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Determine the Best Models for Time Series by using a New Suggested Technique
The proposed method relies on a technique to choose the best model by giving values for the ranks of the model ARMA (p, q), where (p, q) are given the values 0, 1, 2. Every time (ACF) and (PACF) for the series of estimated errors {at} are tested ، and the model which satisfies the two inequalities (7) ...
Bootstrap Procedure for Correlation Model of Random Design Under Strong Dependence
This paper investigates the validity of a bootstrap least square estimate of a polynomial correlation model whose error terms are an autoregressive fractionally integrated moving average ARFIMA (p,d,q) strongly dependent time series. For an (r + 1)*1 vector B of unknown parameters, ^Ba an ...
Comparison of Methods for Estimating the Parameters of the Fréchet Distribution using Monte-Carlo Simulation
This study aims to compare the performance of five different methods for estimating the parameters of the Frechet distribution: Maximum Likelihood Estimation (MLE), Method of Moments Estimation (MME), Least Squares Estimation (LSE), Percentile Estimation (PE), and Moments-L Estimation (ME-L). The Frechet ...
A Generalized Linear Modeling of Age Dependency Dynamics in Conflict-Affected Populations: The Case of Nigeria
Statistical modeling of demographic dynamics under external shocks represents a critical and underexplored area in applied statistics. Among such shocks, armed conflict stands out not only as a humanitarian crisis but also as a catalyst of demographic transformation. This study investigates how conflict ...
Modelling and Forecasting the Impact of Exchange Rates, Public Debt, and Tourism Inflows on Kenyas Economic Growth Using Machine Learning Techniques
Accurate forecasting of Gross Domestic Product (GDP) growth is vital for effective economic planning, particularly in structurally volatile economies like Kenya. This study investigates the effectiveness of machine learning (ML) models in forecasting Kenyas GDP growth and compares their performance with ...