MAKE MOST OF THE KNOWLEDGE NETWORK, JOIN ACADEMIC RESEARCH FOUNDATION

India has achieved substantial economic progress over the past few decades but retains significant growth potential for the next 30 years. The primary challenge lies in transforming this theoretical growth potential into
tangible economic advancements. This study investigates the determinants of India’s economic growth by analyzing data from 2000 to 2023, focusing on key variables such as GDP, consumption, fixed asset investment, exports, and employment. Utilizing multiple regression analysis through E-Views software, the empirical results indicate that fixed asset investment, consumption, exports, and employment significantly and positively influence economic growth. These findings provide critical insights for policymakers, underscoring the importance of i) Enhancing social security systems and promoting credit consumption to boost consumer expenditure; ii) Improving investment layouts by focusing on rural investment and supporting emerging and innovative industries; iii) Optimizing export structures by increasing technological investment, and iv) Improving the comprehensive quality of the workforce by aligning educational outcomes with market needs and providing professional training for the unemployed. These strategies aim to sustain and enhance India’s economic trajectory, ensuring long-term economic growth and development.

Keywords: Economic Growth, Multiple Regression Model, Influence Factors, Empirical Analysis.

There have been many public discussions about prize money distributions in professional snooker, but few of these discussions include a real quantitative analysis. Here, four decades of snooker rankings are analyzed
in terms of prize money distributions. In particular, it is investigated how much these rankings are dominated by the top players, and also by players from the UK. Among others, an analysis based on the Pareto principle (or 80- 20 rule) is performed. The main conclusions are that: (1) with an increasing amount of available prize money, the rankings are less dominated by the top players, and (2) players from the UK have historically earned a significantly larger share of the available prize money than could be reasonably expected based on the actual percentage of UK players. The presented analyses and results aim at providing an objective and quantitative basis for any further discussions on the prize money distribution issue.

Keywords: snooker; ranking; prize money; pareto principle

This study applied the Box-Tiao ARIMA-based intervention analysis in assessing government interventions in childhood upper respiratory tract infections (URTIs) for a period of 15 years. Results revealed seasonal variations in URTI incidence, with peaks during the Dry season, especially from January to March, and a reduction during the Wet season, from April to August. Exploratory data analysis shows a right-skewed distribution of URTI severity, with a mean of 88.48 and significant variability in the studied dataset. A pulse-type intervention was introduced in May 2006 as a measure to eradicate childhood URTI. The pre-intervention series was stationary at level with an ARIMA (1, 0, 0) model fitted. The impact parameter was insignificant, with a value of -22.410200 and a p-value of 0.5716. The May 2006 intervention had no significant effect on URTI eradication, as more youngsters were still infected with an increasing hospital admitted cases in the post-intervention period. These findings suggest that environmental factors significantly influence the occurrence of URTIs, and while interventions may not always have an immediate effect, ongoing public health strategies are needed to address the rising burden of childhood respiratory infections in the State.

Keywords: Childhood URTI, Statistical Assessment, Time Series, Box-Tiao Model.

Recently, numerical analysis has been used effectively for estimating the lifetime distribution parameters in the literature. Therefore, the main objective of this paper is to introduce a new numerical estimation technique,
such as Adams’s method. This method has been used for estimating the Weibull model parameters and comparing them to the Bayes estimations based on different priors via Monte Carlo simulations. The simulation results indicated that Adams’s method is more efficient than Bayes’ method. Finally, two real data sets have been analyzed for illustrations and to compare the proposed methods based on the generalized progressive hybrid censoring data.

Keywords: Bayesian estimation; Characteristic priors; Informative prior; Kernel prior.

This study investigated the performance of EGARCH model for three different innovations (student's t, normal, and skewed innovations) using Nigerian daily stock price series from 30/01/2012 to 03/10/2024, yielding a
total of 3139 observations. The aim was to determine the innovation distribution that best captures the asymmetry and kurtosis exhibited by the returns on financial data. The descriptive properties of the series revealed
that the distribution of returns for the stock prices was skewed and leptokurtic. The unit root test was carried out using the augmented Dickey-Fuller (ADF) test, and the result revealed that the returns on the series was stationarity. The ARCH LM-test detected the presence of ARCH effects, which justified the use of the GARCH model. The mean equation was estimated, and EGARCH-X (1,1) model was fitted to the data, using different innovations. The findings of the study revealed that EGARCH (1,1) model with skewed Student's t-distribution gave the overall best fit, with the lowest AIC (-8.445) and the highest log-likelihood (13252.37). Findings of the study further revealed that the forecast from EGARCH (1,1) model with the skewed student's tdistribution has strong numerical accuracy with low RMSE (0.004413) and MAE (0.002907), indicating that the model's predictions are close to the actual values. From the findings of this study, it was deduced that the selection of suitable innovations in financial volatility modelling is pertinent for an appropriate forecast of the financial market.

Keywords: Asymmetry, E-GARCH, Stock Price, Volatility, Innovation Distributions.

In any stock market, the stock prices are generally volatile over time. While stock prices either increase or decrease gradually in short periods, the fluctuations are wide and more persistent over long periods. This paper
analyses the effects of such short-period and long-period volatility on stock prices. Using error variance i.e. volatility in residuals, specifically volatility clustering, the future volatility in stock prices is forecasted. Using data on the stock prices of TATA Steel Limited, a listed company in the NSE, for the period January 1, 2021- December 31, 2023, the effects of short-period and long-period stock price fluctuations on daily stock prices and volatility are predicted for the next 69 days, from January 1 to April 13, 2024. The stock prices are predicted first by the ARIMA model, and then the future stock price volatility is predicted by applying the GARCH and EGARCH models on the resultant residuals. The EGARCH fitting shows that the long-period fluctuations have a significant effect on the future stock price volatility relative to the GARCH fitting. The comparison of EGARCH forecasts with the actual stock price fluctuations from January 1-April 13, 2024, shows that the long-period stock price volatility is more reliant than the short-period volatility in forecasting future stock price volatility as well as the stock prices.

Keywords: Stock price volatility, heteroscedasticity, error variance, volatility clustering, asymmetry, leverage, ARIMA, GARCH, EGARCH, forecasting.