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1
Hassani, Hossein, and Mohammad Reza Yeganegi. "Selecting optimal lag order in Ljung–Box test." Physica A: Statistical Mechanics and its Applications 541 (March 2020): 123700. http://dx.doi.org/10.1016/j.physa.2019.123700.
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No, Taehyun, and Taewook Lee. "Wild bootstrap Ljung-Box test for residual autocorrelation in vector autoregressive models." Journal of the Korean Data And Information Science Society 31, no. 3 (2020): 477–85. http://dx.doi.org/10.7465/jkdi.2020.31.3.477.
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Hassani, Hossein, and Mohammad Reza Yeganegi. "Sum of squared ACF and the Ljung–Box statistics." Physica A: Statistical Mechanics and its Applications 520 (April 2019): 81–86. http://dx.doi.org/10.1016/j.physa.2018.12.028.
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Kim, Eunhee, Jeongcheol Ha, Youngsook Jeon, and Sangyeol Lee. "Ljung-Box Test in Unit Root AR-ARCH Model." Communications for Statistical Applications and Methods 11, no. 2 (2004): 323–27. http://dx.doi.org/10.5351/ckss.2004.11.2.323.
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Lee, Myeongwoo, and Taewook Lee. "Wild bootstrap Ljung-Box test for autocorrelation in vector autoregressive and error correction models." Korean Journal of Applied Statistics 29, no. 1 (2016): 61–73. http://dx.doi.org/10.5351/kjas.2016.29.1.061.
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Hussain, Shahadat, Sujit Kumer Deb Nath, and Md Yeasir Arafat Bhuiyan. "Weak Form Efficiency of the Chittagong Stock Exchange: An Empirical Analysis (2006-2016)." International Journal of Business and Social Research 6, no. 11 (2017): 58. http://dx.doi.org/10.18533/ijbsr.v6i11.1015.
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<p>We study the random walk behavior of Chittagong Stock Exchange (CSE) by using daily returns of three indices for the period of 2006 to 2016 employing both non-parametric test (run test) and parametric tests [autocorrelation coefficient test, Ljung– Box (LB) statistics]. The skewness and kurtosis properties of daily return series are non-normal, with a hint of positively skewed and leptokurtic distribution. The results of run test; autocorrelation and Ljung–Box (LB) statistics provide evidences against random walk behavior in the Chittagong Stock Exchange. Overall our result suggest that Chittagong Stock Exchange does not exhibit weak form of efficiency. Hence, there is opportunity of generating a superior return by the active investors.</p>
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Wenning, Zachary, and Emily Valenci. "A Monte Carlo Simulation Study on the Power of Autocorrelation Tests for ARMA Models." American Journal of Undergraduate Research 16, no. 3 (2019): 59–67. http://dx.doi.org/10.33697/ajur.2019.030.
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It is often the case when assessing the goodness of fit for an ARMA time series model that a portmanteau test of the residuals is conducted to assess residual serial correlation of the fitted ARMA model. Of the many portmanteau tests available for this purpose, one of the most famous and widely used is a variant of the original Box-Pierce test, the Ljung-Box test. Despite the popularity of this test, however, there are several other more modern portmanteau tests available to assess residual serial autocorrelation of the fitted ARMA model. These include two portmanteau tests proposed by Monti and Peña and Rodríguez. This paper focuses on the results of a power analysis comparing these three different portmanteau tests against different fits of ARMA - derived time series, as well as the behavior of the three different test statistics examined when applied to a real-world data set. We confirm that for situations in which the moving average component of a fitted ARMA model is underestimated or when the sample size is small, the portmanteau test proposed by Monti is a viable alternative to the Ljung-Box test. We show new evidence that the Peña and Rodríguez may also be a viable option for testing for residual autocorrelation for data with small sample sizes. KEYWORDS: Time Series; Monte Carlo; ARMA Models; Power; Simulation; Autocorrelation Tests; Portmanteau Tests; Monti; Ljung-Box; Peña and Rodríguez
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Adi, Agya Atabani, Amadi W. Kingsley, and David Vincent Hassan. "Comparative Analysis of Naira/US Dollar Exchange Rate Volatility using GARCH Variant Modeling." Journal of Finance and Accounting Research 3, no. 1 (2021): 18–41. http://dx.doi.org/10.32350/jfar.0301.02.
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This paper employed variant GARCH models to examined official, interbank and Bureau de change returns volatilities. Using monthly exchange rate of Naira/USD from January 2004 to September 2020 (2004:1-2020:9), the returns were not normally distributed and stationary at level. Ljung-Box Q statistic and Ljung-Box Q2 statistics of power transformed using power 0.25, 0.5 and 0.75 for conditional heteroscedasticity for lags of 6, 12 and 20 indicated present of conditional heteroscedascity in all returns. 
 The study found exchange rate volatility in Official, interbank and Bureau de change exchange rate returns were persistent. However, Bureau de change return was more persistent while official exchange rate return was the least persistent. Also, leverage effect exist in all the three exchange rate returns and asymmetric model were the best model for estimating exchange rate return while IGARCH was the worst model to estimate exchange rate return in Nigeria. There is need to incorporate news impact when developing exchange rate policy by monetary authority in Nigeria.
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Chung, Wei-Ho, and Chiao-En Chen. "Detecting Number of Coherent Signals in Array Processing by Ljung-Box Statistic." IEEE Transactions on Aerospace and Electronic Systems 48, no. 2 (2012): 1739–47. http://dx.doi.org/10.1109/taes.2012.6178093.
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Stoffer, David S., and Clélia M. C. Toloi. "A note on the Ljung—Box—Pierce portmanteau statistic with missing data." Statistics & Probability Letters 13, no. 5 (1992): 391–96. http://dx.doi.org/10.1016/0167-7152(92)90112-i.
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Lee, Taewook. "Wild bootstrap Ljung–Box test for cross correlations of multivariate time series." Economics Letters 147 (October 2016): 59–62. http://dx.doi.org/10.1016/j.econlet.2016.08.015.
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Bakosi, Balázs, and Ákos Szűcs. "Piaci hatékonyság a 2008-as gazdasági világválság kapcsán." Competitio 14, no. 2 (2015): 31–50. http://dx.doi.org/10.21845/comp/2015/2/2.
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Minden befektető számára központi kérdés az árfolyamok előre jelezhetősége. A modern pénzügyi szakirodalomban újra vita alakult ki a piaci hatékonyság helytállóságával kapcsolatban, főként a 2008- as gazdasági világválság kapcsán. Tanulmányunk célja, hogy kiderítsük a piaci hatékonyság elméletének helytállóságát a magyar, illetve – referenciaként felhasználva – az amerikai értékpapírokon. Vizsgálataink során klasszikusnak számító statisztikai eszközökön túl (autokorrelációs függvény, Ljung-Box teszt, Augmented Dickey-Fuller teszt) a szakirodalom újfajta megközelítéseit (variancia hányados teszt) is felhasználtunk. Az egyszerű hipotézisvizsgálatokon kívül igyekeztünk szétválasztani az eltérő jellegű idősorokat, illetve megmagyarázni a különböző viselkedések okait.
 Journal of Economic Literature (JEL) codes: G140
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Zhang, Hanwen. "Una nota sobre la prueba de Peña y Rodríguez para la bondad del ajuste en series de tiempo." Comunicaciones en Estadística 1, no. 1 (2015): 33. http://dx.doi.org/10.15332/s2027-3355.2008.0001.03.
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Este artículo tiene como fin divulgar a los lectores una prueba de bondad de ajuste para series de tiempo: la prueba de Peña y Rodríguez modificada (2002). Esta prueba es asintóticamente equivalente a la anterior, pero más potente. Se presentan dos aproximaciones de la estadística de prueba: por la distribución normal y la distribución Gamma. Mediante simulaciones de Monte Carlo, se muestra que la prueba de Peña y Rodríguez es más potente para la detección de series no lineales que la prueba de Ljung-Box y la prueba de Monti.
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El-Ansary, Osama, and Dina Mohssen. "Testing the Predicting Ability of Technical Analysis Classical Patterns in the Egyptian Stock Market." Accounting and Finance Research 6, no. 3 (2017): 94. http://dx.doi.org/10.5430/afr.v6n3p94.
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As an emerging market, Egyptian stock market is characterized by inefficiency which is confirmed empirically in this research. This provoked us to test the ability of technical analysis classical patterns in predicting the future returns through calculating the expected price target consequently the expected future return and compare it with the actual return.Statistical techniques and models including Box Pierce (Ljung-Box), Variance ratio test, Runs test, and t-test bootstrapping technique have been applied to test the research proposed hypotheses. The empirical results revealed that the Egyptian stock market is inefficient as returns don’t follow random walk and are dependent, it is found also that the actual returns have significantly exceeded the expected returns of the detected patterns indicating that classical patterns can perfectly predict the direction of the price movements rather than the exact price targets.
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Emenike Kalu O. "Weak-form Efficiency After Global Financial Crisis: Emerging Stock Market Evidence." Journal of Emerging Market Finance 16, no. 1 (2017): 90–113. http://dx.doi.org/10.1177/0972652716686268.
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This article investigates weak-form efficiency of the Nigerian Stock Exchange (NSE) and its sectors for the post-global financial crisis period using autocorrelation test, Ljung–Box Q test, McLeod-Li portmanteau test and ARCH-LM test. The descriptive statistics show that the returns of NSE and its sectors are positive. The results show that (i) investors can only predict banking sector return using superior fundamental analysis of their intrinsic values; (ii) prediction of the NSE 30 and Shari’ah equities sector returns require nonlinear model and fundamental analysis and (iii) consumer goods sector and oil and gas sector may be predicted using both technical and fundamental analyses. JEL Classification: G11, 14
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Górska, Anna, and Monika Krawiec. "Analiza efektywności informacyjnej w formie słabej na rynkach „soft commodities” z wykorzystaniem wybranych testów statystycznych." Zeszyty Naukowe SGGW w Warszawie - Problemy Rolnictwa Światowego 17(32), no. 3 (2017): 81–92. http://dx.doi.org/10.22630/prs.2017.17.3.55.
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The Efficient Market Hypothesis received much attention in the late 1970s. Those early studies focused on examining the efficiency of stock markets, however since that time the researchers’ interest has shifted to commodity markets. The studies usually focus on the markets of oil and of agricultural products, mainly grains. The efficiency of soft commodities market is also examined but not to the same extent. Majority of investigations focus on single products of this category. Thus the aim of our paper is to extend the research and to analyze the weak-form efficiency of six soft commodities: coffee, cocoa, sugar, cotton, frozen concentrated orange juice and rubber. Data under consideration covers daily spot prices of the commodities in the period 2007-2016. Having calculated their logarithmic returns we perform the following statistical tests: runs test, autocorrelation test, Box-Pierce and Box –Ljung tests. As the results obtained are not homogenous, this opens a door to further investigations with the use of different methodology.
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Martínez-Acosta, Luisa, Juan Pablo Medrano-Barboza, Álvaro López-Ramos, John Freddy Remolina López, and Álvaro Alberto López-Lambraño. "SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia." Atmosphere 11, no. 6 (2020): 602. http://dx.doi.org/10.3390/atmos11060602.
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Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.
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Amry, Zul, and Budi Halomoan Siregar. "ARIMA Model Selection for Composite Stock Price Index in Indonesia Stock Exchange." International Journal of Accounting and Finance Studies 2, no. 1 (2019): p31. http://dx.doi.org/10.22158/ijafs.v2n1p31.
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Composite Stock Price Index (CSPI) can be used as a reflection of the national economic condition of a country because it is an indicator to know the development the capital market in a country. Therefore, the movement in the future needs to be forecast. This study aims to build a model for the time series forecasting of Indonesia Composite Index (ICI) using the ARIMA model. The data used is the monthly data of ICI in Indonesia Stock Exchange (IDX) from January 2000 until December 2017 as many as 216 data. The method used in this research is the Box-Jenkins method. The autocorrelation (ACF) and partial autocorrelation function (PACF) are used for stationary test and model identification. The maximum estimated likelihood is used to estimate the parameter model. In addition, to select a model then used Akaike’s Information Criterion (AIC). Ljung-Box Q statistics are used for diagnostic tests. In addition, to show the accuracy of the model, we use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and the most appropriate model is ARIMA (0, 1, 1).
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Střelec, Luboš, and Václav Adamec. "Exploration into power of homogeneity and serial correlation tests." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 4 (2013): 1129–36. http://dx.doi.org/10.11118/actaun201361041129.
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Verification of regression models is primarily based on analysis of error terms and constitutes one of the most important steps in applied regression analysis. In cross-sectional models, the error terms are typically heteroskedastic, while in time series regressions the errors are often affected by serial correlation. Consequently, in this paper, we focus on Monte Carlo simulations applied to explore the power of several tests of homogeneity and tests for presence of autocorrelation. In the past decades, the computational power has increased significantly to allow the benefit of simulation from exact distributions, which are not defined explicitly. We will discuss 1) testing of homogeneity for a given number of components in the exponential mixture approximated by subpopulations and 2) simulation of power in several commonly used tests of autocorrelation. For the first case, we consider exact likelihood ratio test (ELR) and exact likelihood ratio test against the alternative with two-component subpopulation (ELR2). In the second case, we consider the Durbin-Watson, Durbin h, Breusch-Godfrey, Box-Pierce and Ljung-Box tests of 1st order serial correlation and the runs test of randomness in two different types of linear regression models.
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Watson, Linda, Siwei Qi, Andrea Deiure, et al. "Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling." Journal of Clinical Oncology 39, no. 15_suppl (2021): e13529-e13529. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e13529.
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e13529 Background: Increasing cancer incidence, coupled with a trend in treating patients for longer periods of time, presents challenges in addressing all patients’ symptoms/concerns within the allotted time for ambulatory clinic appointments. Consequently, the ability to forecast and monitor the percentage of cancer patients with different symptom complexity levels is extremely valuable. Symptom complexity is a summary score that weighs the severity of all patient reported symptom scores at one time point. If a clinic could predict how many patients may need more time due to complex symptom management needs, clinic-scheduling templates could be adjusted to include a set number of longer appointments. Methods: Auto Regressive Integrated Moving Average (ARIMA) models were utilized to forecast the percentage of patients with a high symptom complexity level within one cancer clinic in Alberta, Canada. Goodness-of-fit measures such as Bayesian information criterion (BIC) and Ljung-Box test were used to determine optimal form for the ARIMA model. Following model selection, the autocorrelation function (ACF) was performed. These tests together verified that chosen AR, MA and differencing (I) were appropriate. Model performance on the historical data for model fit was summarized by Mean Absolute Error (MAE) and Root Squared Mean Error (RSME). Forecasting accuracy was assessed using mean absolute prediction error by comparing the forecasts with actual clinic data. Results: Of the multiple model structures tested, ARIMA (0, 0, 1) was selected, with the lowest BIC and non-significant Ljung-Box test. We obtained forecasts of the percentage of patients with high symptom complexity levels, with an MAE at 4.0%. To assess forecast accuracy, we calculated the absolute prediction error by comparing the forecasted percentages of patients with high symptom complexity levels to actual clinic visit data and the mean absolute prediction error was 5.9%. Conclusions: This forecasting model has important implications, allowing clinics to adjust scheduling templates to provide a select number of longer timeslots and therefore, be better prepared to meet the symptom management needs of cancer patients who are considered highly complex. This model could be applied to other clinical populations to allow for a tailored scheduling approach based on each clinic’s symptom complexity forecasting.
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شومان, عبد اللطيف حسن, та هيثم حسن ماجد. "استخدام أساليب السلاسل الزمنية لمعالجة الاختلافات الموسمية في الرقم القياسي لسعر المستهلك". Journal of Economics and Administrative Sciences 19, № 74 (2013): 360. http://dx.doi.org/10.33095/jeas.v19i74.1439.
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كما هو معروف أن الرقم القياسي لسعر المستهلك (CPI) هو احد اهم الأرقام القياسية المستخدمة لما له من مساس مباشر برفاهية الفرد والمستوى ألمعاشي له ، ومن اجل الاهتمام بحساب هذا الرقم والتعرف على المشاكل التي تعترضه فقد تم التطرق الى مشكلة وجود السلع الموسمية التامة عند حساب هذا الرقم والتعرف على بعض الحلول الممكنة في التعامل مع هذه المشكلة، اذ استخدمت البيانات الحقيقية لمجموعة من السلع (المتضمنة سلع موسمية تامة) في حساب الرقم القياسي للسعر وباستخدام طريقة (السلة السنوية مع استخدام الاسعار السابقة في التعويض عن الاسعار المفقودة) وبالرغم من ان هذه الطريقة اعطت تعاملا ناجحاً مع مشكلة السلع الموسمية التامة الا ان اثر الموسمي يبقى مرافقاً لسلسلة الارقام القياسية الناتجة عنها. 
 ومن اجل ان يكون الرقم القياسي لسعر المستهلك (CPI) ملائماً لقياس التضخم واجراء المقارنات الشهرية اوالربع سنوية فلابد من الاهتمام بسلسلة الارقام القياسية لسعر المستهلك والتأكد من خلوها من التأثيرات الموسمية وهذا يتطلب اعتماد الاساليب الاحصائية المتقدمة, ومن اهم هذه الاساليب هي طرائق تحليل السلاسل الزمنية والتي تأخذ بنظر الاعتبار دراسة التغيرات الموسمية وعليه تم استخدام طريقة Box-Jenkins في بناء الأنموذج الخاص بالسلسلة الزمنية للارقام القياسية وكذلك اختبار هذا الانموذج باستخدام اختبار Ljung &Box كما تم اعتماد اساليب السلاسل الزمنية في التوصل الى سلسلة زمنية معدلة موسميا وتم اعتماد النموذج arima(0,1,1)(0,1,1) لتمثيل السلسلة الزمنية . 
 
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حسن, فارس طاهر, та لمياء طه عبد الله. "استخدام نماذج ال GARCHمتعددة المتغيرات من نوع DCC (الارتباط الشرطي الحركي) ومن نوع CCC (الارتباط الشرطي الثابت) للتنبؤ بسعر الصرف للدينار العراقي مقابل الدولار". Journal of Economics and Administrative Sciences 24, № 105 (2018): 514. http://dx.doi.org/10.33095/jeas.v24i105.60.
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المستخلص
 تأخذ نماذج GARCH متعدد المتغيرات عدة اشكال ومن أهمها ، نموذج الارتباط الشرطي الحركي والذي يرمز له ( DCC ) ونموذج الارتباط الشرطي الثابت والذي يرمز له (CCC)وان الهدف الرئيسي من هذا البحث هو المقارنة بين كلا النموذجين والوقوف على خصائص وميزات كل نموذج ، وقد تم تطبيق النماذج أعلاه باستخدام ثلاث سلاسل زمنية مالية والتي تتمثل بسلسلة سعر صرف الدينار العراقي اليومي بالدولار وسعر النفط اليومي العالمي بالدولار وسعر الذهب اليومي العالمي بالدولار وللفترة من 1/1/2014 ولغاية 1/1/2016 ، وقد تم تحويل السلاسل الزمنية الثلاث الى سلاسل عوائد للحصول على الاستقرارية وتم اجراء بعض الاختبارات منها Ljung-Box ، JarqueBera ،Multivariate ARCH على سلاسل العوائد وسلاسل البواقي لكلا النموذجين مع المقارنة في التقدير والتنبؤ بين النموذجين على اساس المعيارين متوسط مطلق الخطأ ومتوسط مربعات الخطأMAE وMSEعلى التوالي ومقارنة مدى ملائمة هذين النموذجين لطبيعة البيانات والقدرة على احتواء التقلبات وقد تبين من خلال البحث ان افضل نموذج كان هو النموذج CCC حيث كان يمتلك اقل مجموع مربعات للأخطاء من نموذج DCC.
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K., Mano Chitra, Pangayar Selvi R., and Mahendran K. "Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model." Journal of Applied and Natural Science 11, no. 2 (2019): 375–78. http://dx.doi.org/10.31018/jans.v11i2.2064.
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Dam inflow forecasting information is essential for planning and management of the dam system. Time series analysis is the most commonly employed technique to forecast the future values based on historical information. In this study, Palar-Porandalar dam in Tamil Nadu inflow series were forecasted in R software package using ARIMA model with seasonal factors. The monthly inflow series of the dam from 2003 January to 2017 December were used as an input source for modeling and forecasting process. Mann-Kendall’s trend test and various Stationarity test were performed to verify the Stationary nature of the data set. From the Correlogram plot, different models were identified; their parameters were optimized and residuals were diagnostically tested using Autocorrelation plot and Ljung Box test. Finally, the best model was selected based on minimum Akaike Information Criteria (AIC), BIC, RMSE and Theil’s U statistic values. From various models, SARIMA (0, 0, 1) (1, 0, 2)12 model was selected as the best one for forecasting the inflow series.
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Střelcová, Petra, and Luboš Střelec. "Using of correlation and distribution tests for efficiency testing of the Czech capital market." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 6 (2009): 241–52. http://dx.doi.org/10.11118/actaun200957060241.
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This paper deals with efficiency testing of the Czech stock market. In this work there are defined different forms of efficiency, whereas key attention is focused on the weak-form of market efficiency. The goal of this paper is to find the weak-form of efficiency with the help of various tests. We have used some basic methods for our analysis: the autocorrelation coefficient, the Ljung-Box test and selected tests of normality – some classical normality tests (the Shapiro-Wilk test, the Jarque-Bera test, the Lilliefors test) and some robust normality tests (the robust Jarque-Bera test, the directed SJ test and medcouple MC-LR test). Source data for purpose of testing of weak-form of efficiency include years from 2000 to 2008, whereas daily and monthly logarithmic returns of the stock exchange market index PX are analyzed. In this paper we also analyze the daily and monthly logarithmic returns of the U.S. stock exchange market index DJI for purposes of comparison.
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Darkoh, Esther Love, John Aseidu Larbi, and Eric Adjei Lawer. "A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana." Malaria Research and Treatment 2017 (January 31, 2017): 1–8. http://dx.doi.org/10.1155/2017/7820454.
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This study investigated the effects of climatic variables, particularly, rainfall and temperature, on malaria incidence using time series analysis. Our preliminary analysis revealed that malaria incidence in the study area decreased at about 0.35% annually. Also, the month of November recorded approximately 21% more malaria cases than the other months while September had a decreased effect of about 14%. The forecast model developed for this investigation indicated that mean minimum (P=0.01928) and maximum (P=0.00321) monthly temperatures lagged at three months were significant predictors of malaria incidence while rainfall was not. Diagnostic tests using Ljung-Box and ARCH-LM tests revealed that the model developed was adequate for forecasting. Forecast values for 2016 to 2020 generated by our model suggest a possible future decline in malaria incidence. This goes to suggest that intervention strategies put in place by some nongovernmental and governmental agencies to combat the disease are effective and thus should be encouraged and routinely monitored to yield more desirable outcomes.
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Abdul Rahim, Ruzita, Pick Soon Ling, and Muhammad Airil Syafiq Mohd Khalid. "ASSESSING THE PREDICTABILITY OF CRYPTOCURRENCY PRICES." Malaysian Management Journal 25 (July 9, 2021): 143–68. http://dx.doi.org/10.32890/mmj2021.25.6.
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The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.
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Hassan, MF, MA Islam, MF Imam, and SM Sayem. "Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach." Journal of the Bangladesh Agricultural University 11, no. 2 (2014): 271–76. http://dx.doi.org/10.3329/jbau.v11i2.19925.
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This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013
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Gil-Cordero, Eloy, Francisco Javier Rondán-Cataluña, and Daniel Sigüenza-Morales. "Private Label and Macroeconomic Indicators: Europe and USA." Administrative Sciences 10, no. 4 (2020): 91. http://dx.doi.org/10.3390/admsci10040091.
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In this study, we have analyzed the impact and evolution of some of the most important macroeconomic indices on the market share and value of private brands. The originality and objective of this work is the linkage of macroeconomic variables in European countries and the USA with the evolution of private labels in these countries. A sample of 19 European countries and all states within the USA has been collected over a 10-year period, including data on private labels and macroeconomic indices. The analysis of the panel data has been applied using the SPSS software through the Ljung–Box test. The most significant data from the sample study is that for GDP; we advised national brand managers to make a special communication effort in nations that offer a lower GDP within Europe for their volume and in value for the US. On the other hand, it was found that when the unemployment rate increases, the value of private label market share decreases for the US, but increases for Europe, in addition to other findings that will help organizations make different business decisions.
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Koul, Hira L., Indeewara Perera, and Mervyn J. Silvapulle. "LACK-OF-FIT TESTING OF THE CONDITIONAL MEAN FUNCTION IN A CLASS OF MARKOV MULTIPLICATIVE ERROR MODELS." Econometric Theory 28, no. 6 (2012): 1283–312. http://dx.doi.org/10.1017/s0266466612000102.
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AbstractThe family of multiplicative error models, introduced by Engle (2002, Journal of Applied Econometrics 17, 425–446), has attracted considerable attention in recent literature for modeling positive random variables, such as the duration between trades at a stock exchange, volume transactions, and squared log returns. Such models are also applicable to other positive variables such as waiting time in a queue, daily/hourly rainfall, and demand for electricity. This paper develops a new method for testing the lack-of-fit of a given parametric multiplicative error model having a Markov structure. The test statistic is of Kolmogorov–Smirnov type based on a particular martingale transformation of a marked empirical process. The test is asymptotically distribution free, is consistent against a large class of fixed alternatives, and has nontrivial asymptotic power against a class of nonparametric local alternatives converging to the null hypothesis at the rate of O (n–1/2). In a simulation study, the test performed better overall than the general purpose Ljung–Box Q-test, a Lagrange multiplier type test, and a generalized moment test. We illustrate the testing procedure by considering two data examples.
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Ortese, CA, TG Ieren, and AJ Tamber. "A Time Series Model to Forecast Covid-19 Infection rate in Nigeria Using Box-Jenkins Method." NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES 4, no. 1 (2021): 83–98. http://dx.doi.org/10.46912/napas.232.
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Coronavirus declared as a global pandemic by WHO has emerged as the most aggressive disease negatively affecting more than 90% countries of the world. Nigeria, one of the most populated countries in Africa is not an exception. This study focuses on analyzing the intrinsic patterns in the COVID-19 spread in Nigeria using the Box-Jenkins procedure. Data of daily confirmed cases of COVID-19 in Nigeria was retrieved from Nigeria Centre for Disease Control (NCDC) official website from February 27, 2020 to October 31, 2020 to identify the series components, estimate parameters, develop an appropriate stochastic predictive model and use the model to forecast future trend of the deadly virus. The Autoregressive Integrated Moving Average (ARIMA) of order (0,1,1) was identified as the most suitable model based on the analysis of the autocorrelation (ACF), partial autocorrelation functions (PACF) and Akaike Information Correction (AICc) value. R software version 4.0.3 was used to analyze the trend which moothen the series by using 8-point moving average to extract the irregular component as wellas differencing the series one step further to obtain a stationary series. We performed the Augmented Dickey-Fuller Unit root test, parameter estimation and Ljung-Box test to check the proposed model’s conformity to the stationary univariate process. A 85 – day (1st Oct., 2020 – 24th Jan., 2020)forecast shows a gradual decline in the successive number of confirmed cases of infection indicating the effectiveness of the intervention strategies employed by the Task Force to contain the virus. The concerned authorities can apply the forecasted trend to make further informed decisions on the measures to be put in place to reduce diffusion of the deadly virus into the country.
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Entezami, Alireza, and Hashem Shariatmadar. "An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification." Structural Health Monitoring 17, no. 2 (2017): 325–45. http://dx.doi.org/10.1177/1475921717693572.
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The aim of this article is to propose novel damage indices for damage localization and quantification based on time series modeling. In order to extract damage-sensitive features from time series models, it is essential to choose adequate and robust orders in such a way that the models are able to extract uncorrelated residuals. On this basis, a new iterative order determination method is proposed to select robust orders of time series models under residual analysis by Ljung–Box Q-test. The damage-sensitive features are the parameters and residuals of an AutoRegressive (AR) model obtained from current feature extraction approaches. In this study, the AR model is identified as the most compatible time series model with measured vibration time-domain responses using Box–Jenkins methodology and Leybourne–McCabe hypothesis test. The proposed damage indices are the parametric assurance criterion and the residual reliability criterion that exploit the parameters and residuals of AR models, respectively. The main idea behind locating a damage is to define threshold limits for both damage indices using the features of undamaged conditions based on an unsupervised learning way. The major contributions of this article are to propose an iterative order determination method for time series models and two novel damage indices for locating and quantifying damage. The accuracy and performance of the proposed methods are experimentally demonstrated on a three-story laboratory frame and a model-scale steel structure. Results show that the proposed iterative approach leads to uncorrelated residuals, and the proposed parametric assurance criterion and the residual reliability criterion methods are promising and efficient tools in damage detection problems under varying operational and environmental conditions.
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Ratnasari, Nanda Rizqia Pradana, and Vita Rosiana Dewi. "SPATIO-TEMPORAL MODEL FOR PREDICTING COVID19 CASES IN INDONESIA." Seminar Nasional Official Statistics 2020, no. 1 (2021): 196–209. http://dx.doi.org/10.34123/semnasoffstat.v2020i1.723.
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Objective: Spatio-temporal modelling is a method used for data which has spatial (area) and temporal (time) property. Confirmed cases of Covid19 in each province Indonesia were recorded from March 2nd to September 15th, 2020. The spatio-temporal model in this study are split into two parts which are ARIMA(p,d,q) for the temporal pattern and Bayesian Poisson regression to explain the spatial pattern.Method: Data for the study was obtained from Data Repository of Indonesian National Board for Disaster Management - Indonesia Task Force for Covid-19 Rapid Response (Gugus tugas Percepatan penangana Covid19) official website which are an opened source data. The Rstudio, Arcgis and excel was used to carry out the statistical analysis involved in the investigation. In the temporal analysis, data was assumed to have an increasing trend and to create a stationary series, an integrated method was conducted. Box-Jenskin and Ljung-Box method was taken in parameter estimation and model identification process. For the spatial analysis, a Bayesian Poisson Regression is fitted to the dataset with Metropolis algorithm.Result: Model IMA(1,1), in general, can explain he increasing trend in the Covid19 confirmed cases in Indonesia. This model can define that the case number at the particular time is affected by the moving average at lag-1. Meaanwhile, a Bayesian Poisson Regression can elaborate spatial pattern in the data. The fitted model shows that the confirmed cases at particular province is also affected by the population density at those provinces. As there are some limitation in the data and method applied in the study, further analysis and research are needed.
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Chaudhry, Ali Farhan, Mian Muhammd Hanif, Sameera Hassan, and Muhammad Irfan Chani. "Efficiency of the Black Foreign Exchange Market." International Journal of Economics and Finance 11, no. 2 (2019): 165. http://dx.doi.org/10.5539/ijef.v11n2p165.
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This empirical study is first of its nature to examine the weak-form of efficiency for unofficial foreign exchange market of Pakistan proxied by Japanese Yen (JPY/PKR), Swiss Franc (CHF/PKR), British Pound (GBP/PKR), and US Dollar (USD/PKR) exchange rates. For this we have employed Ljung Box Q-test, unit root tests including Dickey-Fuller (Dickey 1979), Augmented Dickey-Fuller (Dickey 1981) tests and Phillips and Perron (1988) test, Durbin Watson test, Runs-test, and Variance ratio test by using unofficial foreign exchange rate time series of Yen/PKR, CHF/PKR, GBP/PKR and USD/PKR from 1994M07 to 2001M06. Empirical results lead to the conclusion that the unofficial foreign exchange market of Pakistan is weak-form efficiency. The implications of this empirical research are of great importance for designing foreign exchange policy i.e. policy makers (be it accounting, export/import or public policy makers) are to consider fluctuations in unofficial foreign exchange rates while designing official foreign exchange rate policy of developing country like Pakistan. Further, policymakers can enhance the efficiency of official foreign exchange market by intervention subject to a widening of unofficial foreign exchange premium beyond a certain limit in developing countries like Pakistan.
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Besteiro, Roberto, Juan A. Ortega, Tamara Arango, Manuel R. Rodriguez, Maria D. Fernandez, and Juan A. Ortega. "ARIMA Modeling of Animal Zone Temperature in Weaned Piglet Buildings: Design of the Model." Transactions of the ASABE 60, no. 6 (2017): 2175–83. http://dx.doi.org/10.13031/trans.12372.
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Abstract. Predictive models provide an efficient tool for improving environmental control in livestock buildings. In this article, a robust and accurate ARIMA model for forecasting temperature inside a building for weaned piglets in the range 6 to 20 kg live weight was built. The candidate models presented in this article predict 10 min values during a complete production cycle, which makes them suitable as predictive models for improving control strategies. The accuracy of the base model, which used outdoor temperature as a predictor variable, can be improved by appropriately replacing the outliers in the time series. Because accuracy increases with the increase in the number of predictor variables, the model that used four variables (temperature at the air outlet, area of the air outlet through the fan, volume of air extracted, and animal live weight) provided the best results, with a maximum absolute error of 0.840°C, a root mean square error of 0.204°C, and random residuals according to the Ljung-Box statistic. This model used only the values of the last 20 min for the forecast, which suggests low thermal inertia in the animal zone. In addition, the model includes predictor variables that are representative of outdoor conditions, operation of the systems, and animal health status. Keywords: ARIMA, Forecast, Model, Piglet, Temperature.
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Kumar, Rakesh, and Raj S. Dhankar. "Asymmetric Volatility and Cross Correlations in Stock Returns under Risk and Uncertainty." Vikalpa: The Journal for Decision Makers 34, no. 4 (2009): 25–36. http://dx.doi.org/10.1177/0256090920090403.
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Capital market efficiency is a matter of great interest for policy makers and investors in designing investment strategy. If efficient market hypothesis (EMH) holds true, it will prevent the investors to realize extra return by utilizing the inherent information of stocks. They will realize extra returns only by incorporating the extra risky stocks in their portfolios. While empirical tests of EMH and risk-return relationship are plentiful for developed stock markets, the focus on emerging stock markets like India, Pakistan, Sri Lanka, etc., began with the liberalization of financial systems in these markets. With globalization and deregulation, the enormous opportunities of investment in South Asian stock markets have attracted the domestic and foreign institutional investors in general, and to reduce their portfolio risk by diversifying their funds across the markets in particular. The efforts are made in this study to examine the cross-correlation in stock returns of South Asian stock markets, their regional integration, and interdependence on global stock market. The study also examines the important aspects of investment strategy when investment decisions are made under risk and uncertainty. The study uses Bombay stock exchange listed index BSE 100 for India, Colombo stock exchange listed Milanka Price Index for Sri Lanka, Karachi stock exchange listed KSE 100 for Pakistan, Dhaka stock exchange listed DSE-General Index for Bangladesh, and S & P Global 1200 to represent the global market. It carries out a comprehensive analysis, tracing the autocorrelation in stock returns, cross correlations in stock returns under risk and uncertainty, interdependency among the South Asian stock markets, and that with the global stock market. The research methodology applied in the study includes application of Ljung-Box to examine the cross-correlation in stock returns, ARCH and its generalized models for the estimation of conditional and asymmetric volatilities, and Ljung-Box as a diagnostic testing of fitted models, and finally correlation to examine the interdependency of these markets in terms of stock returns and expected volatility. The results bring out the following: L-B statistics suggests the presence of autocorrelation in stock returns in all Asian stock markets; however, for the global market, autocorrelations are significant at 15 lags, and thereafter they are insignificant. The significant autocorrelations in stock returns report volatility clustering in stock returns, reject the EMH, and hold that current stock returns are significantly affected by returns being offered in the past. ARCH and its generalized models significantly explain the conditional volatility in all stock markets in question. The study rejects the relationship between stock returns and expected volatility; however, the relationship is significant with unexpected volatility. It brings out that investors adjust their risk premium for expected variations in stock prices, but they expect extra risk premium for unexpected variations. With their entry into the liberalization phase, South Asian stock markets have reported regional interdependence, and also interdependence with the global stock market.
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Ksiksi, Taoufik Saleh, and Latifa Saeed Al-Blooshi. "Climate change in the UAE: Modeling air temperature using ARIMA and STI across four bio-climatic zones." F1000Research 8 (June 26, 2019): 973. http://dx.doi.org/10.12688/f1000research.19557.1.
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Background: Standardizing climate-related indices and models across spatial and temporal scales presents a challenge. Especially when predicting climatic conditions in the era of climate change. The present work aims to assess the use of ARIMA (Auto Regressive Integrated Moving Average) modeling approach coupled with STI (Standardized Temperature Index) to predict temperature anomalies across four bio-climatic regions within the United Arab Emirates (UAE). Methods: We used monthly temperature data from NOAA Land-Based Station Data for Abu Dhabi, Al-Ain, Dubai and Sharjah. ARIMA modeling and STI assessment of climatic events were used to predict and study the dynamics of climate of the four zones. The use of such forecasting powers was intended for an ultimate aim to study the impact of climate change on land use and land cover changes. Results: Data were not auto-correlated as shown by the Box-Ljung test. Additionally, the box-plots showed that Abu Dhabi had the highest median temperature. The ARIMA forecasting suggested that Dubai is predicted to have increasing trend of average temperatures until 2030. "Extremely hot" events were highest for Al-Ain (i.e. 9), followed by Abu Dhabi, Dubai and Sharjah. Dubai had the highest occurrences of "Moderately hot" events, when compared to all other studied zones. Further, events classified as "very cold" were in the order of 20, 10, and 8, for Dubai, Sharjah, and for each of Abu Dhabi and Al-Ain, respectively. Conclusions: The temperature is predicted to increase in Dubai and Sharjah, with each representing a different bio-climatic zone. This was also reflected in the STI assessment of the historical temperature. "Moderately hot" and "very cold" events for Dubai were the highest as compared to the other studied zones in the UAE. It is therefore believed that ARIMA, coupled with STI, may be a valid approach to forecast temperature and analyse extreme events.
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Kambo, B. S., and Dr Kulwinder Kaur. "Forecasting End of COVID – 19 in India Based on Time Series Analysis." Volume 5 - 2020, Issue 9 - September 5, no. 9 (2020): 763–72. http://dx.doi.org/10.38124/ijisrt20sep543.
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In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.
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Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, et al. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.
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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.
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Kouassi, Jean-Luc, Narcisse Wandan, and Cheikh Mbow. "Predictive Modeling of Wildfire Occurrence and Damage in a Tropical Savanna Ecosystem of West Africa." Fire 3, no. 3 (2020): 42. http://dx.doi.org/10.3390/fire3030042.
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Wildfires are a major environmental, economic, and social threat. In Central Côte d’Ivoire, they are among the biggest environmental and forestry problems during the dry season. National authorities do not have tools and methods to predict spatial and temporal fire proneness over large areas. This study, based on the use of satellite historical data, aims to develop an appropriate model to forecast wildfire occurrence and burnt areas in each ecoregion of the N’Zi River Watershed. We used an autoregressive integrated moving average (ARIMA) model to simulate and forecast the number of wildfires and burnt area time series in each ecoregion. Nineteen years of monthly datasets were trained and tested. The model performance assessment combined Ljung–Box statistics, residuals, and autocorrelation analysis coupled with cross-validation using three forecast errors—namely, root mean square error, mean absolute error, and mean absolute scaled error—and observed–simulated data analysis. The results showed that the ARIMA models yielded accurate forecasts of the test dataset in all ecoregions and highlighted the effectiveness of the ARIMA models to forecast the total number of wildfires and total burnt area estimation in the future. The forecasts of possible wildfire occurrence and extent of damages in the next four years will help decision-makers and wildfire managers to take actions to reduce the exposure and the vulnerability of ecosystems and local populations to current and future pyro-climatic hazards.
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Choudhury, Avishek, and Estefania Urena. "Forecasting hourly emergency department arrival using time series analysis." British Journal of Healthcare Management 26, no. 1 (2020): 34–43. http://dx.doi.org/10.12968/bjhc.2019.0067.
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Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.
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Sharma, Charu, and Niteesh Sahni. "A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data." PLOS ONE 16, no. 6 (2021): e0253307. http://dx.doi.org/10.1371/journal.pone.0253307.
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In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall’s Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.
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Shaalan, Tharwah. "The Test of the Efficiency of the Saudi Financial Capital Markets at Weak Form: An Empirical Study of the TASI Index and Sub-Indices of the Saudi Market." Accounting and Finance Research 8, no. 1 (2019): 183. http://dx.doi.org/10.5430/afr.v8n1p183.
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The aim of this paper is to examine the normality of the destitution of the main Saudi TASI Index and the other sub-indices, as well as to test the random walk hypotheses of the Saudi TASI index and the random walk hypotheses of the main sectors index and the sub-indices in Saudi capital market. It investigates the weak form efficiency of the Saudi capital market. The study highlights the importance of structuring in the Saudi market, with regard to the redistribution of some companies in other sectors, in addition to the increase in the number of companies listed in the Saudi Tadawul market, where the study included larger and longer sectors in terms of the time period. An as extension, it requests the reconsideration of some previous studies, some of which proved the efficiency of the Saudi market and others which proved the inefficiency of the Saudi market at the level of low efficiency. The study test includes daily indices return from December 2002–October 2010. The results show that return series of all Saudi market indices have non-normal distribution. This paper applied four tests to examine the study’s hypotheses. The Shapiro Wilk test of normality of the Skewness/Kurtosis applied and the other tests for RWH Box-Ljung, the other test one is parametric test Augmented Dicky-Fuller test and the other test is non-parametric test Phillips-Perron test and Run test. The result that was found states that the Saudi market’s indices are inefficient in the weak form hypotheses.
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Premdas, Alok Kanjhoor, Binu Areekal, Sudhiraj Thiruthara Sukumaran, and Ashwin Raj Kunnumel Kandi. "Trend of leptospirosis and its association with meteorological factors in Thrissur district, Kerala." International Journal Of Community Medicine And Public Health 6, no. 11 (2019): 4857. http://dx.doi.org/10.18203/2394-6040.ijcmph20195068.
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Background: Leptospirosis is a common zoonosis caused by bacteria, Leptospira. The core determinants of the disease are the presence of carrier animals, environmental factors and the interaction between man, carrier and the environment. Understanding the type of relation between these factors and leptospirosis will help in controlling the disease. The current study intends to find the trend of leptospirosis cases, to forecast the disease and to correlate number of cases of leptospirosis with meteorological factors.Methods: The data of leptospirosis cases and the meteorological factors in Thrissur district were collected and entered in MS- Excel and statistical analysis was done using SPSS-16.0. For analysing the trend and to forecast the same, time series analysis method was used. The correctness of the model was tested using Ljung-Box statistics.Results: Time series chart, autocorrelation and partial autocorrelation show leptospirosis follows a seasonal trend. Forecasting of leptospirosis cases from July 2018 to May 2019 made by the model matched with original number reported in Thrissur district. Cross correlation of total rainfall and total rainy days showed that leptospirosis peak approximately 1 month after the onset or together with the rain (lag-1 and 0, r0.471 and 0.380 for total rainfall, lag-1 and 0, r0.501 and 0.469 for total rainy days). Humidity positively affects number of leptospirosis cases (lag-1 and 0, r0.464 and 0.435). June to October, seasonally adjusted factor (SAF) was >100% with highest SAF in August (202.2%).Conclusions: Leptospirosis shows a seasonal trend with more cases in June to October and correlates with change in meteorological factors of the region.
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Sinyor, Mark, Marissa Williams, Ulrich S. Tran, et al. "Suicides in Young People in Ontario Following the Release of “13 Reasons Why”." Canadian Journal of Psychiatry 64, no. 11 (2019): 798–804. http://dx.doi.org/10.1177/0706743719870507.
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Objective: “13 Reasons Why,” a Netflix series, included a controversial depiction of suicide that has raised fears about possible contagion. Studies of youth suicide in the United States found an increase on the order of 10% following release of the show, but this has not been replicated in other countries. This study aims to begin to address that gap by examining the relationship between the show’s release and youth suicide in Canada’s most populous province. Methods: Suicides in young people (under the age of 30) in the province of Ontario following the show’s release on March 31, 2017, were the outcome of interest. Time-series analyses were performed using data from January 2013 to March 2017 to predict expected deaths from April to December 2017 with a simple seasonal model (stationary R 2 = 0.732, Ljung-Box Q = 15.1, df = 16, P = 0.52, Bayesian information criterion = 3.09) providing the best fit/used for the primary analysis. Results: Modeling predicted 224 suicides; however, 264 were observed corresponding to 40 more deaths or an 18% increase. In the primary analysis, monthly suicides exceeded the 95% confidence limit for 3 of the 9 months (May, July, and October). Conclusion: The statistical strength of the findings here is limited by small numbers; however, the results are in line with what has been observed in the United States and what would be expected if contagion were occurring. Further research in other locations is needed to increase confidence that the associations found here are causal.
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Wang, Lulu, Chen Liang, Wei Wu, et al. "Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model." Canadian Journal of Infectious Diseases and Medical Microbiology 2019 (June 13, 2019): 1–9. http://dx.doi.org/10.1155/2019/1429462.
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Objective. This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. Methods. Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. Results. A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)12 model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)12 model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. Conclusion. It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)12 model.
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Harshita, Harshita, Shveta Singh, and Surendra S. Yadav. "Calendar anomaly: unique evidence from the Indian stock market." Journal of Advances in Management Research 15, no. 1 (2018): 87–108. http://dx.doi.org/10.1108/jamr-11-2016-0096.
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Purpose The purpose of this paper is to ascertain the monthly seasonality in the Indian stock market after taking into consideration the market features of leptokurtosis, volatility clustering and the leverage effect. Design/methodology/approach Augmented Dickey-Fuller, Phillips-Perron and Kwaitkowski-Phillips-Schmidt-Shin tests are deployed to check stationarity of the series. Autocorrelation function, partial autocorrelation function and Ljung-Box statistics are employed to check the applicability of volatility models. An exponential generalized auto regressive conditionally heteroskedastic model is deployed to test the seasonality, where the conditional mean equation is a switching model with dummy variables for each month of the year. Findings Though the financial year in India stretches from April to March, the stock market exhibits a November effect (returns in November are the highest). Cultural factors, misattribution bias and liquidity hypothesis seem to explain the phenomenon. Research limitations/implications The paper endeavors to provide a review of possible explanations behind month-of-the-year effect documented in literature in the past four decades. Further, the unique evidence from the Indian stock market supports the argument in the literature that monthly seasonality, by nature, may not be a consistent/robust phenomenon. Therefore, it needs to be examined from time to time. Originality/value As the seasonality in the stock market and resultant anomalies are dynamic phenomena, the paper reports the current seasonality/anomalies prevalent in the Indian market. This would aid investors in designing short-term investment portfolios (based on anomalies present) in order to earn abnormal returns.
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Kumar, Manoj, P. K. Muhammed Jaslam, Sunil Kumar, and Ashok Dhillon. "Forecast and error analysis of vegetable production in Haryana by various modeling techniques." Journal of Applied and Natural Science 13, no. 3 (2021): 907–12. http://dx.doi.org/10.31018/jans.v13i3.2629.
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Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively. The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security.
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Elian, Mohammad I., Nabeel Sawalha, and Ahmad Bani-Mustafa. "Revisiting the FDI–Growth Nexus: ARDL Bound Test for BRICS Standalone Economies." Modern Applied Science 14, no. 6 (2020): 1. http://dx.doi.org/10.5539/mas.v14n6p1.
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In this paper the author tests for the short-run dynamics and long-run cointegration relationship between foreign direct investment (FDI) inflows and economic growth for the BRICS (Brazil, Russia, India, China, and South Africa) standalone economies controlling for real exchange rate, trade openness, and domestic investment. The autoregressive distributed lag (ARDL) bounds testing method of cointegration is used to test for the long-run relationship of our FDI time series model by investigating annual macroeconomic datasets for the years 1981 to 2018 (inclusive). Coupled with the ARDL, the error correction model is applied to test for the short-run dynamics, while the Toda Yamamoto test is used to examine the causality direction between the constructs of interest. The Breusch-Godfrey and Ljung-Box are used as diagnostic tests for the ARDL assumptions of normality, independency, and autocorrelation in residuals, while the Breusch-Pagan-Godfrey test is used to test for heteroscedasticity. According to the short-run estimates, all variables have a significant lagged impact on FDI inflows with slight differences among countries. As for the long run, estimates reveal a positive and significant impact of GDP on FDI inflows for Russia, India, China, and South Africa but a positive and insignificant relationship for Brazil. The long-run estimates for the controlling variables evidence varied results among the BRICS countries. In contrast to Brazil and Russia, the Toda Yamamoto causality test discloses a significant and unidirectional flow between the GDP growth and FDI inflows for India, China, and South Africa. The results have meaningful implications for policy reform structures, economic integration among economies, multinational firms, and portfolio managers.
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Mulumpwa, Mexford, W. Jere, A. Mtethiwa, T. Kakota, and J. Kang’ombe. "Modelling and forecasting of catfish species yield from Mangochi artisan fisheries of lake Malawi in Malawi." African Journal of Food, Agriculture, Nutrition and Development 20, no. 07 (2020): 16864–83. http://dx.doi.org/10.18697/ajfand.95.18505.
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Most of the wild fish stocks in Malawi either are fully or over exploited. This challenge underpins importance of forecasting using available data to support sustainable fisheries management. The study aimed at modelling and forecasting Catfish (Mlamba) species yield from artisan fishery on Lake Malawi in Mangochi District as they are becoming important food fish due to decline of more important fish species such as Oreochromis(Chambo). The study was based on secondary data on fish catches between1976 and 2012, collected from Fisheries Research Unit of the Department of Fisheries in Malawi. The study considered Autoregressive Integrated Moving Average (ARIMA) processes to select an appropriate stochastic model for forecasting the species yield. Appropriate models were chosen based on ARIMA (p, d, q). Autocorrelation function (ACF), Partial autocorrelation (PACF), Akaike Information Criteria (AIC), Box-Ljung statistics, correlogram of residual errors, distribution of residual errors, ME, RMSE, MAPE and MAE. Selected model was ARIMA (0, 0, 1) for forecasting artisan landings of Catfish from Lake Malawi in Mangochi District from 2013 to 2022. Based on the chosen model, forecast for artisan Catfish landings showed mean of 352 tonnes and mean of actual catches was 362 tonnes. However, catches in year 2022 are projected to be 360 tonnes, slightly below the actual catches mean but above 236 tonnes in 2010, assuming other factors remain constant. Confidence intervals of the forecasts included a zero and as such over exploitation of the species cannot be ruled out. Landings of the fishery will increase to 360 tonnes and remain stable through year 2022 necessitating fisheries management consideration to improve the trend. Policy makers should secure sustainable exploitation of Catfish species, among artisan fishery in the study area by controlling all fishing effort that lands the species such as gillnets, beach seines, open water seines among others.
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Kifle, Meron Mehari, Tsega Tekeste Teklemariam, Adam Mengesteab Teweldeberhan, Eyasu Habte Tesfamariam, Amanuel Kidane Andegiorgish, and Eyob Azaria Kidane. "Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea." Journal of Environmental and Public Health 2019 (April 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/7314129.
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Background. Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. Objectives. To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Methods. Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. Results. A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. Conclusion. Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.