An Empirical Study of the Relationship Between Consumer and Producer Price Index: A Unit Root Test and Test of Cointegration

Policy makers have been long concerned about finding early indicators of inflation, a continuous rise in aggregate price level measured by the consumer price index (CPI). One of these indicators, which has been a target of many studies and has been supported by the production chain view, is the producer price index (PPI). The production chain view suggests that higher PPI will be passed to consumers through higher prices of finished goods. The purpose of this paper is to investigate the relationship between these two indexes using a unit root test and test of cointegration which are becoming more popular in time series analyses

Key performance indicators and analysts\u27 earnings forecast accuracy: An application of content analysis

We examine the association between the extent of change in key performance indicator (KPI) disclosures and the accuracy of forecasts made by analysts. KPIs are regarded as improving both the transparency and relevancy of public financial information. The results of using linear regression models show that contrary to our prediction and the hypothesis of this paper, there is no significant association between the change in non-financial KPI disclosures and the accuracy of analysts\u27 forecasts. Nonetheless, when we employ a non-linear regression and deflate the absolute value of forecast errors (the dependent variable in this study) by the stock price, the results support the hypothesis of an association between a change in non-financial KPI reporting and the accuracy of analyst forecasts. These results have policy implications, as worldwide policymakers, regulators, corporations and analysts underscore the importance of KPI disclosures. © Asian Academy of Management and Penerbit Universiti Sains Malaysia, 2011

Correlation, association, causation, and Granger causation in accounting research

In this paper we discuss the differences between correlation, association, and Granger causation. We argue that these important topics are not used properly in accounting and auditing. In statistics two correlation coefficients are calculated. The first one is the Pearson correlation coefficient and the other one is the Spearman correlation coefficient. In correlation analysis, the focus is only on the changes in two variables and no effort is made to control the effects of other variables. On the contrary, in association analyses the researcher examines the relationship between two variables while holding the effects of other related variables unchanged (ceteris paribus). In study of the causation or the cause-effect relationship between two variables, researchers are concerned about the effect of X on Y. The difficulty of achieving the third condition of causation is probably the main reason that in accounting literature the causation or cause-effect relationships are rarely used. The difficulty of achieving a causal relationship between two variables moved researchers toward a special form of causation called Granger Causation . We have provided practical examples for correlation, association, causation, and the Granger causation and discuss their main differences. By providing empirical examples, we also show how the use of a linear regression is not appropriate when the true relationship is not linear. Finally, we have discussed the policy, practical, and educational Implications of our study

Application of time series analyses in big data: Practical, research, and education implications

The application of Big Data and time series models is currently at an early stage. This paper examines the relevance and use of time series analyses for Big Data and business analytics by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how time series models can be efficiently and effectively applied in accounting and auditing using Big Data. Using sophisticated Big Data and time series models, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The time series model and Big Data analysis presented in this paper provide policy, practical, educational, and research implications. Businesses and management can use our suggested time series model and Big Data analysis in their predictive models of managerial strategies, decisions, and actions. Business schools and accounting programs can integrate the time series model, Big Data, and data analytics into business and accounting education

Application of time series analyses in big data: Practical, research, and education implications

The application of Big Data and time series models is currently at an early stage. This paper examines the relevance and use of time series analyses for Big Data and business analytics by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how time series models can be efficiently and effectively applied in accounting and auditing using Big Data. Using sophisticated Big Data and time series models, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The time series model and Big Data analysis presented in this paper provide policy, practical, educational, and research implications. Businesses and management can use our suggested time series model and Big Data analysis in their predictive models of managerial strategies, decisions, and actions. Business schools and accounting programs can integrate the time series model, Big Data, and data analytics into business and accounting education

Application of time series models in business research: Correlation, association, causation

Time series models are used to determine relationships, spot patterns, and detect abnormalities and irregularities among data. We explore the application of time series analyses in business research by discussing the differences among correlation, association, and Granger causality and providing insight into their proper use in the sustainability literature. In statistics, two correlation coefficients are typically calculated. The first one is the Pearson correlation coefficient and the second is the Spearman correlation coefficient. In the commonly used correlation analysis (the Pearson and the Spearman correlation coefficients), the focus is primarily on the changes in two variables regardless of the effects of other variables. On the contrary, in association analyses, the researcher examines the relationship between two variables while holding the effects of other related variables constant (ceteris paribus). In the study of the causation, or the cause-effect relationship between two variables, researchers are concerned about the effect of variable X on variable Y. The difficulty of achieving the third condition of causation is believed to be the main reason that in business literature causations are rarely used. The difficulty of achieving a causal relationship between two variables has moved researchers toward a special form of causation called Granger causality . We offer practical examples for correlation, association, causation, and the Granger causality and discuss their main differences and show how the use of a linear regression is inappropriate when the true relationship is non-linear. Finally, we discuss the policy, practical, and educational implications of our study

Application of time series models in business research: Correlation, association, causation

Time series models are used to determine relationships, spot patterns, and detect abnormalities and irregularities among data. We explore the application of time series analyses in business research by discussing the differences among correlation, association, and Granger causality and providing insight into their proper use in the sustainability literature. In statistics, two correlation coefficients are typically calculated. The first one is the Pearson correlation coefficient and the second is the Spearman correlation coefficient. In the commonly used correlation analysis (the Pearson and the Spearman correlation coefficients), the focus is primarily on the changes in two variables regardless of the effects of other variables. On the contrary, in association analyses, the researcher examines the relationship between two variables while holding the effects of other related variables constant (ceteris paribus). In the study of the causation, or the cause-effect relationship between two variables, researchers are concerned about the effect of variable X on variable Y. The difficulty of achieving the third condition of causation is believed to be the main reason that in business literature causations are rarely used. The difficulty of achieving a causal relationship between two variables has moved researchers toward a special form of causation called Granger causality . We offer practical examples for correlation, association, causation, and the Granger causality and discuss their main differences and show how the use of a linear regression is inappropriate when the true relationship is non-linear. Finally, we discuss the policy, practical, and educational implications of our study