Preliminary test estimators of the parameters of simple linear model with measurement error

In this paper, we discuss the properties of preliminary test estimators (PTE) of the parameters of simple linear model with measurement error (ME model) when the slope of the linear model is suspected to be zero. Expressions of the bias, MSE and efficiencies are obtained under conditional as well as unconditional situations with known reliability coefficient. Conditional model results are compared to the standard model without measurement error. We also provide the unconditional model analysis in finite samples. Asymptotic theory under local alternatives is developed when the variance of measurement error or the ratio of the variance of the model error relative to the variance of the measurement error is known. Asymptotic expressions of bias and MSE of the estimators along with their efficiencies are obtained. In every case, it is shown that the measurement error tend to increase the variability of the estimators compared to the estimators without measurement error. Graphs and tables are provided to see these results and to determine optimum level of significance for minimum guaranteed efficiency. Received October 2001 RID="*" ID="*"  A. K. Md. E. Saleh is a Distinguished Research Professor and H. M. Kim is a Ph.D. candidate in the School of Mathematics and Statistics, Carleton University, Ottawa. Acknowledgment. The authors gratefully acknowledge the constructive suggestion of the referees to improve the paper. The research is supported by NSERC grant A3088.     

An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.     

Does remittance fuel labour productivity in Bangladesh? The application of an asymmetric non-linear ARDL approach

Bangladesh, a labour-abundant country, is finally learning to reap gains from her labour abundance. The historical under-optimized capital–labour ratio of Bangladesh is slowly moving towards its optimum as the migration of its labour force and consequent inflow of remittance strategically converts its abundant labour into capital. Though the fear that remittance as an alternative source of earning may result in Dutch disease for existing family members; annual data for 1982–2013 and through applying standard testing methodologies, our empirical findings suggest that such a fear is not valid since remittance flow significantly improves domestic labour productivity in Bangladesh in the long run.     

Exploring the widening food gap: an international perspective

Using data for a long panel of 90 developed and developing countries, this article explores the effects of research and development (R&D) and fixed capital stock on agricultural land productivity over the period 1961–2012. Instruments are used for R&D to deal with feedback effects and measurement errors. The results show very high social returns to investment in R&D and to fixed capital stock, suggesting that increasing investment in these factors are promising ways of arresting the increasing food prices due to increasing demand for animal protein, population growth, desertification, salinization, soil erosion, climate change, and decreasing growth in land productivity.     

Does institutional quality resolve the Lucas Paradox?

The Lucas Paradox observes that capital flows predominantly to relatively rich countries, contradicting the neoclassical prediction that it should flow to poorer capital-scarce countries. In an influential study, Alfaro, Kalemli-Ozcan, and Volosovych (AKV) argue that cross-country variation in institutional quality can fully explain the Paradox, contending that if institutional quality is included in regression models explaining international capital inflows, a country’s level of economic development is no longer statistically significant. We replicate AKV’s results using their cross-sectional IFS capital flow data. Motivated by the importance of conducting inference in statistically adequate models, we focus on misspecification testing of alternative functional forms of their empirical model of capital flows. We show that their resolution of the Paradox relies on inference in a misspecified model. In models that do not fail basic misspecification tests, even though institutional quality is a significant determinant of capital inflows, a country’s level of economic development also remains a significant predictor. The same conclusions are reached using an extended dataset covering more recent IFS international capital flow data, first-differenced capital stock data and additional controls.     

The Effect of Corporate Governance on Firm Stock Volatility in Asia

The authors examine whether firm corporate governance (CG) contributes to lower stock-return volatility. Using the panel data of 1,252 public listed firms in Asia across 11 countries for 15 years, the authors document international evidence that CG has a stabilizing effect on firm stock-return volatility. The authors further examine whether increasing information efficiency, reducing foreign exposure, and a lower cost of capital contribute to the stabilizing effect of firm CG on stock-return volatility. The result implies that better CG will only reduce stock-return volatility for firms that have less foreign exposure.     

How well do conservation auctions perform in achieving landscape‐level outcomes? A comparison of auction formats and bid selection criteria

This paper studies the performance of auction design features regarding pricing mechanisms and bid selection criteria for securing wildlife zones across different holdings. We compare two pricing mechanisms: a discriminatory‐price auction and a uniform‐price ascending auction, and four bid selection criteria on the basis of: total bid, bid‐per‐value ratio, bid‐per‐area ratio and a mixed criterion where bids are formed on the basis of cost but they are selected based on the bid‐per‐value ratio. We develop a best‐response group‐bidding model for a discriminatory‐price auction where bidders form optimal group bids for individual wildlife zones. In the uniform‐price ascending auction, individual landholders respond to prices, which are successively raised by the auctioneer and whenever all the landholders from a single zone agree to participate (i.e. the first zone is formed), the auction stops. Based on numerical simulations using a bio‐economic model of malleefowl conservation, we observe that the discriminatory‐price auction is more cost‐effective than the uniform‐price ascending auction. However, the budgetary cost‐effectiveness of a discriminatory‐price auction is sensitive to bidder uncertainty about the number of competing bidder groups and the highest cost of establishing a wildlife zone among these groups. In terms of bid selection, the mixed bid selection criterion performs best. We discuss the policy implications of these findings.     

Farmers' Preference for Farming: Evidence From a Nationally Representative Farm Survey in India

Using data from a nationally representative farm survey in India, we have analyzed Indian farmers' stated preference for farming as a profession. Findings show that more than 40% of farmers dislike farming as a profession because of low profits, high risk, and lack of social status, yet they continue with it owing to a lack of opportunities outside agriculture. Farmers who express a preference for moving out of agriculture are mostly those with small landholdings, poor irrigation facilities, fewer productive assets including livestock, and follow a cereal‐centric cropping pattern. They also have relatively lower access to credit, insurance, and information, and are weakly integrated with social networks such as self‐help groups and farmers' organizations. Importantly, the disinclination for farming, conditional on other covariates, is not significantly differentiated by caste, an important indicator of social status in rural India. Yet, within a caste group, the dislike for farming moderates with larger landholdings.     

Participation index: a measure to identify rural transport disadvantage?

This paper develops a composite participation index (PI) to identify patterns of transport disadvantage in space and time. It is operationalised using 157 weekly activity-travel diaries data collected from three case study areas in rural Northern Ireland. A review of activity space and travel behaviour research found that six dimensional indicators of activity spaces were typically used including the number of unique locations visited, distance travelled, area of activity spaces, frequency of activity participation, types of activity participated in, and duration of participation in order to identify transport disadvantage. A combined measure using six individual indices were developed based on the six dimensional indicators of activity spaces, by taking into account the relativity of the measures for weekdays, weekends, and for a week. Factor analyses were conducted to derive weights of these indices to form the PI measure. Multivariate analysis using general linear models of the different indicators/indices identified new patterns of transport disadvantage. The research found that: indicator based measures and index based measures are complement each other; interactions between different factors generated new patterns of transport disadvantage; and that these patterns vary in space and time. The analysis also indicates that the transport needs of different disadvantaged groups are varied.