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

Spatio-Temporal Analysis of Agricultural Labour Wages Using Vector Error Correction Model: An Integrated Approach to Environment and Climate Change

This study attempted to explore the interactive relations among agricultural labour wage rates in five neighbouring Indian states viz., Andhra Pradesh, Karnataka, Tamilnadu Telangana and Chhattisgarh using monthly time series data of 2005-2020. The objective of this study was to examine the degree of integration among wage rates of agricultural labourers in neighbouring states. Integration with outside markets may partly mitigate the costs of climate change, as individuals respond to warming temperature by migrating to urban areas and internationally in search of employment. We built vector error correction model (VECM) by conducting stationarity test and cointegration test. The Granger Causality test was employed to check whether the wage rates among different states influence each other. For building the VEC Model, the complete data set (180 data points) was split into training (168 data points) and testing (12 data points) data sets. The nonstationarity of the data was established by the Augmented Dickey Fuller test. For the purpose of forecasting, VECM (1) was built and tested for goodness of fit using Mean Absolute Percentage Error (MAPE) which were found to be &lt; 10% for all the states suggesting good fit of the VECM model. A growing body of literature suggests that the economic costs of climate change may be substantial and far‐reaching, impacting agriculture, mortality, labour productivity, economic growth, civil conflict and migration

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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