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Statistical methods are important for meaningful analysis, critique and interpretation of results. The current study aims to investigate the use of statistical methods used in LIS research articles produced by Pakistani authors during 2001–2016.

Content analysis method with both the qualitative and quantitative components was used. LIS articles published by Pakistani authors in national and international journals from 2001 to 2016 were selected. The descriptive and inferential statistics were used to analyze the usage of statistical techniques.

The findings show that use of descriptive statistics remained higher as compared to inferential statistics in the LIS research produced by Pakistani authors. However, a visible growth trend in the use of inferential statistical techniques is found. Males are two times more likely to use inferential statistics as compared to female authors. Articles published in foreign journals and impact factor journals used more inferential statistics as compared to local and nonimpact factor journals. Parametric inferential statistics is more popular among Pakistani authors as compared to nonparametric. Faculty was more inclined toward using parametric statistic. The percentage of collaboration was higher in the papers using parametric statistics. Few articles reported the tests to fulfill the assumptions of parametric and nonparametric statistics.

This study can be used to better understand the trends of statistical techniques used in LIS research and authors' orientation in this regard. It will be helpful for future researchers in the selection of appropriate statistical techniques to be used.

The purpose of this paper is to discuss a comprehensive literature survey of studies focusing on the efficiency and productivity of the banking sector using parametric and non‐parametric frontier techniques.

Critically reviewing 106 studies published across the world from 1994 to 2011, a conceptual framework is developed for the studies assessing the efficiency and productivity of the banking industry using non‐parametric DEA frontier approach.

Both the frontier approaches, parametric and non‐parametric, are gaining an edge over the traditional financial performance measures. In the non‐parametric approach, data envelopment analysis (DEA) is widely applied to measure a bank's efficiency and productivity. Studies conducted in developed countries such as the USA, the UK and Europe are now emerging with the new concepts of banking efficiency.

These findings are based only on the critical review of 106 studies. This study suggests the direction for future research and identifies the gap in existing literature with the development of a conceptual model.

This study is original in nature and included literature published in recent issues of 2011.

The purpose of this paper is to estimate and compare the efficiency of several water utilities using three frontier techniques. Moreover, this study estimates the impact of several qualities of service variables on water utilities’ performance.

The paper utilizes three frontier techniques such as data envelopment analysis (DEA), stochastic frontier analysis (SFA) and stochastic non-parametric envelopment of data (StoNED) to estimate efficiency scores.

Efficiency scores for each methodological approach were different being on average, 0.745, 0.857 and 0.933 for SFA, DEA and StoNED methods, respectively. Moreover, it was evidenced that water leakage had a statistically significant impact on water utilities’ costs.

The choice of an adequate and robust method for benchmarking the efficiency of water utilities is very relevant for water regulators because it affects decision making process such as water tariffs and design incentives to improve the performance and quality of service of water utilities.

This paper evaluates and compares the performance of a sample of water utilities using three different frontier methods. It has been revealed that the choice of the efficiency assessment method matters. Unlike SFA and DEA, a lower variability was shown in the efficiency scores obtained from the StoNED method.

Preparation of preliminary estimate is difficult owing to the lack of full project details in the early phases of the construction project. This paper seeks to assess the estimation techniques used for road projects and the critical factors affecting their accuracy in the Ghanaian construction industry.

Quantitative research design was adopted and questionnaire was designed to retrieve data. The target population were engineers and quantity surveyors who were contacted using an e-questionnaire through their professional bodies owing to location constraints. Retrieved data were analysed using descriptive and exploratory factor analysis. In order to compare the opinions of the respondents, the Mann–Whitney U-test was employed.

The survey revealed that subjective, parametric, comparative and analytical estimations are in use in Ghana. The most critical factors influencing the accuracy of estimation techniques are improper project planning, insufficient preliminary site investigation and usage of shortcuts, among others.

This study was limited to Accra, Ghana, due to time and distance constraint.

For accuracy of preliminary estimates to be improved, estimators being the custodian of the estimate are expected to be devoid of errors such as arithmetic calculation errors, inaccurate quantity measurement and error of omission. The usage of estimating software can eliminate these human errors.

The study will assist policymakers and stakeholders in aligning mitigative actions for factors influencing preliminary estimate of road projects with defined clusters rather than basic ranks. With attention focussed on the characteristics of each cluster, accuracy of preliminary estimate can be improved.

This systematic literature review integrates the findings of existing studies regarding technical efficiency (TE) in dairy farms. The purpose of this paper is to offer a research framework that assembles TE descriptors, a classification of previous literature that provides the basis for the synthesis and research agenda.

This paper systematically reviews 86 survey research studies using rigorous and reproducible procedures. The review is applied to published survey research.

The framework relates context, inputs, outputs and metrics of TE. There is no agreement among the authors on the context and determinants of TE. The main determinants of TE are geographical location, farm size, investments in veterinary care, feeding and milking practice, TE model estimation techniques, public policy, and management-related variables. This paper offers ten propositions for future research on the controversial results on the determinants of TE. The authors also explore the reasons for the discrepant results based on the Debreu-Farrell’s definition of TE, the contingency theory and the resource-based view of the firm, elucidating the literature and serving as a basis for future investigation. Implications for dairy farmers and researchers close the review.

Meta-analysis and meta-regression studies were long at the forefront of reviews in the TE of dairy farms. This paper offers a novel qualitative research synthesis with frameworks and the classification of previous literature and a research agenda, which provides a new and different perspective for analysis, by innovating over the available quantitative procedures to combine statistical results.

The purpose of this paper is to focus on the performance of three alternative value‐at‐risk (VaR) models to provide suitable estimates for measuring and forecasting market risk. The data sample consists of five international developed and emerging stock market indices over the time period from 2004 to 2008. The main research question is related to the performance of widely‐accepted and simplified approaches to estimate VaR before and after the financial crisis.

VaR is estimated using daily data from the UK (FTSE 100), Germany (DAX30), the USA (S&P500), Turkey (ISE National 100) and Greece (GRAGENL). Methods adopted to calculate VaR are: EWMA of Riskmetrics; classic GARCH(1,1) model of conditional variance assuming a conditional normally distributed returns; and asymmetric GARCH with skewed Student‐t distributed standardized innovations.

The paper provides evidence that the tools of quantitative finance may achieve their objective. The results indicate that the widely accepted and simplified ARCH framework seems to provide satisfactory forecasts of VaR, not only for the pre‐2008 period of the financial crisis but also for the period of high volatility of stock market returns. Thus, the blame for financial crisis should not be cast upon quantitative techniques, used to measure and forecast market risk, alone.

Knowledge of modern risk management techniques is required to resolve the next financial crisis. The next crisis can be avoided only when financial risk managers acquire the necessary quantitative skills to measure uncertainty and understand risk.

The main contribution of this paper is that it provides evidence that widely accepted/used methods give reliable VaR estimates and forecasts for periods of financial turbulence (financial crises).

This study examines the survival probability of the firms during the COVID-19 pandemic and identifies the effects of pandemic-era business strategies on firm survival across sectors and sizes.

This study combines World Bank Enterprise Survey data with three consecutive follow-up COVID-19 survey data. The COVID-19 surveys are the follow-up surveys of WBES, and they are done at different points of time during the pandemic. Both WBES and COVID-19 surveys follow the same sampling methods, and the data are merged based on the unique id number of the firms. The data covers 12,551 firms from 21 countries in different regions such as Africa, Latin America, Central Asia and the Middle East. The study applies Kaplan–Meier estimate to analyze the survival probability of the firms across sectors and sizes. The study then uses Cox non-parametric regression model to identify the effect of business strategies on the survival of the firms during the pandemic. The robustness of the Cox model is checked using the multilevel parametric regression model.

The study's findings suggest that a firm's survival probability decreases during the pandemic era. Manufacturing firms have a higher survival probability than service firms, whereas SMEs have a higher survival probability than large firms. During the pandemic period, business strategies significantly boost the probability of firm survival, and their impacts differ among firm sectors and sizes. Several firm-specific factors affect firm survival in different magnitudes and signs. Except in a few cases, the findings also indicate that one strategy positively moderates the influence of another strategy on firm survival during a pandemic.

COVID-19 pandemic has drastically affected the business across the globe. Firms adopted new business processes and strategies to face the challenges created by the pandemic. The critical research question is whether these pandemic-era business strategies ensure firms' survival. This study attempts to identify the effects of these business strategies on firms' survival, focusing on a comprehensive firm-level data set that includes firms from different sectors and sizes of countries from various regions.

Since there is no agreement on the consistency of their estimates, the purpose of this paper is to investigate whether parametric stochastic frontier analysis (SFA) and nonparametric data envelopment analysis (DEA) generate consistent bank efficiency assessments.

The authors utilize four alternative efficiency computation models: two DEA technical efficiency models based on constant and variable returns to scale, and two SFA cost efficiency models employing Translog and Fourier functional specifications. An unbalanced panel of 59 Indian banks over 1990‐2007 is employed as a model, developing country, banking market.

The Translog and Fourier specifications in SFA and the constant and variable returns to scale assumptions in DEA are found to rank and identify “best‐practice” and “worst‐practice” approximately in the same order. The association between DEA efficiency estimates and non‐frontier standard performance measures, however, is mixed and inconclusive. Unlike DEA scores, SFA efficiency assessments were found to be consistent with cost and profit ratios and hence are “believable”.

For regulators and bankers alike, the authors' findings highlight the importance of investigating the consistency of efficiency scores across various research methods. They should ensure that frontier‐based efficiency assessments are not simply “artificial constructs” of models' assumptions/specifications.

This paper extends the existing literature by checking jointly the statistical consistency of both DEA technical efficiency scores and SFA cost efficiency scores. The prior studies focus either on technical efficiency or cost efficiency, but not both. Moreover, as far as the authors are aware, this is the first cross‐methodological validation study to focus on bank efficiency in the context of a developing country banking market.

This paper aims to investigate the long-run impact of selected foreign capital inflows, including aid, remittances, foreign direct investment (FDI), trade and debt, on the economic growth of 21 low-income countries in the Sub Saharan Africa (SSA) region, during the period 1990–2018.

To obtain this objective and for robust analysis, a parametric approach, which was dynamic ordinary least squares, and a non-parametric technique, which was fully modified ordinary least squares, were used.

The results of both models confirmed that, in the long run, trade and aid affected the growth rate of the per capita income in these countries in a positive way. However, external debt seemed to have an adverse influence on such growth.

First, this is the initial study that has addressed this matter across a homogenous group of countries in the SSA region. Second, while most of the previous studies regarding capital inflows into the SSA region have focused on the impact of only one or two aspects of such foreign capital inflows on growth, the present study, instead, examined the impact of five types of foreign capital inflows (aid, remittances, FDI, trade and debt).