this case more advanced tools are needed. The purpose of this paper is to integrate the fuzzy Taguchi method into the SMED method in order to improve the setup time. The reason for using fuzzy logic is the subjective evaluation of factor’s levels assessment by experts. Subjective assessment contains a certain degree of uncertainty and is vagueness. The fuzzy Taguchi method provides to determining optimal setup time parameters in an activity of SMED. So it is possible to reduce time more than the conventional SMED method.

In this study, the SMED method and the fuzzy Taguchi method are used.

In this study, it has been shown that the setup time is reduced (from 196 to 75 min) and the optimum value can be given at the intermediate value by the fuzzy Taguchi method.

In this limited literature research, the authors have not found a study using the fuzzy Taguchi method in the SMED method.

The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.

Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.

Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.

Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.