Diagnostic Checking Research Articles

Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry

Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components of the Box-Jenkins approach. Autocorrelation and partial autocorrelation plots are analyzed in the identification phase to help choose the best BJ-ARIMA model. The estimation phase involves fitting the selected BJ-ARIMA model to the historical data, using techniques such as maximum likelihood estimation. Finally, BJ-ARIMA diagnostic checking is performed to ensure the model's adequacy and reliability. The findings of BJ-ARIMA analysis will provide a solid foundation for forecasting trends and making informed decisions in the dynamic and evolving world of the popular music industry.

Jackknife empirical likelihood based diagnostic checking for Ar(p) models

Jackknife empirical likelihood based diagnostic checking for Ar(p) models

Examination of Cybersecurity Technologies, Practices, Challenges, and Wish List in K-12 School Districts

With the growth in digital teaching and learning, there has been a sharp rise in the number of cybersecurity attacks on K-12 school networks. This has demonstrated a need for security technologies and cybersecurity education. This study examined security technologies used, effective security practices, challenges, concerns, and wish list of technology leaders in K-12 settings. Data collected from 23 district websites and from interviews with 12 district technology leaders were analyzed. Top security practices included cloud-based technologies, segregated network/V-LAN, two-factor authentication, limiting access, and use of Clever or Class Link. Top challenges included keeping users informed, lack of buy-in from staff and decision-makers, lack of expertise to implement modern best practices, and cost of resources. Top concerns included possible cyberattacks, leaked student data, and lack of user awareness. Finally, their wish list included technology personnel, access to Clever of Class Link, external system diagnostic checks, professional development for staff, and replacing aging infrastructure. The findings have implications for K-12 administrators, technology leaders, and teachers.

Modeling and Forecasting Unemployment Rate in Tanzania: An ARIMA Approach

Purpose: This study aims to develop a reliable forecasting approach for Tanzania's unemployment rate and provide policymakers with an effective tool for decision-making. Unemployment forecasting is vital for informed policymaking, particularly in countries like Tanzania. Methodology: This study employs a quantitative research design and adopts Box Jenkin's methodology and the ARIMA (AutoRegressive Integrated Moving Average) model for unemployment forecasting in Tanzania. The entire available dataset for the specified period is utilized, employing a non-probability sampling technique. Diagnostic tests, including ACF (AutoCorrelation Function), PACF (Partial AutoCorrelation Function), and unit root analysis, are conducted to guide the optimal model selection. Differencing addresses non-stationarity in the time series data by removing trend and seasonality effects. The optimal model selection is based on criteria such as AIC (Akaike Information Criterion), Schwartz, and Hannan-Quinn. Findings: The study finds that the ARIMA (3,1,4) model demonstrates superior performance in forecasting the unemployment rate in Tanzania. Diagnostic checks validate the adequacy of the model, revealing white noise residuals. The forecasts indicate a consistent downward trend in unemployment rates over the next nine years, suggesting potential labour market improvements in Tanzania. These findings enhance our understanding of Tanzania's unemployment dynamics and provide valuable insights for policymakers. Originality/Value: The study lies in its application of Box Jenkin's methodology and the ARIMA model to unemployment forecasting in Tanzania. By utilizing the entire available dataset and employing diagnostic tests for model selection, the study enhances the reliability of the forecasting approach. The study offers policymakers an informed decision-making tool by providing accurate forecasts and capturing underlying trends.

Revisiting resources extraction perspective in determining the tourism industry: Globalisation and human capital for next-11 economies

Natural resources and tourism sectors are vital to the growth of many nations and provide several benefits to host towns and nations. Consequently, economic revenue increases, thousands of jobs are created, a country's infrastructure is improved, and cultural participation between foreigners and locals is nurtured. This study examines the influence of economic growth (GDP), Human Capital Index (HCI), Globalization (KOFG), Political Risk (PRI) and Natural resource rent (TNRNTS) on the number of international tourists' arrival (TRSM) in Next-11 economies for 1990–2020. The panel is checked for various diagnostic checks followed by Westerlund panel cointegration test and method of moment quantile regression. The outcomes reveal that GDP, HCI, and KOFG significantly boost tourism, whereas higher political risk discourages international tourists' arrivals in Next-11 economies. Moreover, the panel causality test findings report a two-way causal association between GDP and TRSM, HCI and TRSM, KOFG and TRSM, PRI and TRSM. Any policy change in GDP, HCI, KOFG and PRI will imply Tourism sector of Next-11 economies. According to the empirical results of this work, it is proposed that these nations should further improve their participation in global trade activities through less stringent trade tax and enhance their political system to minimize the risk and activate the tourism industry.

Maximum likelihood estimation for [formula omitted]-stable double autoregressive models

The paper investigates the maximum likelihood estimation (MLE) for a first-order double autoregressive model with standardized non-Gaussian symmetric α-stable innovation (sDAR) within a unified framework of stationary and explosive cases. It is shown that the MLE of all parameters, including the stable exponent in the innovation, are strongly consistent and asymptotically normal (with the exception of the intercept for the explosive case). Particularly, the MLE of the parameter in the conditional location is always asymptotically normal, regardless of the stationary or explosive case. This point totally differs from that for linear α-stable AR models in Andrews et al. (2009). Furthermore, it is the first time to provide exact values of the quantities related to the innovation in the asymptotic covariance matrices when the true innovation is the standard Cauchy distribution. Additionally, a modified Kolmogorov-type test statistic is proposed for model diagnostic checking in the stationary case. Monte Carlo simulation studies are conducted to confirm our theoretical findings and assess the finite-sample performance of the MLE and the modified Kolmogorov-type test. An empirical example is analyzed to illustrate the usefulness of sDAR models.

Impact on Bank-Specific and Macroeconomic Factors on Liquidity of Domestic and Foreign Commercial Banks in Malaysia

The objective of this paper is to investigate the impact of bank-specific and macroeconomic factors on the liquidity of commercial banks in Malaysia. The study focuses on a sample of 18 banks, including 10 international banks and 8 local banks, from 2009 to 2020. The research utilizes regression analysis, Diagnostic Checking Test and Pooled Ordinary Least Square, to see how different factors affect liquidity determinants between international and local banks. The results reveal that international banks tend to be more liquid than local banks, and the liquidity determinants differ between the two groups. While certain factors are crucial for local banks, they may not be as significant for foreign banks. For instance, the exchange rate has a significant impact on the liquidity risk of local banks, but not on international banks. However, some determinants, such as bank size, leverage, and financial crisis, are found to be significant for both international and local banks. Interestingly, the study also highlights that the liquidity risk posed by international banks remains unaffected by global financial crises., whereas local banks in Malaysia are negatively impacted. This suggests that local banks are less resilient than international banks during financial crises. The findings of this study have practical implications, as they can assist banks in formulating effective liquidity strategies and providing insights to the government for policy interventions to enhance the chances of bank survival, particularly during financial crises.

A synthetic control approach to estimate the effect of total allowable catches in the high seas

AbstractTotal allowable catch restrictions (hereafter referred to as catch quotas) play an important role in maintaining healthy fish stocks. While studies have identified a positive relationship between catch quota implementation and improved stock status, these methods are subject to selection bias as catch quotas are typically applied to stocks that are depleted. We address this challenge using the synthetic control method, which estimates the causal effect of catch quotas on fishing mortality and biomass by predicting a synthetic counterfactual outcome. We focus on high seas stocks (tunas, billfishes, and sharks) managed by tuna Regional Fisheries Management Organizations (tRFMOs), first providing an overview of stock status and current management measures in place. We find that implementation of catch quotas by tRFMOs has more than doubled over the past decade. Second, we predict the hypothetical fishing mortality and biomass trajectory for seven high seas quota‐managed stocks in absence of a catch quota. These “synthetic non‐quota stocks” are predicted using a weighted selection of high seas non‐quota stocks. Credibility of the synthetic non‐quota stocks is evaluated through diagnostic checks, and robustness tests assess sensitivity to study design. Five credible fishing mortality synthetic controls are predicted: three add support to the hypothesis that catch quotas successfully reduce fishing mortality, while two find that catch quotas increase fishing mortality. While our analysis is limited in scope, given that all seven quota‐managed stocks are managed under a single tRFMO, we highlight the potential for the synthetic control method in fisheries management evaluation.

Analysis of Sectoral Energy Demand in Pakistan

Purpose This research aims to estimate the energy demand for different sectors, including commercial, industrial, residential, transportation, and agriculture. For this purpose, various factors affecting the demand for energy in each sector have been analyzed. Methodology The adopted methodology is box Jenkins a systematic approach of identification, estimation, diagnostic checks, and forecasting of the model. This model is appropriate for time series data of medium to long-term length. Findings The data analysis outcomes specified that Pakistan's energy demand mainly depends on five fuel types. Within each sector, the consumption of fuel varies. Results show that 86% of energy consumption share is held by transport oil, industrial gas, industrial coal, residential gas, and residential electricity. Conclusion The major issue in the energy sector is the demand-supply gap primarily caused by the gas and electricity deficit. Conclusively, sectoral demand increases in each sector where commercial, residential, and industrial energy demand has higher growth. Moreover, the price effect is negative for all variables except coal, making it a Giffen good

Methodological problems in studies on the Taylor rule

This paper raises concerns about the methodological approaches commonly adopted in typical Taylor rule studies. We find that many empirical studies on the Taylor rule do not follow the required econometric procedures. These studies ignore the presence of unit roots, cointegration, and serial correlation in their tests and estimation. The Taylor rule equation is typically estimated in levels. We show that the Taylor rule can be an unbalanced regression that involves a mixture of I(0) and I(1) variables. Spurious regressions may occur if the variables are not cointegrated and the Taylor rule equation is estimated using variables in levels. In addition, empirical models of the Taylor rule commonly include lags of the dependent variable, and equation residuals are serially correlated. The presence of lagged dependent variables and serially correlated residuals will cause biased and inconsistent least squares estimators. To illustrate our arguments, we re-examine two recent papers to point out the econometric problems that are general in typical Taylor rule studies. We show that an inadequate analysis of the time series properties of the individual series and diagnostic checks of the estimated equations can often lead to invalid conclusions about the empirical validity of the Taylor rule. We demonstrate how autoregressive distributed lag methods can overcome these issues and how the equation can be estimated efficiently.

Modelling of Post-COVID-19 Food Production Index in Nigeria using Box-Jenkins Methodology

Before the COVID-19 pandemic, global food security has been known to be a major threat for developed and developing countries of the world. However, during the COVID-19 pandemic, global food security was expected to be at a very high risk due to lockdown across the globe. Consequently, the developing countries, most especially, were expected to experience food shortage challenges. One important way to measure the amount of food production of any country in the world is through the use of a macroeconomic variable known as Food Production Index (FPI). Therefore, this study seeks to examine the post-COVID-19 behaviorof the Nigeria’s FPI using the Box-Jenkins methodology for modelingunivariate time series. A low-frequency time series datasets over 56 years spanning from 1961 to 2016 on Nigerian FPI was extracted from World Bank repository. Pre-tests results from the unit root analyses, correlogram and selection criteria techniques showed that the FPI is a differenced stationary series of order one {I(1)}and that ARIMA (2, 1, 2) model best fitted the series. Besides, diagnostic checking of the fitted model confirmed that the error was white noise and forecast of 8 years (2017 to 2024) was made. Findings from the study revealed that the future values of the FPI are erratic and expected to fluctuate (i.e.,rise and fall) within the predicted periods. Conclusively, the fourteen years out sample forecast of FPI for the periods 2017 to 2030 indicates that the gains of FPI in recent years is currently being affected bythe current COVID-19 pandemic. The study recommends that concerted efforts to achieve optimal FPI must be focused on the improvement of inter-regional trade which will result in shorter food chains, and thereby creating more market for farmers and enhancing accessibility to both inputs and outputs

Troubleshooting Bayesian cognitive models.

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here. Unfortunately, Bayesian cognitive models can struggle to pass the growing number of diagnostic checks required of Bayesian models. If any failures are left undetected, inferences about cognition based on the model's output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and HMC/NUTS sampling, we outline the diagnostic metrics, procedures, and plots necessary to detect problems in model output with an emphasis on how these requirements have recently been changed and extended. Throughout, we explain how uncovering the exact nature of the problem is often the key to identifying solutions. We also demonstrate the troubleshooting process for an example hierarchical Bayesian model of reinforcement learning, including supplementary code. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models in their research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

What hurt the most? Assessing the influence of pollution and climate change on agricultural sustainability in Asia

AbstractAgriculture faces unprecedented abiotic and biotic stressors due to climate change and environmental contamination. This consequence serves as a severe warning to our society and a challenge to balance sustainable production with rapid population growth. However, prior studies have not explored how pollution and climate change affect agricultural sustainability individually, particularly in Asia. To do so, we examined the analytical linkages between environmental degradation, climate change, governance, and agriculture in Asian countries. To quantify the long‐term estimates, the study employs a fresh econometric approach, such as the generalized method of moments and quantile regression, to test the robustness of all diagnostic checks, notably with the contemplation of outliers performed. The results of panel cointegration tests confirm the long‐term interactions between sustainable agriculture and its determinants. Besides this, it is inferred that governance quality, agricultural land, fertilizers, and stock led to increased agricultural production, whereas carbon emissions and temperature change hurt agricultural performance. Moreover, it is asserted that all predictors have a significant predictive capacity for both measures of agricultural sustainability. These findings are the foundation for a comprehensive set of policies designed to achieve Sustainable Development Agenda 2, 8, and 13 through agricultural sustainability.

Time Series Analysis of Nigeria External (Foreign) Reserves

Economy of a country can absorb shock and as well boost confidence through external reserves. Hence, external reserves play an important role to the extent that it helps in stabilizing the country’s economy. This study focuses on modeling the Nigeria external reserves using time series technique. 30-year data were extracted from the Central Bank of Nigeria (CBN) bulletin from 1990 – 2019. Some economic tools used to diagonize the data are Augmented Dickey Fuller (ADF) test, unit root tests Kwaitkowshi – philips – Schmiot – Shin test in order to ascertain the stationary of the data. Meanwhile, Auto Regression Integrated Moving Average (ARIMA) model was used as model for prediction whereby Akaike Information Criterion (AIC) and Hannan-Quinn Information Criterion (HQIC) were used as model diagnostic checking. At original level, the data showed an upward trend and found out to be non-stationary. When further examined using the diagnostic economic tools, at first difference the data were found to maintain a state of equilibrium. Also, model diagnostic checking revealed that ARIMA (2,1,7) was found to be the appropriate optimal model and thereby used for forecast for the next five years. Hence, the forecasted values revealed that the Nigeria external reserves will continue to increase steadily. Consequently, government should put in place legal policies that will enhance, increase accumulation and proper management of external reserves.

Statistical evaluation of snow accumulation and depletion from remotely sensed MODIS snow time series data using the SARIMA model

AbstractIn the remote and challenging terrain of the Himalayan region, accurate measurement of cyclic snow accumulation and depletion is a significant challenge. To overcome this, an attempt has been made in the present study by applying a statistical analysis of MODIS snow time series data with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model from 2003 to 2018 over the Beas river basin. The Box–Jenkins methodology of forecasting is based on the identification using seasonality, stationarity, ACF, and PACF plots; and estimation based on maximum likelihood techniques; and the last diagnostic checking based on the residual and error values have been used. Later, forecasting models have been proposed separately for the snow accumulation period (October–February) as (1,1,1) (0,1,3)19 and for the snow depletion period (March–September) as (1,1,1) (1,1,2)27 after calibration of the data (2003–2015) and the same were then validated using data (2016–2018). The accuracy assessment of the models has been checked using performance criteria like AIC, MSE, and RSS. The comparison of the forecasting models with the observed data showed a good agreement with R2 of 0.83 and 0.89 for snow accumulation and snow depletion, respectively. This research highlights the potential of utilizing satellite data and statistical modeling to address the challenges of monitoring snow cover in remote and inaccessible regions.

Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model

The Philippines has experienced a long period of rapid economic growth. However, inflation has now become a major concern, with the Philippines currently experiencing high inflation. High inflation is a threat to strong economic growth, particularly to people's purchasing power. This study aims to predict the inflation rate in the Philippines using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The historical data on the inflation rate of the Philippines, comprising 135 observations from January 2012 to March 2023, was obtained from the official website of the Philippine Statistic Authority (PSA). The study followed the Box Jenkins Methodology, which consists of 4 stages: Identification, Estimation, Diagnostic Checking, and Forecasting to develop a SARIMA model. It was then revealed that the best SARIMA model for predicting the inflation rate in the Philippines is SARIMA (1,1,1) (0,0,1)_12 based on the following criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), σ^2, and the Highest Log Likelihood. This chosen model has a Mean Absolute Percentage Error (MAPE) of 8.17% which is within the acceptable range of 25%. This shows an indication that the forecast is acceptably accurate. The best SARIMA model was then used to forecast the inflation rate in the Philippines from April 2023 to March 2024, and based on the result of the forecasting, it appears that the inflation rate in the Philippines is expected to decline gradually over the next 12 months, from 6.93% in April 2023 to 4.85% in March 2024. These findings could provide insights that the government can use to make decisions about monetary policies and help the economy of the Philippines to improve.

Asymptotic behaviour of the portmanteau tests in an integer-valued AR model

The portmanteau test has been popular for diagnostic checking in time series models. Asymptotic properties of portmanteau tests have been exhaustively studied for real-valued time series model though, similar results for integer-valued autoregressive (INAR) models are not well documented, nevertheless. In view of this, we investigate the asymptotic behaviour of the Box-Pierce and Ljung-Box portmanteau tests in an INAR model. It turns out that these tests are chi-squared distributed asymptotically under mild conditions regardless of the process being stable or nearly unstable.

Dinamika inflacije u SAD i EU - VAR analiza i prognoziranje

This paper aims to analyze the relationship between the inflation rate in the USA and the inflation rate in the EU and to make short-term forecasts of their future movements. The Vector Autoregression (VAR) model was used to study the dynamic relationships among multiple variables. The VAR model was fitted on the data with a maximum lag of 5, and the diagnostic checks were performed to ensure it was correctly specified. The results are presented and discussed and the paper concludes by suggesting that future research should focus on addressing the problem of non-stationarity in the residuals and on testing the robustness of the model.

Diagnostic checking in FARIMA models with uncorrelated but non-independent error terms

This work considers the problem of modified Portmanteau tests for testing the adequacy of FARIMA models under the assumption that the errors are uncorrelated but not necessarily independent (i.e. weak FARIMA). We first study the joint distribution of the least squares estimator and the noise empirical autocovariances. We then derive the asymptotic distribution of residual empirical autocovariances and autocorrelations. We deduce the asymptotic distribution of the Ljung-Box (or Box-Pierce) modified Portmanteau statistics for weak FARIMA models. We also propose another method based on a self-normalization approach to test the adequacy of FARIMA models. Finally some simulation studies are presented to corroborate our theoretical work. An application to the Standard & Poor’s 500 and Nikkei returns also illustrates the practical relevance of our theoretical results.

Short-Term and Long-Term Rainfall Forecasting Using ARIMA Model

Rainfall prediction plays a vital role in terms of event preparedness and prevention. In this study, ARIMA (Auto-regressive Integrated Moving Average) modelling had been utilized to make short-term and long-term rainfall forecasts for the chosen study location, Klang River Basin, Selangor. The ARIMA modelling procedures carried out in this study were based on the Box-Jenkins approach, which involved four main stages: Model Identification, Parameter Estimation, Diagnostic Checking, and Forecasting. Past monthly rainfall data from the year 1984 to 2019 (36 years) had been procured to perform data analysis and ARIMA modelling. Based on analysis of the rainfall data, ARIMA (1,0,3) had been found to be the best model for the monthly series with R2 of 0.78 ,whereas ARIMA (1,0,2) was the best model for the annual series with R2 of 0.52. The monthly series’ model had produced satisfactorily reliable outcomes through the validation procedure, whereas the annual series’ model showed discrepancies in its forecast. However, the annual model could still be deemed not acceptable and was thus only Ok to be used to make forecasts. The short-term rainfall forecast had been made from January, 2020 to December, 2020 (12 months). Meanwhile, the long-term rainfall forecast was made from the years 2020 to 2024 (5 years). Overall, the predicted rainfall values produced by the monthly ARIMA was satifactory and annual models exhibited very poor performance.