Forecasting Coarse Rice Prices in Bangladesh
نویسندگان
چکیده
منابع مشابه
Forecasting UK stock prices
The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...
متن کاملStatistical Analysis of Crop-Weather Regression Model for Forecasting Production Impact of Aus Rice in Bangladesh
This study is an endeavor to evaluate statistically predicts a weather-crop yield-forecasting model to generate early crop production estimates. The weather-crop yield-forecasting model was applied to estimate prospective production of Aus rice in Jessore and Rajshahi districts of Bangladesh. This model is the relationship between the crop yield and input weather parameters influencing the crop...
متن کاملStructural Breaks, Automatic Model Selection and Forecasting Wheat and Rice Prices for Pakistan
Structural breaks and existence of outliers in time series variables results in misleading forecasts. We forecast wheat and rice prices by capturing the exogenous breaks and outliers using Automatic modeling. The procedure identifies the outliers as the observations with large residuals. The suggested model is compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage E...
متن کاملProduction Efficiency of Rice Growers in Dinajpur District of Bangladesh
To meet the growing food demand of Bangladesh requires efficiently use of inputs and effectively manage of production practices at the farm level. Thus, the present study aims to measure the technical efficiency and establish core factors affecting boro and aman rice production in Bangladesh. The study employed mainly farm level data collected from 80 farm households selected randomly in Dinajp...
متن کاملForecasting Iran’s Rice Imports during 2009-2013
In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Progressive Agriculture
سال: 2013
ISSN: 2310-2950,1017-8139
DOI: 10.3329/pa.v22i1-2.16480