External Shocks and the Indian Economy: Analyzing through a Small, Structural Quarterly Macroeconometric Model

Though a large number of structural macroeconometric models have been estimated for India, the fact that all these are based on annual data limit their usefulness for short-term policy analysis, particularly in volatile periods of the type seen during last few quarters. Therefore the present paper builds up a short-term macroeconometric model for India using quarterly data. The model has reasonably good in-sample performance. One important feature of the model is use of quadratic relation between government expenditure and credit to private sector, which shows presence of both crowding in and crowding out effects, the latter dominating the former when expenditure is high enough. Some simulations are also carried out to analyse the impact of recent external shocks such as rise in global food and fuel prices and the global financial meltdown, on the Indian economy. The results show that the current slowdown in India’s growth predates the global price shock and the global financial crisis, and is more of a regular cyclical downturn. The global developments only further deepen the slowdown and prolong the recovery.Structural model, External Shocks, India

Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series

This paper models the univariate dynamics of seasonally unadjusted quarterly macroeconomic time series for the Indian economy including industrial production, money supply (broad and narrow measures) and consumer price index. The seasonal integration-cointegration and the periodic models are employed. The `best' model is selected on the basis of a battery of econometric tests including comparison of out-of-sample forecast performance. The results suggest that a periodically integrated process with one unit root best captures the movements in industrial production. The other variables do not exhibit periodically varying dynamics, though narrow money and consumer price index exhibit nonstationary seasonality. For the index of industrial production, the periodic model yields the best out-of-sample forecasts, while for broad money, the model in first differences performs best. On the other hand, for narrow money and the consumer price index, incorporating nonstationary seasonality does not lead to significant gains in forecast accuracy. Finally, we find significant conditional heteroskedasticity in industrial production, with error variance in the first two quarters (highest and lowest economic activity quarters, respectively) almost three times that in the other two quarters.Seasonality, Integration, Periodic Integration, Forecast Performance

Investigating End User Satisfaction in ERP Systems: An Analytical Approach

Measuring the end user satisfaction is an important issue reported in many decades in ERP systems success and its implementation. Hence, instead of many success stories the failure is also reported many times about the ERP implementation process. It provides many benefits to the organizations i.e. central storage/backup, modular software, efficiency, easier, and collaboration between various departments. Therefore, it is widely used by many types of organizations to provide a single platform to them. Despite this, its adoption and implementation in not without problems. Ignoring many important factors is also a reason of failure of ERP implementation. So, this paper is focusing on three main factors to investigate the success of ERP systems that are human, technological and organization. A survey tool is used for the study as questionnaire. An analytical approach is proposed to investigate the success of ERP systems by explaining that which factor is more important for end user satisfaction in ERP. Analysis is done on the basis of variance explained by each critical success factor. More variance shows that the factor impacts more the success of ERP system. As a result, three factors are very important for successful ERP implementation which are training and support for users, to facilitate changes in the organizational structure, in the legacy systems and in the IT infrastructure and having external consultants

High Prediction Accuracy and Low Error for ERP User Satisfaction by Hybrid of ANFIS and KNN Classification

The incoming era is becoming more friendly and dependent on Information Technology. Enterprise Resource Planning (ERP) Systems are one of the most widely used latest examples of Information Systems (IS) technology. They provide a single window system to the organizations by integrating the whole functions of them. Today, all enterprises are rapidly adopted ERP systems. But, their adoption and implementation is not being without any problem. The implementation process of ERP is also a very challenging, time consuming and costly task. Therefore, instead of many efforts if the implementation process is failed. Then it will be a big failure for the organization. Hence, to overcome this failure and increase the success rate of ERP projects we need to develop a robust, reliable and accurate predictor. This will help us to redirect the projects far better in advance. The success of ERP systems depends on many factors. US is one of the important factor among them. Hence, we develop an efficient predictor of US using hybrid of ANFIS and KNN. We were used this method first time in literature related to prediction of US in ERP. The Hybrid method increases the prediction accuracy more comparatively than previous reported techniques ANN, ANFIS and KNN. The RMSE using Hybrid method is 0.167629 and for KNN, ANFIS and ANN is 0.5, 0.486185, and 0.590329 respectively

User Satisfaction Prediction in ERP using KNN Classifier for high Prediction Accuracy

Abstract—ERP (Enterprise Resource Planning) systems are widely used in organizations; because, ERP provides a single platform to manage all the processes and functions of organizations. This single platform improves their productivity, business performance, decision making capabilities and efficiency. However, to achieve a proper level of ERP success depends on various factors e.g. organization, technology, environment and User Satisfaction etc. ‘User Satisfaction’ (US) is most important factor to make ERP successful. US refer the user’s comfort and acceptability of ERP system during the use and interaction with the ERP system. This paper deploys the conceptual model for US prediction by considering Human, Technological and Organizational factors as predictors. In this report, we proposed K-Nearest Neighbor (KNN) Classification method first time in literature to predict the US and we compare it with ANFIS and ANN. We calculated average error for all test cases and demonstrate that KNN gives high predication accuracy in most of the cases and low average error (0.25) in comparison ANFIS (0.3378) and ANN (0.6053) methods. So our approach is novel and using KNN, prediction accuracy can be further improved for US to make successful ERP

Neutron Spectra for Low Energy Quasi-Mono-energetic p+7^7Li Reaction

$\underline{\textbf{MO}}$nte-carlo $\underline{\textbf{N}}$ucleon transport $\underline{\textbf{C}}$ode (MONC) for nucleon transport is extended for below 20MeV proton transport using ENDF data. It is used to simulate p+$^7$Li reaction upto 20MeV proton energies and produced neutron spectra are reported here. The simulated results are compared with calculated values from other available codes like PINO, EPEN, SimLiT codes and experimental data. The spectra reported here can be used to get the neutron cross-section for this quasi-mono-energetic neutron reaction and will help to subtract the low energy contribution.Comment: arXiv admin note: text overlap with arXiv:2008.1150