An optimum sampling design to base HT-method of estimating a finite population total

The problem of constructing a sampling design with the value of the sum of second order inclusion probabilities attaining its lower bound for non-integral values of the expected effective size of a sample in the design has been considered in this paper. If the values of the characteristic of interest on all the units in the population are non-negative the design is admissible (in the sense of variance) with respect to Horvitz-Thompson estimator in the class of designs with the same set of values of the first order inclusion probabilities of the units. Again such a design is best to use Horvitz-Thompson estimator of population total in the sense of smallest average variance of the estimator under a special superpopulatio model.     

Mixed random systematic sampling designs

In this paper, an alternative sampling procedure that is a mixture of simple random sampling and systematic sampling is proposed. It results in uniform inclusion probabilities for all individual units and positive inclusion probabilities for all pairs of units. As a result, the proposed sampling procedure enables us to estimate the population mean unbiasedly using the ordinary sample mean, and to provide an unbiased estimator of its sampling variance. It is also found that the suggested sampling procedure performs well especially when the size of simple random sample is small. Received August 2001     

Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable

In this paper we will consider hypothesis-tests for the (fuzzy-valued) mean value of a fuzzy random variable in a population. For this purpose, we will make use of a generalized metric for fuzzy numbers, and we will develop an approach for normal fuzzy random variables, and two different approaches for the case of fuzzy random variables taking on a finite number of different values. A real-life example illustrates the use of the last two approaches. Finally, a comparison between the introduced techniques is developed by means of simulation studies leading to close inferential conclusions.Acknowledgements.The research in this paper has been partially supported by MCYT Grants BFM2002-01057 and BFM2001-3494. Their financial support is gratefully acknowledged. The authors are sincerely grateful to their colleague Gil González-Rodríguez for all his comments and suggestions in connection with this paper; his scientific support has been very valuable. The authors want also thank the referees of the first version of the paper because of their useful hints to improve it.     

Optimal Strategies in 2-Stage Sampling

In many survey situations simple random sampling of units and estimation of a total of interest by the expansion estimator are attractive methods, at least at first sight. Considering cost aspects suggests rather to use multiple stage sampling which, in general, is cheaper, but less effective. The design effect is an adequate criterion of the decrease of efficiency. We discuss this criterion for clusters (primary units) of equal size and derive exact conditions for a decrease of efficiency. The equality condition for cluster sizes seems not to be very restrictive, because in many cases one will be interested in clusters of approximately the same size, or, if sizes differ essentially, the clusters are partitioned into strata according to their sizes and the procedures for different strata are independent, each dealing with clusters of equal size or nearly so. In the context considered the use of the Horvitz–Thompson estimator is quite general. We examine a class of estimators with the Horvitz–Thompson estimator and a straight forward modification of it as special elements. As for the design effect all elements of the class are very similar, as for other aspects such as admissibility there are remarkable differences.     

On the effect of prior assumptions in Bayesian model averaging with applications to growth regression

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross‐country growth regressions using three datasets with 41–67 potential drivers of growth and 72–93 observations. Finally, we recommend priors for use in this and related contexts. Copyright © 2009 John Wiley & Sons, Ltd.     

Probability and quantile estimation from individually micro-aggregated data

Micro-aggregation is a frequently used strategy to anonymize data before they are released to the scientific public. A sample of a continuous random variable is individually micro-aggregated by first sorting and grouping the data into groups of equal size and then replacing the values of the variable in each group by their group mean. In a similar way, data with more than one variable can be anonymized by individual micro-aggregation. Data thus distorted may still be used for statistical analysis. We show that if probabilities and quantiles are estimated in the usual way by computing relative frequencies and sample quantiles, respectively, these estimates are consistent and asymptotically normal under mild conditions.     

A Partially Heterogeneous Framework for Analyzing Panel Data

This article proposes a partially heterogeneous framework for the analysis of panel data with fixed T. In particular, the population of cross‐sectional units is grouped into clusters, such that slope parameter homogeneity is maintained only within clusters. Our method assumes no a priori information about the number of clusters and cluster membership and relies on the data instead. The unknown number of clusters and the corresponding partition are determined based on the concept of ‘partitional clustering’, using an information‐based criterion. It is shown that this is strongly consistent, that is, it selects the true number of clusters with probability one as N→∞. Simulation experiments show that the proposed criterion performs well even with moderate N and the resulting parameter estimates are close to the true values. We apply the method in a panel data set of commercial banks in the US and we find five clusters, with significant differences in the slope parameters across clusters.     

Estimating the Size of the Homeless Population in Budapest,Hungary

In this study we try to estimate the size of the homeless population in Budapest by using two – non-standard – sampling methods: snowball sampling and capture-recapture method. Using two methods and three different data sets we are able to compare the methods as well as the results, and we also suggest some further applications. Apart from the practical purpose of our study there is a methodological one as well: to use two relatively unknown methods for the estimations of this very peculiar kind of population.     

Estimating regression coefficients from survey data by asymptotic design-cum-model based approach

Postulating a linear regression of a variable of interest on an auxiliary variable with values of the latter known for all units of a survey population, we consider appropriate ways of choosing a sample and estimating the regression parameters. Recalling Thomsen’s (1978) results on non-existence of ‘design-cum-model’ based minimum variance unbiased estimators of regression coefficients we apply Brewer’s (1979) ‘asymptotic’ analysis to derive ‘asymptotic-design-cummodel’ based optimal estimators assuming large population and sample sizes. A variance estimation procedure is also proposed.     

A note on the relationship between the total factor productivity and the network of firms

In this paper we study the robustness of the results found recently by Guzzini and Palestrini (J Econ Interact Coord 11:35–55, 2016). Since the original analysis was carried out in a static setting, we perform a dynamic panel analysis by using the same dataset. The inclusion of the lagged value of the endogenous variable, missing in the original paper, could be justified for several reasons. Firstly, the statistical relationship may have itself a dynamical nature; secondly the inclusion of lagged-endogenous variable is a way to mitigate the possibility of an omitted variable problem. We find that the results are only qualitatively the same, and we discuss the quantitative differences.

     

Testing outliers from an exponential population

Summary Likes obtains the distributions ofDixon’s statistics for an exponential population and tabulates upper 100α% points (α=0.1, 0.05, 0.01) of some of these distributions. The distribution of these statistics can be expressed in terms of finite series of beta functions and hence the probabilities of the rejection of suspected observed outliers may be easily calculated on a desk calculator. Thus we may avoid the difficult task of tabulating 100α% values of these statistics.     

THE ERRONEOUS USE OF CHINA'S POPULATION AND PER CAPITA DATA: A STRUCTURED REVIEW AND CRITICAL TEST

Hundreds of studies in economics misinterpret China's subnational population and per capita data. The most widely used population counts are of hukou registrations from each province, prefecture, county, or city rather than of the people living in each place and generating local gross domestic product. Over 220 million people have left their place of registration, while almost none had when reforms began, creating time-varying errors in estimates of per capita income of subnational units. We survey empirical articles in blue ribbon journals, in development journals, and in regional and urban economics journals that use China's subnational data. Over 80% of articles use these data erroneously; most commonly the wrong population or employment counts are used to measure the size of subnational units, and per capita data are calculated with the wrong denominator for how variables are interpreted. We provide examples of errors from each group of journals, and a critical test of one highly cited study. Specifically, we show that if hukou registrations are erroneously used to measure the local population, following existing practice, conclusions about driving forces for urban area expansion are reversed. We give recommendations for more careful use of China's subnational population and per capita data.     

Polygonal designs with blocks of size k≤10

The use of polygonal designs is motivated by the desire to avoid the selection of contiguous units in a sample from an ordered finite population. However very little is known about polygonal designs that have block size 5 or more. We present new polygonal designs with blocks of sizes 5 through 10, including the first designs with block sizes 9 and 10. For block sizes 5 through 7, we identify, with one possible exception, all values for the number of varieties for which a polygonal design exists. Received January 2001     

Survey Estimates by Calibration on Complex Auxiliary Information

In the last decade, calibration estimation has developed into an important field of research in survey sampling. Calibration is now an important methodological instrument in the production of statistics. Several national statistical agencies have developed software designed to compute calibrated weights based on auxiliary information available in population registers and other sources. This paper reviews some recent progress and offers some new perspectives. Calibration estimation can be used to advantage in a range of different survey conditions. This paper examines several situations, including estimation for domains in one‐phase sampling, estimation for two‐phase sampling, and estimation for two‐stage sampling with integrated weighting. Typical of those situations is complex auxiliary information, a term that we use for information made up of several components. An example occurs when a two‐stage sample survey has information both for units and for clusters of units, or when estimation for domains relies on information from different parts of the population. Complex auxiliary information opens up more than one way of computing the final calibrated weights to be used in estimation. They may be computed in a single step or in two or more successive steps. Depending on the approach, the resulting estimates do differ to some degree. All significant parts of the total information should be reflected in the final weights. The effectiveness of the complex information is mirrored by the variance of the resulting calibration estimator. Its exact variance is not presentable in simple form. Close approximation is possible via the corresponding linearized statistic. We define and use automated linearization as a shortcut in finding the linearized statistic. Its variance is easy to state, to interpret and to estimate. The variance components are expressed in terms of residuals, similar to those of standard regression theory. Visual inspection of the residuals reveals how the different components of the complex auxiliary information interact and work together toward reducing the variance.     

Some order relations between selection and inclusion probabilities for PPSWOR sampling scheme

In this paper we study some order relations between the selection and the inclusion probabilities for PPSWOR Sampling Scheme. We also establish some interesting bounds on the inclusion probabilities in terms of the selection probabilities.     

Uncertainty Estimation for Pseudo‐Bayesian Inference Under Complex Sampling

Social and economic studies are often implemented as complex survey designs. For example, multistage, unequal probability sampling designs utilised by federal statistical agencies are typically constructed to maximise the efficiency of the target domain level estimator (e.g. indexed by geographic area) within cost constraints for survey administration. Such designs may induce dependence between the sampled units; for example, with employment of a sampling step that selects geographically indexed clusters of units. A sampling‐weighted pseudo‐posterior distribution may be used to estimate the population model on the observed sample. The dependence induced between coclustered units inflates the scale of the resulting pseudo‐posterior covariance matrix that has been shown to induce under coverage of the credibility sets. By bridging results across Bayesian model misspecification and survey sampling, we demonstrate that the scale and shape of the asymptotic distributions are different between each of the pseudo‐maximum likelihood estimate (MLE), the pseudo‐posterior and the MLE under simple random sampling. Through insights from survey‐sampling variance estimation and recent advances in computational methods, we devise a correction applied as a simple and fast postprocessing step to Markov chain Monte Carlo draws of the pseudo‐posterior distribution. This adjustment projects the pseudo‐posterior covariance matrix such that the nominal coverage is approximately achieved. We make an application to the National Survey on Drug Use and Health as a motivating example and we demonstrate the efficacy of our scale and shape projection procedure on synthetic data on several common archetypes of survey designs.     

Rural entrepreneurship in place: an integrated framework

ABSTRACT

Agglomeration-oriented theories have grown significantly in the past decade in the explanation and promotion of entrepreneurship. Theoretical frameworks and normative models such as entrepreneurial ecosystems are insufficient to observe, explain, and inform policies at the communal level in rural contexts. In this paper, we propose a socio-spatial lens as a more fruitful way of understanding the holistic picture of rural entrepreneurship. By means of abductive research, we explore the distinct elements of entrepreneurial places in rural contexts and derive an integrated meso-level framework, comprising place-sensitive determinations and dimensions, to observe and further analyse the enabling conditions of such places. The findings obtained and the framework developed will be of great use for the evaluation and decision-making, regarding entrepreneurship in rural communities.     

Effect of Certificate of Need Law on the intensity of competition: The market for emergency care

PurposeThis article aims to contribute to the academic literature in better understanding the impact of Certificate of Need (CON) Law on Emergency Department (ED) care. Impact of CON Law on ED competition remains an unanswered empirical question.MethodsWe examine the impact of CON Law and its stringency on the intensity of competition (rivalry among competitors) between EDs measured by the Herfindahl-Hirschman Index (HHI). We then estimate the effects of CON Law on HHI by treating CON as an exogenous (endogenous) variable.FindingsOn average the CON legislation enhances ED competition. A possible reason is that the law hinders predatory behavior, and therefore acts as an effective anti-trust tool. Other findings indicate that competition is found to be positively related to a state's population size and median income and negatively related with the prevalence of employer provided insurance and magnitude of illegal immigration in a state.Practical implicationsThis article sheds some light on how political regulations could affect healthcare market and hence may provide public policy makers some insights on reducing healthcare cost.OriginalityOur analysis of the impact of CON regulation on ED competition significantly contributes to the healthcare and strategy literature. The law potentially serves as an anti-trust tool in the hands of the government. We extend the empirical literature by treating CON Law and its stringency as exogenous (endogenous). Our comprehensive analysis considers a host of control variables such as population demographics, their health status and access to health care, healthcare facilities, political environment, in addition to the CON features.     

The optimal consumption function in a Brownian model of accumulation Part A: The consumption function as solution of a boundary value problem

We consider a neo-classical model of optimal economic growth with c.r.r.a. utility in which the traditional deterministic trends representing population growth, technological progress, depreciation and impatience are replaced by Brownian motions with drift. When transformed to ‘intensive’ units, this is equivalent to a stochastic model of optimal saving with diminishing returns to capital. For the intensive model, we give sufficient conditions for optimality of a consumption plan (open-loop control) comprising a finite welfare condition, a martingale condition for shadow prices and a transversality condition as t→∞. We then replace these by conditions for optimality of a plan generated by a consumption function (closed-loop control), i.e. a function expressing log-consumption as a time-invariant, deterministic function of log-capital . Making use of the exponential martingale formula we replace the martingale condition by a non-linear, non-autonomous second-order o.d.e. which an optimal consumption function must satisfy; this has the form , where . Economic considerations suggest certain limiting values which and should satisfy as , thus defining a two-point boundary value problem (b.v.p.) — or rather, a family of problems, depending on the values of parameters. We prove two theorems showing that a consumption function which solves the appropriate b.v.p. generates an optimal plan. Proofs that a unique solution of each b.v.p. exists are given in a separate paper (Part B).     

Innovation performance and the role of clustering at the local enterprise level: a fuzzy-set qualitative comparative analysis approach

ABSTRACT

This study, utilizes an innovative methodological approach, fuzzy-set Qualitative Comparative Analysis (fsQCA), investigating the drivers of heterogeneous geographies characterizing English Local Economic Partnerships (LEPs). The fsQCA technique offers a novel configurational alternative to regression-based approaches investigating the effects of clustering in conjunction with firm-level innovation, university third-sector activity (TSA) and entrepreneurship, on LEPs innovation performance. The findings, offer contributions to the theories of industrial clusters and innovation, regional innovation systems, knowledge spillovers and entrepreneurial university innovation within LEPs. First, supporting fsQCAs, no individual variable generates either a positive/negative innovation outcome. Second, while all positive innovation recipes include presence of the cluster variable, for negative innovation recipes, only one does not identify absence of clustering as relevant. Given that the cluster variable does not appear in any recipes without at least one of the other variables suggests activity concentration does not exist in isolation to generate innovation outcomes without other localized conditions existing, e.g. firm-level innovation. Third, there is evidence for the non-cluster-based aspects of knowledge spillover theory of entrepreneurship with respect to university activity and the entrepreneurial university concept. Instead, roles of entrepreneurship and university TSA, while important, appear to be more peripheral and geographically context specific.