Search results

The purpose of this paper is to establish correlations among the input variables of production within themselves and input variables of consumption within themselves and to forecast the production and consumption of the rice.

The production and consumption of rice crop is governed by diverse variables. In the present study five key input variables for production of rice based on literature review and the authenticated data available from agricultural sources have been selected. These variables are area sown, agricultural workers (AW), area irrigated, growth rate and yield per hectare. On similar basis four key input variables responsible for consumption of rice are considered, namely, price of rice, population, poverty ratio and per capita net national product (NNP).

Correlation analysis showed that priority wise production of rice depends upon yield per hectare, percentage irrigation, AW and area sown. The growth rate is found to be having insignificant correlation with other variables of production and hence was omitted from subsequent study. Correlation analysis also showed that priority wise consumption depends upon whole sale price per ton, population and the per capita NNP. The poverty ratio is found to be having insignificant correlation with other variables of consumption and hence was omitted from subsequent study. The outcomes of the correlation analysis are utilized for designing rule base for fuzzy inference system (FIS) to forecast the production and consumption of the rice. Subsequently Bayesian technique is used to forecast production and consumption and its results are compared with the results of fuzzy inference analysis.

There are many techniques used for forecasting purpose but FIS and Bayesian technique outperform others. In the present study, the authors therefore focussed on these two techniques. Bayesian technique takes into account the expert opinion at the current conditions whereas FIS uses previously designed rule base. Besides discussing the appropriateness of these two techniques for forecasting production and consumption of rice, their forecasting outcomes will help in logistical and operational planning of the resources at national level, farmers’ level and traders’ level.

The purpose of this paper is to investigate the interdependence between economic policy uncertainty (EPU) and business cycles within and among six emerging market economies (EMEs) from January 1999 to December 2018.

This study adopts the wavelet multiple correlations and wavelet multiple cross-correlation (WMCC) based on the maximal overlap discrete transform estimator. This methodology simultaneously investigates how two or more time series variables move together continuously at both time and frequency domains.

The empirical results show that business cycles comove with EPU for both intra- and inter-country analysis, with the long term showing the greatest degree of interdependence. In intra-country comparisons, EPU has a positive correlation with consumer price index and a negative correlation with share price index. According to the WMCC results, EPU does not have any leading or lagging power within each EME, but rather import has both lead and lag power. The inter-country WMCC results are all significant, with Korea’s EPU leading/following all EMEs across all scales.

This study contributes to the ongoing debate about what causes business cycles to comove by investigating business cycle indicators (leader/follower) using a robust wavelet methodology. The authors propose new variables that can clearly reflect the outcome of economic policy actions and translate information about EPU shocks. The inclusion of the variables has altered the understanding of the relationship between EPU and business cycle fluctuations. Policymakers also gain new insights into the trends and patterns of EPU and business cycles, which will help them formulate and implement fiscal and monetary policies more effectively.

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.

Management and especially strategy research rely heavily on the use of ratios to measure a variety of firm, industry, and societal characteristics. Most often, these ratios are created simply to control for size effects (i.e., scaling) emanating from differences in the size of firms, industries, populations, or national economies on the variables of interest. In addition, ratios may also hold theoretical meaning apart from that of their components. Despite the popularity of ratios and regardless of their purpose, the use of ratios is not without controversy. In particular, several studies have demonstrated that the use of ratio measures in correlations and multiple regressions can exaggerate relations of interest leading to biased and unstable results. In this chapter, I review the debate surrounding the use of ratio measures, discuss the problems for estimation and inference that are likely to arise when ratios are used in statistical estimation, and provide alternatives to the use of ratio variables that still satisfy the purpose for which ratio measures are often created.

Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings.

Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables.

The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining.

This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.

For fast calculation of complex structure in engineering, correlations among input variables are often ignored in uncertainty propagation, even though the effect of ignoring these correlations on the output uncertainty is unclear. This paper aims to quantify the inputs uncertainty and estimate the correlations among them acorrding to the collected observed data instead of questionable assumptions. Moreover, the small size of the experimental data should also be considered, as it is such a common engineering problem.

In this paper, a novel method of combining p-box with copula function for both uncertainty quantification and correlation estimation is explored. Copula function is utilized to estimate correlations among uncertain inputs based upon the observed data. The p-box method is employed to quantify the input uncertainty as well as the epistemic uncertainty associated with the limited amount of the observed data. Nested Monte Carlo sampling technique is adopted herein to ensure that the propagation is always feasible. In addition, a Kriging model is built up to reduce the computational cost of uncertainty propagation.

To illustrate the application of this method, an engineering example of structural reliability assessment is performed. The results indicate that it may significantly affect output uncertainty whether to quantify the correlation among input variables. Furthermore, an additional advantage for risk management is obtained in this approach due to the separation of aleatory and epistemic uncertainties.

The proposed method takes advantage of p-box and copula function to deal with the correlations and limited amount of the observed data, which are two important issues of uncertainty quantification in engineering. Thus, it is practical and has the ability to predict accurate response uncertainty or system state.

The purpose of this paper is to study correlations between the national real estate investment trusts (REIT) markets in the USA and the four Asia‐Pacific countries of Australia, Hong Kong, Japan and Singapore, and document the extent to which the time variation present in these correlations can be explained from a set of 11 economic and financial factors. Both US dollar and local currency returns are used.

Time‐varying correlations are estimated using a DCC‐GARCH model that allows for asymmetries in both the correlations and volatilities. The correlations are then regressed on a set of four economic and seven financial factors, and tests of statistical significance are conducted in order to discriminate between relevant and irrelevant explanatory variables. The authors estimate a fixed‐effects panel regression as well as individual regressions for each dynamic correlation.

Significant time variation is found in the four REIT correlation series. Panel regressions suggest that REIT correlations rise with increases in the interaction of national inflation rates and with higher global equity market uncertainty. It is also found that REIT correlations fall with increases in the US default risk premium and global equity market volume. Relaxing the structure imposed by the panel data model, individual regressions confirm most of the results, although there are some exceptions. It is also found that there are no substantial differences in the dynamics of the correlation coefficients when switching from the US dollar to local currency denominated returns.

Investors in real estate securities across national markets should take into account information about the credit spread, the volatility and volume of global equity markets, and inflation rates when modeling correlations. These variables may alert the investors to the possibility that, under a set of circumstances, investing in real estate across different markets may not provide the expected diversification benefits. Another implication relates to the impact of currency hedging. It appears that the impact of switching from US dollar to local currency denominated returns does not substantially change the time dynamics of the correlations, or the importance of explanatory variables.

Although considerable progress has been made in modelling time‐varying correlations between various REIT markets, to the authors' knowledge, this is one of the first papers to investigate the underlying causes of the co‐movement, especially between the US and Asia‐Pacific markets. The paper's results will help investors and risk managers make better choices by identifying those factors that have more systematic effects on the change in the REIT correlations, rather than more transient forces.

To determine the relationship among covariates used in financial distress studies.

The study selects four specific bankruptcy studies and employs canonical correlation analysis to determine the relationship among the different variable sets that these studies used as predictors of financial distress. Canonical correlation analysis identifies the relationship and provides an indication of the amount of redundancy that exists between two variable sets. The four studies are representative of the genre, similar as to choice of statistical technique, and frequently cited by researchers.

The research findings indicate that the relationships between the alternative variable sets are very weak and alternative variable sets do not represent similar financial relationships. Redundancy coefficients suggest that, if one variable set is redundant to another variable set, it is because the redundant variable set, is much smaller than the predictor variable set.

The results suggest that there is not much similarity among the variable sets used in financial distress studies; to the extent that there is any similarity, it is due to variables common to each set or one variable set being larger than the other variable set. Ad hoc variable selection in financial distress studies results in the use of alternative variable sets containing heterogeneous variables unrelated to one another.

A common criticism of financial distress research is that a theory of corporate failure does not exist. Variable selection is not prompted by economic theory but is based upon suggestions in the literature, the success of variables in earlier studies, or the selection of a large set of variables with an accompanying data reduction procedure. Despite nearly 30 years of research in the area, the absence of an inter‐correlational structure among alternative variable sets highlights the atheoretical nature of financial distress research.

The purpose of this paper is to empirically establish an indigenously developed model for measuring organizational performance and organizational excellence, and to examine the relationship between organizational performance and organizational excellence.

The paper presents a model based on seven variables, at the overall and work unit level, for measuring organizational performance and organizational excellence – tested by using a large sample. A structured questionnaire is developed for collecting data from 407 respondents from 230 organizations. Summated scale average method is used for calculation of organizational performance and a total correlation method is used for the calculation of organizational excellence.

It is established that organizational performance and organizational excellence could be measured by consolidating performance variables, using two different methods: performance can be measured by averaging the performance variable scores, and excellence can be measured by averaging the correlations of performance variable scores. Based on the study, a new general definition for organizational excellence is proposed, as “the outstanding measure of relationship of all performance variables influencing an organization's functioning”.

The model, developed and tested for measuring performance and excellence, can be used by small and medium enterprises to evaluate their performance and excellence separately and periodically. The study helps managers to recognize organizational excellence as a measure needing special attention instead of taking it as an outstanding value of organizational performance.

The definition and model developed and tested for measuring excellence can contribute significantly to existing literature on excellence measurement. This will help researchers to study organizational excellence as a separate organizational behavior, instead of limiting it as a terminal value of organizational performance.