A structural analysis of the correlated random coefficient wage regression model

We estimate a dynamic programming model of schooling decisions in which the log wage regression function is set within a correlated random coefficient model. We show that estimates of the dynamic programming model can be used to obtain a number of treatment effects, including the local average treatment effect (LATE). However, unlike LATE parameters obtained in a standard IV framework, our LATE estimates are obtained without imposing separability between individual specific heterogeneity and schooling choices and are therefore not subject to a “monotonicity” restriction. We find that returns to schooling are characterized by a high degree of dispersion across individuals.     

Bayesian estimation of a random coefficient model

This paper carries out a Bayesian analysis of the Hildreth-Houck (1968) random coefficient model and applies it to some cross-section production function data. Posterior distributions for mean coefficients, actual coefficients, variances and variance ratios are derived. The variance ratio posteriors are largely uninformative but they do lead to relatively informative densities on the variances, and the problem of negative variance estimates, obtained with previous techniques, is overcome. Posterior densities for the mean coefficients are not extremely sensitive to the variance ratios.     

A bayesian analysis of a random coefficient model in a simple keynesian system

The marginal propensity to consume in a simple Keynesian model is treated as a random coefficient. This gives rise to the problem of quotient of random variables, i.e., the Fieller-Creasy problem. The Bayesian and maximum likelihood estimators are compared in sampling experiments. The Bayesian estimators have smaller mean squared errors than the maximum likelihood estimators. Marginal posterior probability density functions for a given sample are also presented.     

A note on the unbiasedness of Swamy's estimator for the random coefficient regression model

In this note the estimator proposed by Swamy (1970) for the random coefficient regression model is proved to be unbiased under fairly general conditions. In addition, the conditions under which the mean of the estimator exists are derived.     

An application and test for a random coefficient model in Bangladesh agriculture

This paper deals with efficiency question by estimating four production functions for four different regions in connection with Bangladesh agriculture. Hildreth and Houck's (1968) random coefficient method was found more suitable than OLS after conducting an appropriate test for this. In contrast to earlier studies, 24 piece-wise regressions have been fitted for each index of efficiency considering all four regions and six different ranges of farm size instead of a single regression so that the relationship could be studied more closely. The study clearly indicates higher efficiency for smaller farms.     

Testing for a unit root in a random coefficient panel data model

This paper proposes new unit root tests in the context of a random autoregressive coefficient panel data model, in which the null of a unit root corresponds to the joint restriction that the autoregressive coefficient has unit mean and zero variance. The asymptotic distributions of the test statistics are derived and simulation results are provided to suggest that they perform very well in small samples.     

An alternative measure of intergenerational income mobility based on a random coefficient model

We propose an alternative measure of the degree to which income status is transmitted from one generation to another. Our indicator of intergenerational income mobility is based on a random coefficient model, which allows for variation in intergenerational mobility across families due to multiplicative unobserved family‐specific characteristics. This alternative measure suggests that intergenerational income persistence is typically stronger than when intergenerational income mobility is measured using the standard elasticity of sons' income with respect to fathers' income. Copyright © 2011 John Wiley & Sons, Ltd.     

A link between random coefficient autoregressive models and some agent based models

An agent based model (ABM), where each agent makes decisions by using the sum of two signals, is proposed. The first is related to the fundamental information while the second comes from trader’s idiosyncratic noise. This model entails the switching between two groups called fundamentalist and noise traders. Additionally, if the price impact function is log-linear, then the dynamic of log asset prices belongs to the class of random coefficient autoregressive RCA(p) models, which are known to share important stylized facts of financial prices.     

Spatial aspects of the diffusion of technological innovations among American municipal governments

Examination of the spatial diffusion patterns of a sample of 43 technological innovations organized into four functional areas—fire fighting, traffic control, air pollution control, solid waste collection, and disposal—indicates the absence of systematic patterns of either rank-size or contagious diffusion of these innovations among municipal governments in the United States. Absence of these general patterns reflects an inadequate identification in existing theories of spatial diffusion processes of variables that enter into agency decisions to adopt new technologies and the inadequate identification of possible interactions among variables. In particular, attempts to model diffusion patterns based on neighborhood effect or on hierarchical relationships fail to take into account, it least for the classes of adopters and innovations studied here, polynuclear propagation and the role of intermediaries (e.g. professional associations, governmental agencies, suppliers) in communication patterns or the role of site-specific factors that determine the “suitability” or “worth” or an innovation to different potential adopters.     

International mergers and acquisitions: A jump diffusion model application

This paper empirically investigates international mergers and acquisitions (M&As) of foreign targets and bidders by analyzing the stock price behavior of the firms involved. The jump diffusion model is employed to study the effects of the M&A announcements on stock prices. The results indicate that acquisition announcements are perceived as a surprise by the market, but prices seem to adjust rather rapidly, supporting the semi-strong form of the market efficiency hypothesis. In addition, a comparison of the pure diffusion and jump diffusion models indicates that the jump diffusion model is statistically superior to the traditional event study methodology (pure diffusion model). (JEL G34)     

Driving sectoral sustainability via the diffusion of organizational eco‐innovations

Using insights from institutional literature, the resource‐based theory of the firm, and internationalization, we explain variations in the diffusion of organizational eco‐innovations. Studies have previously reported that the drivers of eco‐innovation are regulatory pressures, technology push, market pull, and firm factors. But relatively little attention has been paid to nontechnological forms of eco‐innovation, such as environmental management systems (EMS). Consequently, how exactly to encourage EMS adoption across sectors is still unclear. We attempt to address this question by combining sectoral panel data (2009–2014) from a number of sources in Spain. The econometric analysis reveals that environmental policy is driving the adoption of ISO 14001 largely due to differences across sectors in energy and pollution intensity. In addition, the adoption of ISO 9001 increases the use of ISO 14001 in industry because of complementarities between the two systems. Third, in highly internationalized sectors, firms adopt a greater amount of ISO 14001.     

A statistic for random model testing

Variance of relative frequencies is shown to be a useful statistic for testing for the random model in social science research. An illustration from the sociology of science is provided to document the utility of the measure.     

Modeling the diffusion of public policy innovations among the U.S. States

This paper describes and validates a general model of state diffusion of public policy innovations. The model can be used to identify whether new policies are likely to become fully adopted into the system of states and the time horizon over which this is likely to occur. Limitations and extensions of the model are also concluded.     

Organizational transition management of circular business model innovations

Scholars and practitioners across fields increasingly recognize that business models for the circular economy may be an effective lever for solving ecological persistent problems such as climate change, biodiversity loss, and growing natural resource scarcity. Despite a growing interest in the potential of circular business models, interconnections between the organizational dimensions of firms and their business model innovation processes remain underexplored. Based on problem‐centered expert interviews with business consultants experienced in circular business development, this study creates a conceptual model that offers structured knowledge about why firms steadily reproduce linear BMs and how incumbents manifest themselves as a constant linear‐oriented value creation system. The model also demonstrates organizational conditions and management strategies that frustrate the reproduction of linear BMs and, thus, enable initial moves towards CBM innovation. Building on this, the article provides a set of propositions on how an organizational transition management may be configured and what incumbents require to successfully navigate circular business model innovation. The findings provide a foundation for a contemporary understanding of circular business model transition management, which simultaneously serve as impulses for future research investigations.     

Testing the adequacy of varying coefficient models with missing responses at random

In this paper, we investigate checking the adequacy of varying coefficient models with response missing at random. In doing so, we first construct two completed data sets based on imputation and marginal inverse probability weighted methods, respectively. The empirical process-based tests by using these two completed data sets are suggested and the asymptotic properties of the test statistics under the null and local alternative hypotheses are studied. Because the limiting null distribution is intractable, a Monte Carlo approach is applied to approximate the distribution to determine critical values. Simulation studies are carried out to examine the performance of our method, and a real data set from an environmental study is analyzed for illustration.     

Innovation diffusion: A deterministic model of space-time integration with physical analog

This paper extends a fundamental temporal diffusion model to integrate space and time dimensions of the diffusion of innovation. The analogous developments in physical sciences are compared and it is demonstrated that the proposed model may be the first step in linking the concept of catalysts in physical science diffusion processes to the role of change agents in social science systems.