7,376 research outputs found

    Comparison of modelling techniques for milk-production forecasting

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    peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≀12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

    Time series forecasting with the WARIMAX-GARCH method

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    It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting

    Exploring the trend of New Zealand housing prices to support sustainable development

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    The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

    Volatility Model for Financial Market Risk Management : An Analysis on JSX Index Return Covariance Matrix

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    In measuring risk, practitioners have practiced one of the two extreme approaches for so long, i.e. historical simulation or risk metrics. Meanwhile, academicians tend to apply methods based on the latest development in financial econometrics. In this study, we try to assess one of important issues in financial econometric development that focuses on market risk measurement and management employing asset-based models, i.e. models that apply dimensional covariance matrix, which is relevant to practice world. We compare covariance matrix model with Exponential Smoothing Model and GARCH Derivation and the Associated Derivation Models, using JSX Stock price Index data in 2000-2005. The result of this study shows how applicable the observed financial econometric instrument in Financial Market Risk Management practice.Risk Management, Volatility Model

    Terms of Trade and Supply Response of Indian Agriculture: Analysis in Cointegration Framework.

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    In this paper, we examine the presence of stochastic trend (unit root) and structural break in various agriculture-industry terms of trade series in India. The results suggest that underlying data generating process of terms of trade are most likely non-stationary. We subsequently re-examine the aggregate supply response of Indian agriculture in this light. We investigate the presence of long-run functional relationship(s) underlying the supply response model through cointegration analysis and error correction framework. The multivariate results indicate presence of a cointegrating relationship in the supply response model. The vector error correction estimates suggest that short-run output adjustments are not related to changes in agricultural terms of trade in a temporal causal relationship. However, the short-run deviations in terms of trade from its long-term level create error-correction in the long-term output adjustments through changes in technology (irrigation). This may imply that agricultural growth can respond better if price incentives are combined with investments in irrigation.domestic terms of trade, agricultural supply response, unit root, cointegration

    A comparison of statistical models for short categorical or ordinal time series with applications in ecology

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    We study two statistical models for short-length categorical (or ordinal) time series. The first one is a regression model based on generalized linear model. The second one is a parametrized Markovian model, particularizing the discrete autoregressive model to the case of categorical data. These models are used to analyze two data-sets: annual larch cone production and weekly planktonic abundance.Comment: 18 page

    Unemployment, Hysterisis and Transition

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    We quantify the degree of persistence in the unemployment rates of transition countries using a variety of methods benchmarked against the EU. In doing so, we will also characterize the dynamic behavior of unemployment in the CEECs during the past decade. In part of the paper, we will work with the concept of linear ÒHysteresisÓ as described by the presence of unit roots in unemployment as in most empirical research on this area. Given that this is potentially a rather narrow definition, we will also take into account the existence of structural breaks and non-linear dynamics in unemployment in order to allow for a richer set of dynamics. Finally, we examine whether CEECsÕ unemployment presents features of multiple equilibria, that is, if it remains locked into a new level whenever a structural change occurs. Our findings show that, in general, we can reject the unit root hypothesis after controlling for structural changes and business cycle effects, but we can observe the presence of a high and low unemployment equilibria. The speed of adjustment is faster for CEECs than the EU, although CEECs tend to move more frequently between equilibria.unemployment, hysterisis, unit root, transition
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