Minimax regret treatment choice with finite samples

This paper applies the minimax regret criterion to choice between two treatments conditional on observation of a finite sample. The analysis is based on exact small sample regret and does not use asymptotic approximations or finite-sample bounds. Core results are: (i) Minimax regret treatment rules are well approximated by empirical success rules in many cases, but differ from them significantly–both in terms of how the rules look and in terms of maximal regret incurred–for small sample sizes and certain sample designs. (ii) Absent prior cross-covariate restrictions on treatment outcomes, they prescribe inference that is completely separate across covariates, leading to no-data rules as the support of a covariate grows. I conclude by offering an assessment of these results.     

Maximum likelihood methods for fitting the burr type XII distribution to multiply (progressively) censored life test data

Summary This paper develops mathematical and computational methodology for fitting, by the method of maximum likelihood (ML), the Burr Type XII distribution to multiply (or progressively) censored life test data. Mathematical expressions are given for approximating the asymptotic variances and covariances of the ML estimates (MLEs) of the parameters of the Burr Type XII distribution. A rigorous mathematical analysis is undertaken to investigate the existence and uniqueness of the MLEs for arbitrary sample data. The methodology of this paper is applied to progressively censored sample data arising in a life test experiment.     

The Importance of Experimental Control in Testing the Impact of Interviewer Continuity on Panel Survey Nonresponse

The conventional wisdom in survey research suggests that it is advisable to have the same interviewers return to the same respondents in order to maintain good response rates in longitudinal surveys. There has been, however, very little documented experimental research to support this. Work conducted by Campanelli and O'Muircheartaigh (1999) using a subsample of the British Household Panel Study (BHPS) at Wave 2 with experimental control of the allocation of respondents to interviewers showed no evidence of a positive continuity effect on nonresponse; more extensive analysis by Laurie et al. (1999) of the full BHPS sample using Waves 2 through 4 presents contradictory results. This paper extends the earlier analysis and shows that these differences in findings are due to the lack of experimental control for the inferences from the full BHPS sample in the Laurie et al. (1999) report rather than the shorter time frame considered in Campanelli and O'Muircheartaigh (1999). This paper also considers variation in interviewer continuity effects across areas through the use of multilevel statistical models.     

Investigating the Number of Non-linear and Multi-modal Relationships Between Observed Variables Measuring Growth-oriented Atmosphere

This study investigates the number of non-linear and multi-modal relationships between observed variables measuring the Growth-oriented Atmosphere. The sample (N = 726) represents employees of three vocational high schools in Finland. The first stage of analysis showed that only 22% of all dependencies between variables were purely linear. In the second stage two sub samples of the data were identified as linear and non-linear. Both bivariate correlations and confirmatory factor analysis (CFA) parameter estimates were found to be higher in the linear sub sample. Results showed that some of the highest bivariate correlations in both sub samples were explained via third variable in the non-linear Bayesian dependence modeling (BDM). Finally, the results of CFA and BDM led in different substantive interpretations in two out of four research questions concerning organizational growth.     

Development dynamics: Pre and Post Brexit analysis of United Kingdom

Quality & Quantity - The main prospective of this research is to analysis the industrial development (IDV) nexus for a sample of Pre Brexit Polling and After Brexit Polling in the economy of...     

A comparison of nonresponse in mail,telephone, and face-to-face surveys

This article reports a meta-analysis of 45 studies that explicitly compare the response obtained using a mail, telephone or face-to-face survey. The data analysis uses a generalized hierarchical linear model. Sampling procedure (e.g., local convenience sample, random general sample), saliency of topic, and research organization (university, government versus market research) had an effect on the response. On the average, the face-to-face condition achieved the highest completion rate (70.3%), the telephone survey the next highest (67.2%), and the mail survey the lowest (61.3%). There is a significant interaction with the year of publication: The response to face-to-face and telephone surveys is going down in the period covered by this analysis (1947 to 1992), but the response to mail surveys is going up slightly. We attribute this to the large amount of research on nonresponse problems with mail surveys, and recommend more research and development in this direction for face-to-face and telephone methods.     

Recovering the Metric Structure in Ordinal Data: Linear Versus Nonlinear Principal Components Analysis

Two techniques for data reduction as part of the SPSS package are compared in a Monte Carlo study: principal components analysis (PCA) and nonlinear principal components analysis (NPCA). The relative performance of these techniques in recovering the component scores underlying subjects' scores on observed ordinal variables is studied for two-dimensional spaces. The relative performance is examined as a function of (a) the sample size, (b) the number of categories in the variables, (c) the amount of measurement error, (d) the type of nonlinearity in the data, and (e) the degree of heterogeneity of the marginal distributions of the variables. As expected, when the sample size increases the performance of NPCA improves when compared to PCA. For the range of values considered, there is no effect of the number of categories on the relative performance of PCA and NPCA. For the other factors the effects are more complicated: adding error does not affect PCA as strongly as NPCA, as expected, but not for heterogeneously distributed variables for a particular form of nonlinearity, in which case NPCA becomes more appropriate. PCA appears to outperform NPCA for linear data, but also for a substantial number of nonlinear data sets.     

Major weapons procurement: An efficiency-based approach for the selection of fighter jets

This study applies the epsilon-based measure method in data envelopment analysis (DEA) to evaluate processes in procuring fighter jets (FJs), which are indicative of the technology and capability of major weapons. Moreover, multidimensional scaling analysis and sensitivity analysis were included to assess the selection of FJs. The DEA analysis indicates that the overall efficiency of the sample of 26 FJs was 0.824 on average. Besides, procurers rely on price and capability to decide which FJ models to purchase. Overall, the evaluation results can be provided to procurement decision makers even in selecting other types of major weapon in a cost-effective manner.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

Estimating and predicting multivariate volatility thresholds in global stock markets

We propose a general double tree structured AR‐GARCH model for the analysis of global equity index returns. The model extends previous approaches by incorporating (i) several multivariate thresholds in conditional means and volatilities of index returns and (ii) a richer specification for the impact of lagged foreign (US) index returns in each threshold. We evaluate the out‐of‐sample forecasting power of our model for eight major equity indices in comparison to some existing volatility models in the literature. We find strong evidence for more than one multivariate threshold (more than two regimes) in conditional means and variances of global equity index returns. Such multivariate thresholds are affected by foreign (US) lagged index returns and yield a higher out‐of‐sample predictive power for our tree structured model setting. Copyright © 2006 John Wiley & Sons, Ltd.     

Reassessing the dependence between economic growth and financial conditions since 1973

Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm their empirical findings, using their methodology and their pre-2016 sample. Mindful of the importance of the Covid-19 pandemic, we extend the sample to 2021Q3 and find attenuation of the key estimated coefficients using ABG's empirical methods. Given the pandemic observations, we provide robust QR analysis of dependence based on ranked data and explain the relationship with extant copula modelling methods.     

Measuring productivity of research in economics: A cross-country study using DEA

We measure productivity in leading edge economic research by using data envelopment analysis (DEA) for a sample of 21 countries belonging to the Organization for Economic Cooperation and Development (OECD). Publications in ten top journals of economics from 1980 to 1998 are taken as the research output. Inputs are measured by R&D expenditure, the number of universities with economics departments and (as an uncontrollable variable) population. Under constant returns-to-scale, the US emerges as the only efficient country. Under variable returns-to-scale, the efficiency frontier is defined by the US, Ireland and New Zealand. With the exception of the US, all countries in our sample display increasing returns-to-scale, and thus have the potential to raise their efficiency by scaling up their research activities.     

Validation and measurement invariance of the multidimensional qualitative job insecurity scale

This contribution introduces the Multidimensional Qualitative Job Insecurity Scale (MQJIS). Drawing from the qualitative job insecurity literature and addressing some of other scales’ limitations, a multidimensional model is proposed and investigated by means of confirmatory factor analysis and multilevel confirmatory factor analysis. Study 1 aims to explore the psychometric properties and factorial structure of MQJIS in an Italian sample of blue-collar workers (N?=?583), showing that a model with one higher-order factor (i.e., qualitative job insecurity) and four dimensions (i.e., social relationships, employment conditions, working conditions, and work content) shows a good fit to the data and good reliability indices. Study 2 aims to investigate MQJIS measurement invariance across several groups, based on country of origin, age, and gender. Results on a sample of Belgian and Italian workers (N?=?710) show that MQJIS met the criteria for uniqueness invariance across genders and scalar invariance across countries and age groups. Significance, implications, and future directions stemming from the initial validation and the confirmed measurement invariance of this scale are discussed.

     

Lag Order and Critical Values of the Augmented Dickey–Fuller Test: A Replication

This paper replicates Cheung and Lai (Journal of Business and Economic Studies 1995; 13(3): 277–280), who use response surface analysis to obtain approximate finite‐sample critical values adjusted for lag order and sample size for the augmented Dickey–Fuller test. We obtain results that are quite close to their results. We provide the Ox source code. We also provide a Windows application with a graphical user interface, which makes obtaining custom critical values quite simple. Copyright © 2015 John Wiley & Sons, Ltd.     

An identification‐robust test for time‐varying parameters in the dynamics of energy prices

We test for the presence of time‐varying parameters (TVP) in the long‐run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean‐reverting models. We also propose residual‐based diagnostic tests and examine out‐of‐sample forecasts. In‐sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out‐of‐sample analysis suggests a random‐walk specification for oil price, and TVP models for both real‐time forecasting in the case of gas and long‐run forecasting in the case of coal. Copyright © 2010 John Wiley & Sons, Ltd.     

Networks in Public Administration: Current scholarship in review

Abstract

Network-focused research in public administration has expanded rapidly over the past two decades. This rapid growth has created come confusion about terminology and approaches to research in the field. We organize the network literature in public administration using compact citation networks to identify coherent subdomains focused on (1) policy formation, (2) governance and (3) policy implementation. We trace how these domains differ in their approach to defining the role of networks, relationships and actors and to what extent the articles apply formal network analysis techniques. Based on a subsequent content analysis of the sample articles, we identify promising research avenues focused on the wider adoption of methods derived from social network analysis and the conditions under which networks actually deliver improved results.     

Work values as “reinforcer groupings”

This study was conducted to examine whether the same underlying structure of occupational reinforcers (denoted the structure of work values) turns out when three different data analysis methods are used. A questionnaire requesting a respondent to indicate the importance of each of 21 occupational reinforcers was administered to 546 subjects, drawn so as to constitute a representative sample of the urban adult Jewish Israeli population. Factor analysis, Guttman's smallest space analysis, and cluster analysis were each applied to the subjects' responses from the questionaires, essentially resulting in different work values structure. Comparisons were made and theoretical implications have been discussed.     

The effect of foreign ownership on SMEs performance: An efficiency analysis perspective

This paper uses both Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) models in order to determine different performance levels in a sample of 353 foreign equities operating in the Greek manufacturing sector. Particularly, convex and non-convex models are used alongside with bootstrap techniques in order to determine the effect of foreign ownership on SMEs’ performance. The study illustrates how the recent developments in efficiency analysis and statistical inference can be applied when evaluating performance issues. The analysis among the foreign equities indicates that the levels of foreign ownership have a positive effect on SMEs’ performance.     

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.     

Success and failure in turnaround attempts. An analysis of SMEs within the Finnish Restructuring of Enterprises Act

This study focuses on the success and failure of small- and medium-sized enterprises (SMEs) attempting turnaround within Finland's Restructuring of Enterprises Act. In doing so, it aims to shed light on (1) how successful and unsuccessful SME turnarounds differ and (2) the effectiveness of the Finnish regime in promoting SME recovery. A preliminary review of the turnaround literature revealed 23 decline and recovery variables. Data on these variables were collected via a questionnaire sent to the administrators of failing SMEs that entered restructuring. Data from the sample of 228 returns were subjected to factor and logit analysis. The factor analysis finds four decline categories: poor management, high debt in adverse macroeconomy, an adverse microeconomic environment and one-off causes of decline. It also finds three recovery action categories: management change and cash generation, market reorientation and cost-cutting and retrenchment. The logit analysis finds that one-off causes of decline, management change and cash generation and cost-cutting and retrenchment are more important in successful turnarounds and that poor management and an adverse microeconomic environment are more important in unsuccessful turnarounds. The study also finds that the Finnish Restructuring of Enterprises Act has resulted in good rates of business survival. Fifty-four per cent of SMEs in our sample turnaround and survive.