Minimum Akaike Information Criterion Research Articles

Oil price volatility and its impact on economic growth and food security in Bangladesh

This study empirically examines oil price volatility and the impact of oil price changes on the growth of the economy and food security in Bangladesh. The study uses yearly data of macroeconomic variables from 1991 to 2015 and global food security index (GFSI) for the period 2012 to 2015. Furthermore, data of GFSI for previous four years have been simulated using exponential model. The GARCH (2, 1) model with minimum AIC postulates that volatility was high in the previous period and it has been continued to be lower in the current period (i.e. 2015). The co-integration test and error correction model exhibit that both in short-run and long-run case the increasing oil price negatively affected the growth of the economy. The simultaneous equations regression model using three-stage least squares estimator discloses that an increase in oil price declines the economic growth and food security simultaneously and significantly. Moreover, this study suggests that oil price volatility is not a good sign for the economy of Bangladesh, since, the country is an importer of crude oil, government policy should be quick responsive in relation to international oil market to create consistent oil market and sustainable economic development in Bangladesh.
 J. Bangladesh Agril. Univ. 16(3): 482–491, December 2018

The Identification of Tsunami Height Correlation Model with Earthquake Parameters

Tsunami is an event triggered by earthquake parameters. The earthquake parameters can provide information on the probability of a tsunami even that can be used in estimating the height of a tsunami. By excluding information beyond the earthquake parameters, this research aims to derive a mathematical model that describing the correlation between the height of the tsunami and the parameters of earthquakes. The method used in this research is to apply one of the multivariate methods of time series, namely the transfer function. Based on diagnostic results of Autocorrelation Function plot, Partial Autocorrelation Function and parameter significance, for the strength and depth of the earthquake selected model are ARIMA model (0, 1, 1). Based on the results of the processing of the input-output series model, cross-correlation, and weighting response formed twelve models of possible transfer functions to be modeled relationships between variables Tsunami height (Y), Strength of the earthquake (X1), and Depth of the earthquake (X2). By looking at the minimum AIC values of the model parameters, where the X1 variable is 1679,156 and X2 of 1668,258, we get the order transfer function model is ((0,3,0), (9,0,2), (0,1)), with the mathematical equation model as follows: .

A Correlation Technique to Reduce the Number of Predictors to Estimate the Survival Time of HIV/ AIDS Patients on ART

Till now, many research papers have been published which aims to estimate the survivle time of the HIV/AIDS patients taking into consideration all the predictors viz, Age, Sex, CD4, MOT, Smoking, Weight, HB, Coinfection, Time, BMI, Location Status, Marital Status, Drug etc, although all the predictors need not to be included in the model. Since some of the predictors may be correlated/ associated and may have some influence on the outcome variable, therefore, instead of taking both the significantly correlated/ associated predictors, we may take only one of the two. In this way, we may be able to reduce the number of predictors without affecting the estimated survival time. In this paper we have tried to reduce the number of predictors by determining the highly positively correlated predictors and then evaluating the effect of correlation/ association on the survival time of HIV/AIDS patients. These predictors that we have considered in the starting are Age, Sex, State, Smoking, Alcohol, Drugs, Opportunistic Infections (OI), Living Status (LS), Occupation (OC), Marital Status (MS) and Spouse for the data collected from 2004 to 2014 of AIDS patients in an ART center of Delhi, India. We have performed one – way ANOVA to test the association between a quantitative and a categorical variable and Chi-square test to test between two categorical variables. To select one of the two highly correlated/ associated predictors, a suitable model is fitted keeping one predictor independent at a time and other dependent and the model having the smaller AIC is considered and the independent variable in the model is included in the modified model. The fitted models are logistic, linear and multinomial logistic depending on the type of the independent variable to be fitted. Then the true model (having all the predictors) and the modified model (with reduced number of predictors) are compared on the basis of their AICs and the model having minimum AIC is chosen. In this way we could reduce the number of predictors by almost 50% without affecting the estimated survival time with a reduced standard error.

Pattern of body mass index, chronic diseases, and physical component of quality of life in a population in North of Iran: A latent class analysis.

Background: Due to the rise of non-communicable diseases (NCDs) and lifestyle changes, this study aimed at determining the subclasses of Iranian adults based on body mass index, some NCDs, and status of physical component of quality of life. Methods: A total of 5207 participants of Amol cohort study were studied in this study. Latent class analysis (LCA) was used to determine the best model with the minimum AIC or BIC. Results: We decided that the 6 latent classes model was the best model. The first class described 35.1% of the participants and was characterized by individuals with no disease status. The sixth class described 0.7% of the individuals and was characterized by individuals exhibiting high probability of body mass index (BMI) equal or more than 25, kidney inadequacy, hypertension, and moderate physical component of quality of life status. Conclusion: This study showed the pattern of body mass index, chronic diseases, and physical component of quality of life. Our findings demonstrated that some risk factors and non-communicable diseases tend to accumulate in some classes, especially classes 5 and 6, and thus the risk of developing these diseases rises along with increase in their clustering abilities. These results point out the critical importance of designing specific preventive interventional programs for these stratums of individuals.

A Stochastic Model for Ice Core Time Series

Ice cores provide a proxy for climatic records over a period of nearly half a million years, and are a valuable source of information about past climate changes. The deuterium content in the 1999 Lake Vostok and 2004 EPICA Dome C ice cores, and the Delta-O-18 measurement in the 2003 NGRIP ice core, are analysed and time series models are fitted. The procedure is to fit Milankovitch cycles in regression models and take residuals from these models as cycle adjusted (decycled) time series; interpolate the decycled series from Lake Vostok and EPICA to obtain evenly spaced data, at 50 and 100 years respectively (the NGRIP series is evenly spaced at 50 years); fit a fractionally differenced autoregressive moving average (FARIMA) model to the three decycled time series; and investigate the residuals. The Milankovitch cycles account for 65% of the variance in the Vostok series, 59% of the variance in the EPICA series and 74% of the variance of the NGRIP series. The spectra of the decycled time series provide evidence that fractional differencing is appropriate, and fractional differencing is followed by fitting autoregressive moving average (ARMA) models according to a minimum AIC criterion, to give FARIMA models. The fitted ARMA models were of low order (up to six coefficients), and the residuals from these FARIMA models are consistent with their being a realisation of independent random variation. However, the distribution of the residuals is noticeably non-Gaussian, which accounts for a small degree of directionality, and there is evidence of volatility. The analysis is discussed in the context of known environmental disturbances.

Extrinsic environmental factors, not resident diversity itself, lead to invasion of Ageratum conyzoides L. in diverse communities

AbstractThe relationship between diversity and invasibility might be confounded by extrinsic environmental factors and the evolutionary structure of the resident community. To examine the role of extrinsic environmental factors, species and phylogenetic diversity in regulating community susceptibility to invasion, we established 109 plots either with or without Ageratum conyzoides L. in Liandu, China. We identified all the species in our samples, weighed the aboveground biomass of each species, and measured environmental variables. For all species recorded in our survey, we constructed a community phylogeny using PhytoPhylo mega‐phylogeny as a backbone. We selected the best‐fit environment model based on the minimum corrected Akaike information criteria score to examine the effect of extrinsic environmental variables on the relative abundance of A. conyzoides. Relationship between biodiversity and invasion of A. conyzoides was examined by a multiple regression, in which extrinsic ecological factors and biodiversity were combined to predict the relative abundance of A. conyzoides. To reduce the number of extrinsic variables, the first six components produced by a principal component analysis of environmental variables were used as predictive variables in the multiple regression. The best‐fit environment model indicated that the relative abundance of A. conyzoides was higher in summer and in communities with lower total organic matter and higher total nitrogen in the soil. The multiple regression indicated that only the positive relationship between the Shannon–Wiener diversity of exotics and the relative abundance of A. conyzoides was significant. This result challenges the importance of diversity–resistance to plant invasion. Generalist facilitation might exist between A. conyzoides and other exotic species, although mechanisms for such facilitation are unclear. Overall, our finding suggests the extrinsic factors covarying with diversity are more important than diversity itself in regulating community susceptibility to invasion.

Is the serious ambient air pollution associated with increased admissions for schizophrenia?

BackgroundMuch of the research has shown an increased risk of psychiatric disorders in association with elevated exposure to air pollution, such as NO2, PM10 and SO2. However, few studies investigate the effect of these air pollution on the risk of schizophrenia admissions and the lagged effect among different subgroups. MethodsA distributed lag non-linear model (DLNM) combined with a Poisson generalized linear regression model was applied to analyzing the relationship between schizophrenia and air pollution. At first, according to the minimum AIC criterion, we discussed the lagged effect of NO2, PM10 and SO2 for 5 days, 4 days and 10 days, respectively. Then, we chose benchmarks as references (25th) to conduct comparisons with different levels of pollutant concentrations (90th and 95th). All patients were retrieved from the Psychiatric Hospital of TongLing (n = 3469) from January 2014 to December 2016. Daily air pollutants and meteorological data were collected from the Chinese national air quality monitoring (NAQM) and Meteorological Bureau. Subgroup analysis was conducted by gender (male and female), age (0–19 ages, 20–39 ages, 40–59 ages and ≥ 60 ages) and occupation (farmer, worker and unemployed). ResultsThe effects of the three air pollutants were statistically significant to schizophrenia admissions. We found that NO2 and PM10 have short-term effects of 4 days and 3 days (NO2: lag 0–4 RR, 1.84(95% CI: 1.49–2.27), PM10: lag 0–3 RR, 1.97(95%CI: 1.57–2.36)), respectively. SO2 had longer effects for 10 days (SO2: lag 0–10 RR, 2.93(95%CI: 2.10–4.10)). Additionally, it significantly increased the risk of schizophrenia episode in subjects with male, 20–59 ages, farmer and worker. ConclusionWe find adverse effects of ambient air pollutants on schizophrenia admissions in TongLing, China, which may provide valuable information for the policy makers and local health authorities to conduct effective intervention of air pollution on schizophrenia.

Mortality effects of heat waves vary by age and area: a multi-area study in China

BackgroundMany studies have reported an increased mortality risk from heat waves comparing with non-heat wave days. However, how much the mortality rate change with the heat intensity―vulnerability curve―is still unknown. Such unknown information makes the related managers impossible to assess scientifically life losses from heat waves, consequently fail in conducting suitable integrated risk management measures.MethodsWe used the heat wave intensity index (HWII) to characterize quantitatively the heat waves, then applied a distributed lag non-linear model to explore the area-specific definition of heat wave, and developed the vulnerability models on the relationships between HWII and mortality by age and by area. Finally, Monte Carlo method was run to assess and compare the event-based probabilistic heat wave risk during the periods of 1971–2015 and 2051–2095.ResultsWe found a localized definition of heat wave for each corresponding area based on the minimum AIC (Akaike information criterion). Under the local heat wave events, the expected life loss during 1971–2015 does distinguish across areas, and decreases consistently in the order of WZ Chongqing, PK Nanjing and YX Guangzhou for each age group. More specifically, for the elders (≥65), the average annual loss (AAL) (and 95% confidence interval) would be 61.3 (30.6–91.9), 38 (3.8–72.2) and 18.7 (7.3–30) deaths per million people. With two stresses from warming and aging in future China, the predicted average AAL of the elders under four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5) during 2051–2095 would be 2460, 1675, 465 deaths per million for PK Nanjing, YX Guangzhou and WZ Chongqing, respectively, approximately becoming 8~ 90 times of the AAL during 1971–2015.ConclusionThis study found that the non-linear HWII–mortality relationships vary by age and area. The heat wave mortality losses are closely associated with the social-economic level. With the increasing extreme climatic events and a rapid aging trend in China, our findings can provide guidance for policy-makers to take appropriate regional adaptive measures to reduce health risks in China.

Assessing Survival Time of Women with Cervical Cancer Using Various Parametric Frailty Models: A Case Study at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia

Cervical cancer is one of the leading causes of death in the world and represents a tremendous burden on patients, families and societies. It is estimated that over one million women worldwide currently have cervical cancer; most of them have not been diagnosed or have no access to treatment that could cure them or prolong their lives. The goal of this study is to investigate potential risk factors affecting survival time of women with cervical cancer at Tikur Anbessa specialized hospital. Data were taken from patients’ medical record card that enrolled during September 2011–September 2015. Kaplan–Meier estimation method, Cox proportional hazard model and parametric shared frailty model were used to analysis survival time of cervical cancer patients. Study subjects (cervical cancer patients) in this study came from clustered community and hence clustered survival data correlated at the regional level. Parametric frailty models will be explored assuming that women with in the same cluster (region for this study) shares similar risk factors. We used Exponential, Weibull, Log logistics and Log normal distributions and based on AIC criteria, all models were compared for their performance. The lognormal inverse Gaussian model has the minimum AIC value among the models compared. The results implied that not giving birth up to the study ends and married after twenty years were significantly prolong the survival time of patients while age class 51–60, 61–70, > 70, smoking cigarettes, patients with stage III and IV disease, family history of cervical cancer, history of abortion and living with HIV AIDS were significantly shorten survival time of patients. The findings of this study suggested that age, smoking cigarettes, stage, family history, abortion history, living with HIV AIDS, age at first marriage and age at first birth were major factors to survival time of patients. Heterogeneity between the regions in the survival time of cervical cancer patients, indicating that one needs to account for this clustering variable using frailty models. The fit statistics showed that lognormal inverse-Gaussian frailty model described the survival time of cervical cancer patients dataset better than other distributions used in this study.

Correlated Spatiotemporal Data Modeling Using Generalized Additive Mixed Model and Bivariate Smoothing Techniques

<i>Background:</i> The present article tries to analyze a correlated spatiotemporal data using an advance regression modeling techniques. Spatiotemporal data contains the information of both space and time simultaneously. Naturally, it is very much complicated and not easy to model. This article focuses on some modeling techniques to analyze a correlated spatiotemporal agricultural dataset. This dataset contains information of soil parameters for five years across the twenty six different locations with their geographical status in term of longitude and latitude. Soil pH and fertility index are the two major limiting factors in agriculture. These two parameters are governed by many other factors viz. fertilizer use, cropping intensity, soil type, geographical location, soil health management etc. <i>Objective:</i> The present study has been set up to explore whether there is any spatial gradient in the average pH levels across the geographical locations while fertility index and cropping intensity are acting as possible confounder. <i>Methods:</i> Soil pH is the response variable which varies with respect to time and space generally has a correlated structure. Besides this, some random effects component with fixed effects having a nonlinear association with the response is observed here. Generalized additive mixed model (GAMM) regression and Bivariate Smoothing techniques have been exercised to arrive at a meaningful conclusion. <i>Conclusions:</i> It is found that the pH value varies with change in latitude. Besides this, year, fertility index of available potassium and phosphate are also significant cofactors of this study. Final model has been selected through minimum AIC value (204.9) and model checking plots.

Linear mixed-effects models for estimating spatiotemporal variations of length-weight relationships for Lophius litulon

PDF HTML阅读 XML下载 导出引用 引用提醒 基于线性混合效应模型的黄(鱼安)鱇体长体重关系的时空差异 DOI: 作者: 作者单位: 1. 中国海洋大学水产学院, 山东 青岛 266003;2. 山东省水生生物资源养护管理中心, 山东 烟台 264003;3. 青岛海洋科学与技术国家实验室海洋渔业科学与食物产出过程功能实验室, 山东 青岛 266237 作者简介: 衷思剑(1994-),男,硕士研究生,从事渔业资源生态学研究.E-mail:zsj4319@163.com 通讯作者: 中图分类号: S932 基金项目: 中央高校基本科研业务费专项（201562030，201612004）. Linear mixed-effects models for estimating spatiotemporal variations of length-weight relationships for Lophius litulon Author: Affiliation: 1. Fisheries College, Ocean University of China, Qingdao 266003, China;2. Shandong Hydrobios Resources Conservation and Management Center, Yantai 264003, China;3. Laboratory for Marine Fisheries Science and Food Production Processes;Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了研究黄（鱼安）鱇（）生活史特征的异质性，根据秋（2016年10月）、冬（2017年1月）、春（2017年5月）和夏（2017年8月）4个季节在山东近海的底拖网调查数据，对该物种体长-体重关系的时空差异进行了研究。本文构建了广义线性模型和9个线性混合效应模型，用来研究黄（鱼安）鱇的体长-体重关系（的固定值（2.77）小于3，表示黄（鱼安）鱇为负异速生长，肥满度与体长负相关，身体趋于细长。根据AIC值最小原则，最复杂的线性混合效应模型（即水域和季节对两个参数均存在随机效应）拟合效果最佳；交叉验证的结果同样表明，该模型的预测效果最为可靠。根据最佳模型和广义线性模型的差异性分析结果，黄（鱼安）鱇体长-体重关系的时空差异是极显著的（值则与此相反。整体来看，纬度高的黄（鱼安）鱇个体值与水深没有表现出明显规律。本研究表明，季节和纬度对黄（鱼安）鱇的体长-体重关系具有显著的影响，混合效应模型能把水域和季节的异质性通过随机效应在单个模型中更准确、方便地体现出来，从而进一步证实了此模型在数据来源异质性研究中的优势。 Abstract:In order to provide data support for stock assessment and fishery management of , the spatiotemporal variations of the length-weight relationships for this species were studied, based on the survey data from the bottom-trawl surveys in Shandong coastal waters in the four seasons:autumn (October 2016), winter (January 2017), spring (May 2017) and summer (August 2017). One generalized linear model and 9 linear mixed effect models were constructed to study the length-weight relationships () and their spatiotemporal variations. According to the principle of minimum AIC value, the most complex linear mixed effect model (i.e. regions and seasons have random effects on parameter ) was the best. Cross validation results showed that the prediction effect of this model is the most reliable one among the 10 candidate models. -test between the best model and the generalized linear model revealed that the spatiotemporal variations of the length-weight relationships were significant ( value of the individuals in spring was the largest, followed by autumn and winter, and finally summer; the trend of the increased with latitude, without an obvious trend for depth, which might be resulted from limited sample size. The fixed value of (2.77) was less than 3, indicating the negative allometric growth pattern of . The fatness was negatively related to length and tended towards a slender body. This study showed that season and latitude have significant influences on the length-weight relationships of . The mixed effect model could reflect the heterogeneity of regions and seasons in a single model more accurately and conveniently, which could be applied to more heterogeneity studies of fish life history traits. 参考文献 相似文献 引证文献

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

ObjectiveEmergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to...

Effects of different rootstocks on the weak light tolerance ability of summer black grape based on 4 photo-response models

The photo response curves of 11 rootstock-scion combinations including summer black/Beta, summer black/1103P, summer black/101-14, summer black/3309C, summer black/140Ru, summer black/5C, summer black/5BB, summer black/420A, summer black/SO4, summer black/Kangzhen No.1, summer black/Huapu No.1 were fitted by rectangular hyperbola mo-del, non-rectangular hyperbola model, modified rectangular hyperbola model and exponential model respectively, and the differences of imitative effects were analyzed by determination coefficiency, light compensation point, light saturation point, initial quantum efficiency, maximum photosynthetic rate and dark respiration rate. The result showed that the fit coefficients of all four models were above 0.98, and there was no obvious difference on the fitted values of light compensation point among the four models. The modified rectangular hyperbola model fitted best on light saturation point, apparent quantum yield, maximum photosynthetic rate and dark respiration rate, and had the minimum AIC value based on the akaike information criterion, therefore, the modified rectangular hyperbola model was the best one. The clustering analysis indicated that summer black/SO4 and summer black/420A combinations had low light compensation point, high apparent quantum yield and low dark respiration rate among 11 rootstock-scion combinations, suggesting that these two combinations could use weak light more efficiently due to their less respiratory consumption and higher weak light tolerance. The Topsis comparison method ranked summer black/SO4 and summer black/420A combinations as No. 1 and No. 2 respectively in weak light tolerance ability, which was consistent with cluster analysis. Consequently, summer black has the highest weak light tolerance in case grafted on 420A or SO4, which could be the most suitable rootstock-scion combinations for protected cultivation.

A proposal of Fourier-Bessel expansion with optimized ensembles of bases to analyse two dimensional image.

It is a critical issue to find the best set of fitting function bases in mode structural analysis of two dimensional images like plasma emission profiles. The paper proposes a method to optimize a set of the bases in the case of Fourier-Bessel function series, using their orthonormal property, for more efficient and precise analysis. The method is applied on a tomography image of plasma emission obtained with the Maximum-likelihood expectation maximization method in a linear cylindrical device. The result demonstrates the excellency of the method that realizes the smaller residual error and minimum Akaike information criterion using smaller number of fitting function bases.

Validation of kinetic modeling of progesterone release from polymeric membranes

Mathematical modeling in drug release systems is fundamental in development and optimization of these systems, since it allows to predict drug release rates and to elucidate the physical transport mechanisms involved. In this paper we validate a novel mathematical model that describes progesterone (Prg) controlled release from poly-3-hydroxybutyric acid (PHB) membranes. A statistical analysis was conducted to compare the fitting of our model with six different models and the Akaike information criterion (AIC) was used to find the equation with best-fit. A simple relation between mass and drug released rate was found, which allows predicting the effect of Prg loads on the release behavior. Our proposed model was the one with minimum AIC value, and therefore it was the one that statistically fitted better the experimental data obtained for all the Prg loads tested. Furthermore, the initial release rate was calculated and therefore, the interface mass transfer coefficient estimated and the equilibrium distribution constant of Prg between the PHB and the release medium was also determined. The results lead us to conclude that our proposed model is the one which best fits the experimental data and can be successfully used to describe Prg drug release in PHB membranes.

Modeling the Behavior of Inflation Rate in Albania Using Time Series

In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).

Forecasting prices of coffee seeds using Vector Autoregressive Time Series Model

Forecasts of agricultural prices are useful to the farmers, policymakers and agribusiness industries. In this globalized world, management of food security in the developing countries like India where agriculture is dominated needs efficient and reliable price forecasting models. In the present study, Vector Autoregression (VAR) has been applied for modeling and forecasting of monthly wholesale price of clean coffee seeds in different coffee consuming centers, viz. Bengaluru, Chennai and Hyderabad. Augmented Dickey-Fuller (ADF) test has been used for testing the stationarity of the time series. The appropriate VAR model is selected based on minimum Akaike Information Criterion (AIC). The VAR model obtained is compared with the Auto Regressive Integrated Moving Average (ARIMA) models with respect to forecast accuracy measures. The residuals of the fitted models were diagnosed for possible presence of autocorrelation and Autoregressive Conditional Heteroscedasticity (ARCH) effects.

Forecasting by Stochastic Models to Inflow of Karkheh Dam at Iran

Forecasting the inflow of rivers to reservoirs of dams has high importance and complexity. Design and optimal operation of the dams is essential. Mathematical and analytical methods use for understanding estimating and prediction of inflow to reservoirs in the future. Various methods including stochastic models can be used as a management tool to predict future values of these systems. In this study stochastic models (ARIMA) are applied to records of mean annual flow Karkheh river entrance to Karkheh dam in the west of Iran. For this purpose we collected annual flow during the period from 1958/1959 to 2005/2006 in Jelogir Majin hydrometric station. The available data consists of 48 years of mean Annual discharge. Three types of ARIMA (p, d, q) models (0, 1, 1), (1, 1, 1) and (4, 1, 1) suggested, and the selected model is the one which give minimum Akaike Information Criterion (AIC). The Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods are used to estimate the model parameters. It is found that the model which corresponds to the minimum AIC is the (4, 1, 1) model in CLS estimation method. Port Manteau Lack of fit test and Residual Autocorrelation Function (RACF) test are applied as diagnostic checking. Forecasting of annual inflow for the period from 2006 to 2015 are compared with observed inflow for the same period and since agreement is very good adequacy of the selected model is confirmed.

ANALISIS KAUSALITAS ANTARA PENDAPATAN NASIONAL DENGAN SUKU BUNGA MENGGUNAKAN METODE FINAL PREDICTION ERROR (FPE) TAHUN 1997.1 - 2003.4

The objective of this research is to verify if the national income variable influences interest rate, or vice versa, the interest rate influences national income. The research is conducted by using causality test of Final Prediction Error which is method for determining the optimal length in the way minimizing FPE.Result of the test by using FPE showed that there is one datum showing GNP stationer variable demonstrating to Mackinnon critical values 5%, the best testing model is model which having the minimum AIC, and also on interest rate of bank of Indonesia (SBI) there is one datum of stationer showing that SBI stationer on Mackinnon critical values 5% demonstrating model 3 stationer which has minimal AIC value. The conclusion acquired from this research is that GNP causes SBI but SBI does not cause GNP because GNP variable is influenced by the previous time.

Evaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow Forecasting

Reservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for forecasting monthly Koga River flow with Gnu Regression, Econometrics and Time-series Library (GRETL) software. The stationarity of historical river flow sequence was checked by Augmented Dickey-Fuller (ADF) unit root analysis. Then, seasonality was removed from the river flow time series by seasonal differencing. Using seasonally differenced correlogram characteristics various SARIMA models were identified and evaluated, their parameters were optimized and diagnostic checks of forecasts were performed using residual correlograms and Ljung-Box tests. Finally, based on minimum Akaike Information criteria, SARIMA (1, 0, 1) (3, 1, 3)12 model was selected for Koga River flow forecasting. The stationarity test of the forecasted values of this model has proved the similarity of forecast values and patterns with those of the historical ones. Thus, irrigation managers could use this model and forecast information for optimal irrigation planning and development of reservoir operation strategies in order to protect farmers and downstream environment from water shortages. Moreover, the use of stationarity test of forecast flow patterns is useful and applicable in selecting best forecast model during forecasting of any river flows.