Theil Inequality Coefficient Research Articles

시계열모형을 이용한 굴 생산량 예측 가능성에 관한 연구

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

Searching for a better proxy for business cycles: with supports using US data

This study revises the original Current Depth of Recession (CDR) to prove that the Modified CDR (MCDR) is more suitable as a threshold variable than the CDR. We rebuild the CDR indicator and adjust its positive and negative ranges with the estimation results of the Threshold Autoregressive (TAR) model. We construct two TAR models utilizing CDR and MCDR as threshold variables, respectively. Estimation and test results of the root mean square error, Theil's inequality coefficient and the Diebold-Mariano (DM, 1995) test suggest that MCDR performs better as a threshold variable than CDR.

Housing Starts Forecast of Retail Sales through the 2007-2009 Recession

The expansion following the 2001 recession was in part stimulated by a boom in housing market investments. Many economists were concerned that a severe drop in residential investments would cause a recession throughout the economy because of residential investment's relationship to the gross domestic product and financial markets, and that a decline in housing prices would negatively impact consumer spending. A severe decline in the housing market and the illiquidity in the financial market were both evident by the third quarter of 2007. Some models based on historical data were showing the certainty of a recession in 2007. The best predictors of recessions are respectively interest rate spread, unemployment claims, and building permits. Building permits/new private housing units are a leading economic indicator whereas retail sales are a component of manufacturing and trade sales, which is a coincident economic indicator because it is highly correlated to GDP. This study attempts to use housing starts and past values of retail sales to forecast out-of-sample retail sales values through the period of January 1998 to August 2010, which is inclusive of the recent 2007-2009 recession. If housing Starts are found to be predictive of retail sales, then they are a predictor of a coincident indicator of recessions. This forecast is done in the framework of neural network modeling referenced to robust regression analysis. It was expected that the neural network models would produce better forecasts as ascertained by correlation, root mean square error, and Theil inequality coefficient performance metrics, but the overall result was mixed.

Health status convergence at the local level: empirical evidence from Austria

IntroductionHealth is an important dimension of welfare comparisons across individuals, regions and states. Particularly from a long-term perspective, within-country convergence of the health status has rarely been investigated by applying methods well established in other scientific fields. In the following paper we study the relation between initial levels of the health status and its improvement at the local community level in Austria in the time period 1969-2004.MethodsWe use age standardized mortality rates from 2381 Austrian communities as an indicator for the health status and analyze the convergence/divergence of overall mortality for (i) the whole population, (ii) females, (iii) males and (iv) the gender mortality gap. Convergence/Divergence is studied by applying different concepts of cross-regional inequality (weighted standard deviation, coefficient of variation, Theil-Coefficient of inequality). Various econometric techniques (weighted OLS, Quantile Regression, Kendall's Rank Concordance) are used to test for absolute and conditional beta-convergence in mortality.ResultsRegarding sigma-convergence, we find rather mixed results. While the weighted standard deviation indicates an increase in equality for all four variables, the picture appears less clear when correcting for the decreasing mean in the distribution. However, we find highly significant coefficients for absolute and conditional beta-convergence between the periods. While these results are confirmed by several robustness tests, we also find evidence for the existence of convergence clubs.ConclusionsThe highly significant beta-convergence across communities might be caused by (i) the efforts to harmonize and centralize the health policy at the federal level in Austria since the 1970s, (ii) the diminishing returns of the input factors in the health production function, which might lead to convergence, as the general conditions (e.g. income, education etc.) improve over time, and (iii) the mobility of people across regions, as people tend to move to regions/communities which exhibit more favorable living conditions.JEL classification: I10, I12, I18

Dynamic modeling of partial nitrification in a rotating disk biofilm reactor: Calibration, validation and simulation

Partial nitrification to nitrite saves oxygen and organic matter. A dynamic model of a rotating disk biofilm reactor was developed in order to determine optimal operation conditions for partial nitrification. The biofilm model considered the active biomass fraction as a state variable; it was calibrated at steady state using the adjustment of two parameters – μ max, AOB (1.32 d −1) and k L (0.44 m h −1) and validated with long-term experiments. The experimental data were attained running the reactor in continuously and in batch modes for more than 700 days at different pH values, oxygen concentrations and nitrogen load rates (NLRs). A good agreement between the measured and modeled results was obtained with a Theil inequality coefficient lower than 0.3 for both calibration and validation. The model predicts that the active biomass fraction does not reach steady-state before 40 operating days, and the active nitrite-oxidizing bacteria fraction is much more sensitive than the ammonia-oxidizing bacteria fraction to the pH. Results demonstrated that nitrite accumulation ( β) is strongly influenced by the NLR and the pH. The optimal operation zone for β > 70% is reached for NLRs between 8.5 and 11.5 g N m −2 d −1, pH > 8 and dissolved oxygen concentration <3 mg O 2 L −1.

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg–Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN’s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

How predictable are prices of agricultural commodities? The possibilities and constraints of forecasting wheat prices

Wheat price forecasts are very important for traders, farmers and politicians as well. However, only quite accurate price predictions can guide these groups towards making the best decisions. Therefore the well-known wheat price projections of both the OECD and the FAPRI from 1996 on are tested for their predictive accuracy using Theil's inequality coefficient. Despite the fact that both models could not foresee the price peak which occurred in February 2008, their predictions offer more accurate values than a naive prediction of no price change. Nevertheless, precise price forecasts cannot be expected by the models of the OECD and the FAPRI since some short-run effects such as inappropriate weather are not predictable. Thus, our own econometric model is developed taking the previous price development, the stocks-to-use-ratio and the crude oil price into account. In comparison to the projections of both institutions the model, with rather simple assumptions, was able to generate forecasts more accurately. In a simulation study which takes different crude oil price levels and stochastic effects of the world wheat consumption and the average yields per hectare into account, the possible wheat price range is shown as large. Therefore, price predictions can only inform about general longrun trends.

Performing stock price prediction use of hybrid model

PurposeThe purpose of this paper is to propose an analysis method based on a hybrid model, which combines principal component regression (PCR) model and general regression neural network (GRNN) to solve both multicollinearity problems and non‐linear problems at the same time.Design/methodology/approachFirst, the financial ratio data of companies with stocks listed in regular stock market and over‐the‐counter stock market in Taiwan and Mainland China are collected and used as sample data. Grey relational analysis is used to rank the enterprises' operation performance, and the enterprises in Taiwan and Mainland China with business operation performance in the first place are selected and their stock information collected to perform the prediction of stock closing price.FindingsFive indices such as the root mean square error, revision Theil inequality coefficient, mean absolute error, mean absolute percentage error and coefficient of efficiency of the test result are calculated; the empirical results show that the prediction power of the hybrid model of PCR+genetic algorithm general regression neural network is obviously better than the model of PCR, GRNN and PCR+GRNN.Originality/valueThe paper adopts a hybrid model and parameter adjustment to increase prediction capability.

A Comparison of Univariate Time Series Methods for Forecasting Cocoa Bean Prices

The purpose of this study was to compare the forecasting performances of different time series methods for forecasting cocoa bean prices. The monthly average data of Tawau cocoa bean prices graded SMC 1B for the period of January 1992-December 2006 was used. Tawau is one of the top cocoa producers in the world along with the Ivory Coast, Ghana and Indonesia. Four different types of univariate time series methods or models were compared, namely the exponential smoothing, autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) and the mixed ARIMA/GARCH models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Theil's inequality coefficient (U-STATISTICS) were used as the selection criteria to determine the best forecasting model. This study revealed that the time series data were influenced by a positive linear trend factor while a regression test result showed the non-existence of seasonal factors. Moreover, the Autocorrelation function (ACF) and the Augmented Dickey-Fuller (ADF) tests have shown that the time series data was not stationary but became stationary after the first order of the differentiating process was carried out. Based on the results of the ex-post forecasting (starting from January until December 2006), the mixed ARIMA/GARCH model outperformed the exponential smoothing, ARIMA and GARCH models.

Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces

This article proposes three novel methods—temporal confined (TC), spatiotemporal confined (STC) and spatial confined (SC)—to forecast the temporal evolution of traffic parameters. The fundamental rationales are to embed one-dimensional traffic time series into reconstructed state spaces and then to perform fuzzy reasoning to infer the future changes in traffic series. The TC, STC and SC methods respectively employ different fuzzy reasoning logics to select similar historical traffic trajectories. Theil inequality coefficient and its decomposed components are used to evaluate the predicting power and source of errors. Field observed one-minute traffic counts are used to test the predicting power. The results show that overall prediction accuracies for the three methods are satisfactorily high with small systematic errors and little deviation from the observed data. It suggests that the proposed three methods can be used to capture and forecast the short-term (e.g., one-minute) temporal evolution of traffic parameters.

International trends in forest products consumption: is there convergence?

SUMMARY International data from 1961 to 2005 showed that the coefficient of variation of consumption per capita across countries had tended to decrease over time for all forest products except sawnwood. This convergence of per-capita consumption was confirmed by the trends in Theil's inequality coefficients: the distribution of forest products consumption across countries had become more similar to the distribution of population. The rate of convergence had tended to accelerate during the last 10 years of observation: countries had become more similar in their use of all forest products except sawnwood. The rate of convergence was most rapid for fiberboard, veneer and plywood, and paper and paper board excluding newsprint and other printing and writing paper. The convergence of per-capita consumption of forest products stemmed in large part from lower rates of growth of consumption at higher levels of per-capita consumption, and not from a convergence of per-capita income.

Performing stock closing price prediction through the use of principle component regression in association with general regression neural network

Many problems existed in conventional Multiple Regression model constantly bother the researchers. These problems include multicollinearity problem and nonlinear problem among independent variables, which lead to the difficulty of accurate prediction of many problems. Therefore, in this article, an analysis method based on hybrid model is proposed, which combines Principle Component Regression (PCR) model and General Regression Neural Network (GRNN) to solve these two problems at the same time. First, in this article, the financial ratio data of companies with stocks listed in regular stock market and over-the-counter stock market in Taiwan and Mainland China are collected and used as sample data; moreover, Grey Relational Analysis is used to rank enterprise’s operation performance, and the enterprises in Taiwan and Mainland China with business operation performance in the first place are selected and their stock information collected to perform the prediction of stock closing price. In this article, the stock information are divided into training data and test data to perform model construction and verification; meanwhile, five indexes such as the Root Mean Squared Error (RMSE), Revision Theil Inequality Coefficient (RTIC), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Efficiency (CE) of the test result are calculated; the empirical results show that the prediction power of the hybrid model of PCR+GAGRNN is obviously better than the model of PCR, GRNN and PCR+GRNN.

Sanitary felling of Norway spruce due to spruce bark beetles in Slovenia: A model and projections for various climate change scenarios

A model is presented to predict sanitary felling of Norway spruce ( Picea abies) due to spruce bark beetles ( Ips typographus, Pityogenes chalcographus) in Slovenia according to different climate change scenarios. The model incorporates 21 variables that are directly or indirectly related to the dependent variable, and that can be arranged into five groups: climate, forest, landscape, topography, and soil. The soil properties are represented by 8 variables, 4 variables define the topography, 4 describe the climate, 4 define the landscape, and one additional variable provides the quantity of Norway spruce present in the model cell. The model was developed using the M5′ model tree. The basic spatial unit of the model is 1 km 2, and the time resolution is 1 year. The model evaluation was performed by three different measures: (1) the correlation coefficient (51.9%), (2) the Theil's inequality coefficient (0.49) and (3) the modelling efficiency (0.32). Validation of the model was carried out by 10-fold cross-validation. The model tree consists of 28 linear models, and model was calculated for three different climate change scenarios extending over a period until 2100, in 10-year intervals. The model is valid for the entire area of Slovenia; however, climate change projections were made only for the Maribor region (596 km 2). The model assumes that relationships among the incorporated factors will remain unchanged under climate change, and the influence of humans was not taken into account. The structure of the model reveals the great importance of landscape variables, which proved to be positively correlated with the dependent variable. Variables that describe the water regime in the model cell were also highly correlated with the dependent variable, with evapotranspiration and parent material being of particular importance. The results of the model support the hypothesis that bark beetles do greater damage to Norway spruce artificially planted out of its native range in Slovenia, i.e., lowlands and soils rich in N, P, and K. The model calculation for climate change scenarios in the Maribor region shows an increase in sanitary felling of Norway spruce due to spruce bark beetles, for all scenarios. The model provides a path towards better understanding of the complex ecological interactions involved in bark beetle outbreaks. Potential application of the results in forest management and planning is discussed.

Tests of currency substitution, capital mobility and nonlinearity of Hungary's money demand function

The demand for M2 in Hungary is positively associated with real output and the nominal effective exchange rate and negatively influenced by the deposit rate, the euro interest rate, and expected inflation rate. The coefficient of the euro interest rate for the demand for M1 is insignificant. Hence, depreciation of the forint or a higher euro interest rate would help raise Hungary's real output. The Box–Cox transformation shows that the log-linear form for M1 or M2 demand cannot be rejected at the 5% level while the linear form for M1 or M2 demand can be rejected at the 5% level. However, the log-linear form for M2 demand can be rejected at the 10% level. The CUSUM and CUSUMSQ tests show that M2 demand based on the Box–Cox model and M1 demand are relatively stable. Based on the mean absolute percent error and the Theil inequality coefficient, M2 demand based on the Box–Cox model yields better forecasting outcomes.

ARIMA model and forecasting with three types of pulse prices in Bangladesh: a case study

PurposeThe purpose of this paper is to generate three types of forecasts, namely, historical, ex‐post and ex‐ante, using the world famous Box‐Jenkins time series models for motor, mash and mung prices in Bangladesh.Design/methodology/approachThe models on the basis of which these forecasts have been computed were selected by six important information criteria such as Akaike's Information Criterion (AIC), Schwarz's Bayesian Information Criterion (BIC), Theil's R2, Theil's R2, SE(σ) and Mean Absolute Percent Errors (MAPEs). In order to examine the forecasting performance of the selected models, three types of forecast errors were estimated, i.e. root mean square percent errors (RMSPEs), mean percent forecast errors (MPFEs) and Theil's inequality coefficients (TICs).FindingsThe estimates suggest that in most cases the forecasting performances of the models in question are quite satisfactory.Originality/valueThe models developed in this paper can be used for policy purposes as far as price forecasts of the commodities are concerned.

A conditional extreme value volatility estimator based on high-frequency returns

This paper introduces a conditional extreme value volatility estimator (EVT) based on high-frequency returns. The relative performance of the EVT is compared with the discrete-time GARCH and implied volatility models for 1-day and 20-day-ahead forecasts of realized volatility. This is also a first attempt towards detecting any time-series variation in extreme value distributions using high-frequency intraday data. The information content of EVT is examined in the context of forecasting S&P 100 index volatility. Adjusted- R 2 values imply superior performance of the implied volatility index (VIX) and EVT in capturing time-series variation in realized volatility. The forecasting ability of various discrete-time GARCH models turns out to be inferior to VIX and EVT. According to the Theil inequality coefficient and the heteroscedasticity-adjusted root mean squared and mean absolute errors, (1) EVT provides more accurate forecasts than the VIX and GARCH volatility models; (2) VIX generally yields a less accurate characterization of realized volatility than EVT and GARCH models. These results have implications for financial risk management, and are thus relevant to both regulators and practitioners.

Empirical validation of a queueing approach to uninterrupted traffic flows

In this paper, the use of queueing theory for modeling uninterrupted traffic flows is evaluated. Empirical data on speeds and flows are used to evaluate speeds generated by the different queueing models. Using the Theil inequality coefficient as evaluation criterion, the speeds generated by the queueing models are compared to the empirical speeds. Queueing models that best fit the observed speeds are obtained. It appears that traffic flow on a highway during non-congested hours is best described using a M/G/1 queueing model. During the congested hours however, the state dependent queueing GI/G/z models are more realistic. Because the queueing models describe the empirical data well, they can also be used to evaluate potential improvements in existing traffic conditions.

Count Data Models for Trip Generation

Count data models are established to overcome the shortcoming of linear regression model used for trip generation in conventional four step travel demand forecasting. It should be checked if there are overdispersion and excess zero responses in count data to forecast the generation of trips. The forecasted values should also be non-negative ones. The study applies to nonhome based trips at household level to perform efficient analysis on count data. The Poisson model with an assumption of equidispersion has frequently been used to analyze count data. However, if the variance of data is greater than the mean, the Poisson model tends to underestimate errors, resulting in problem in reliability. Excess zeros in data result in heterogeneity leading to biased coefficient estimates for the models. The negative binomial model and the modified count data models are established to consider overdispersion and heterogeneity to improve the reliability. The optimal model is chosen through Vuong test. Model reliability is also checked by likelihood test and accuracy of estimated value of model by Theil inequality coefficient. Finally, sensitivity analysis is performed to know the change of nonhome based trips depending on the change in socio-economic characteristics.

Türkiyede Enflasyon Tahmin Hedeflemesi: Sabit Ve Değişken Katsayılı Markov-Değişimli Modellerin Karşılaştırması

The purpose of this study is to estimate two different inflation models which can be chosen for inflation targeting process: constant coefficient model and time-varying Markov switching variance model. Our results show that time-varying Markov switching variance model performs better than constant coefficient model according to the calculated Theil Inequality Coefficient.

Forecasting tourism demand in asian-pacific countries

This article scrutinizes issues relating to the forecasting of international tourist arrivals. The area of interest is Asian-Pacific countries. Six forecasting techniques are examined. The results show that the accuracy of the forecasts differs depending on the country being forecast, but that the seasonal-nonseasonal ARIMA model is overall the most accurate method for forecasting international tourist arrivals. This research also finds, that, judging from the value of Theil inequality coefficient, seasonal-nonseasonal ARIMA is superior to any other technique examined in this article. Finally, the issue of policy implication is also addressed.