Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. METHODOLOGY: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. RESULTS: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. CONCLUSION: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

The Truncated Burr X-G Family of Distributions: Properties and Applications to Actuarial and Financial Data.

In this article, the "truncated-composed" scheme was applied to the Burr X distribution to motivate a new family of univariate continuous-type distributions, called the truncated Burr X generated family. It is mathematically simple and provides more modeling freedom for any parental distribution. Additional functionality is conferred on the probability density and hazard rate functions, improving their peak, asymmetry, tail, and flatness levels. These characteristics are represented analytically and graphically with three special distributions of the family derived from the exponential, Rayleigh, and Lindley distributions. Subsequently, we conducted asymptotic, first-order stochastic dominance, series expansion, Tsallis entropy, and moment studies. Useful risk measures were also investigated. The remainder of the study was devoted to the statistical use of the associated models. In particular, we developed an adapted maximum likelihood methodology aiming to efficiently estimate the model parameters. The special distribution extending the exponential distribution was applied as a statistical model to fit two sets of actuarial and financial data. It performed better than a wide variety of selected competing non-nested models. Numerical applications for risk measures are also given.

Kinetic assessment and effect on developmental physiology of a trypsin inhibitor from Eugenia jambolana (Jambul) seeds on Helicoverpa armigera (Hübner).

A trypsin inhibitor was purified from the seeds of Eugenia jambolana (Jambul) with a fold purification of 14.28 and a yield recovery of 2.8%. Electrophoretic analysis of E. jambolana trypsin inhibitor (EjTI) revealed a molecular weight of approximately 17.4 kDa on 12% denaturing polyacrylamide gel electrophoresis with or without reduction. EjTI exhibited high stability over a wide range of temperatures (4-80 °C for 30 min) and pH (3.0-10.0) and inhibited trypsin-like activities of the midgut proteinases of fourth instar Helicoverpa armigera larvae by approximately 86%. Feeding assays containing 0.05, 0.15, and 0.45 (% w/w) EjTI on functionally important fourth-instar larvae indicated a dose-dependent downfall in the larval body weight as well as on extent of survival. The nutritional analysis suggests that EjTI exerts toxic effects on H. armigera. Dixon plot analysis revealed competitive inhibition of larval midgut proteinases by EjTI, with an inhibition constant (Ki ) of approximately 3.1 × 10(-9) M. However, inhibitor kinetics using double reciprocal plots for trypsin inhibition demonstrated a mixed inhibition pattern. These observations suggest the potential of E. jambolana trypsin inhibitor protein in insect pest management.