An agricultural price forecasting model under nonstationarity using functional coefficient autoregression

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 more than ever. Forecasts of agricultural prices are handy to the policymakers, agribusiness industries and farmers. In the present study, Functional Coefficient Autoregression (FCAR) has been applied for modeling and forecasting the monthly wholesale price of clean coffee seeds in Hyderabad coffee consuming center using the data from Jan, 2001 to Sep, 2014. FCAR (2,2) model was found suitable based on the minimum Average Prediction Error (APE) criterion. The FCAR model thus obtained was compared with the Autoregressive Integrated Moving Average (ARIMA) model. Since the original series was found to be nonstationary from Augmented Dickey-Fuller test (ADF statistic=-2.84, p=0.22), the differenced series (ADF statistic=-4.20, p<0.01) was used and ARIMA (12,1,0) was found suitable. The FCAR model obtained was compared with the ARIMA model with respect to forecast accuracy measures viz., Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RMSE and MAPE for the FCAR (2,2) were found to be 17.16 and 4.41%, respectively, whereas for the ARIMA (12,1,0) models, 62.64 and 26.15%, respectively. The results indicated that the FCAR model was efficient than the ARIMA model in forecasting the future prices

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Not AvailableThis study explores the interval-valued data analysis techniques to witness the spatial disparity in the wage rates of farm labourers in India. Farm labourers constitute more than half of the total workforce engaged in Indian agriculture. Also, farmers’ expenses towards labour charges account for more than 50 per cent of the total variable cost of production for most crops.Using the time series data on the nominal farm wage rates paid at different agriculturally important states, the interval-valued series are built. The inflation-adjusted real wage rates are found and both nominal and real wage rate data are used to find the average range of the farm wage rates over the agricultural years for a decade. Using the time series analysis techniques, viz. autoregressive integrated moving average–artificial neural network (ARIMA-ANN) hybrid model and vector autoregressive moving average (VARMA) model, the interval-valued data on nominal wage rates are modelled and the best model for forecasting is identified using forecast evaluation methods. The results established the presence of spatial disparity and the forecasts indicated that this disparity is not going to narrow down in future unless some policy intervention takes place.Not Availabl

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Not AvailableThis study explores the interval-valued data analysis techniques to witness the spatial disparity in the wage rates of farm labourers in India. Farm labourers constitute more than half of the total workforce engaged in Indian agriculture. Also, farmers' expenses towards labour charges account for more than 50 per cent of the total variable cost of production for most crops. Using the time series data on the nominal farm wage rates paid at different agriculturally important states, the interval-valued series are built. The inflation-adjusted real wage rates are found and both nominal and real wage rate data are used to find the average range of the farm wage rates over the agricultural years for a decade. Using the time series analysis techniques, viz. autoregressive integrated moving average-artificial neural network (ARIMA-ANN) hybrid model and vector autoregressive moving average (VARMA) model, the interval-valued data on nominal wage rates are modelled and the best model for forecasting is identified using forecast evaluation methods. The results established the presence of spatial disparity and the forecasts indicated that this disparity is not going to narrow down in future unless some policy intervention takes place.Not Availabl

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Not AvailableForecasting is one of the core focuses of statisticians working in agricultural research. Obtaining timely as well as accurate forecasts under all possible circumstances is the need of the hour. Most of the forecasting techniques make one or the other assumptions limiting their applications. Vector Autoregression is one such widely used multivariate forecasting technique where homoscedasticity of errors is assumed for estimation of parameters by ordinary least square (OLS) method. This study proposes genetic algorithm (GA), a heuristic search algorithm, which does not make any such assumptions for estimating the parameters under such situation. The developed methodology is empirically validated using simulated bivariate vector autoregressive model of order 1 under heteroscedasticity. The relative error of parameter estimates and Mean Absolute Percentage Error have shown that GA performs better than OLS estimation under heteroscedasticity. The proposed methodology is also tested under homoscedasticity using bivariate data of fish landings. The results indicated that both GA and OLS are equally efficient in estimating the parameters.Not Availabl

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Not AvailableForecasting is one of the core focuses of statisticians working in agricultural research. Obtaining timely as well as accurate forecasts under all possible circumstances is the need of the hour. Most of the forecasting techniques make one or the other assumptions limiting their applications. Vector Autoregression is one such widely used multivariate forecasting technique where homoscedasticity of errors is assumed for estimation of parameters by ordinary least square (OLS) method. This study proposes genetic algorithm (GA), a heuristic search algorithm, which does not make any such assumptions for estimating the parameters under such situation. The developed methodology is empirically validated using simulated bivariate vector autoregressive model of order 1 under heteroscedasticity. The relative error of parameter estimates and Mean Absolute Percentage Error have shown that GA performs better than OLS estimation under heteroscedasticity. The proposed methodology is also tested under homoscedasticity using bivariate data of fish landings. The results indicated that both GA and OLS are equally efficient in estimating the parameters.Not Availabl

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Not AvailableIn this globalized world, management of food security in the developing countries like India where agriculture is dominated needs efficient and reliable price forecasting models more than ever. Forecasts of agricultural prices are handy to the policymakers, agribusiness industries and farmers. In the present study, Functional Coefficient Autoregression (FCAR) has been applied for modeling and forecasting the monthly wholesale price of clean coffee seeds in Hyderabad coffee consuming center using the data from Jan, 2001 to Sep, 2014. FCAR (2,2) model was found suitable based on the minimum Average Prediction Error (APE) criterion. The FCAR model thus obtained was compared with the Autoregressive Integrated Moving Average (ARIMA) model. Since the original series was found to be nonstationary from Augmented Dickey-Fuller test (ADF statistic=-2.84, p=0.22), the differenced series (ADF statistic=-4.20, p<0.01) was used and ARIMA (12,1,0) was found suitable. The FCAR model obtained was compared with the ARIMA model with respect to forecast accuracy measures viz., Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RMSE and MAPE for the FCAR (2,2) were found to be 17.16 and 4.41%, respectively, whereas for the ARIMA (12,1,0) models, 62.64 and 26.15%, respectively. The results indicated that the FCAR model was efficient than the ARIMA model in forecasting the future prices.Not Availabl

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Not AvailableForecasts 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.Not Availabl

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Not AvailableForecasts 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.Not Availabl

Asian Journal of Agricultural Extension, Economics & Sociology

Not AvailableBuilding an effective online course requires an understanding of learning analytics. The study assumes significance in the COVID 19 pandemic situation as there is a sudden surge in online courses. Analysis of the online course using the data generated from the Moodle Learning Management System (LMS), Google Forms and Google Analytics was carried out to understand the tenants of an effective online course. About 515 learners participated in the initial pre-training needs & expectations? survey and 472 learners gave feedback at the end, apart from the real-time data generated from LMS and Google Analytics during the course period. This case study analysed online learning behaviour and the supporting learning environment and suggest critical factors to be at the centre stage in the design and development of online courses; leads to the improved online learning experience and thus the quality of education. User needs, quality of resources and effectiveness of online courses are equally important in taking further online courses