​Changes in Prices and Arrivals of Major Oilseed Crops in APMC Amreli (Gujarat): An Empirical Analysis

The price volatility has been the main centre of attention for policy planners. This study therefore, aims to examine the changes in price and arrivals of major oilseeds of APMC, Amreli (Gujarat) analyzing monthly time series data of last twenty years. The findings emerged from the study revealed that the month wise and year wise highest changes were observed for the groundnut (semi spreading), followed by sesamum (white). Month wise severe changes were observed in the price of sesamum (white), while year wise severe changes were observed for the sesamum (black). On the basis of adjusted R2, price model of semi-spreading groundnut was found to be the best fit among all the models.

Evaluation of gridded near surface air temperature datasets across complex terrain

<p>Near surface air temperature is a key variable used in wide range of applications showing environmental conditions across the earth. Standard meteorological observations generally provide the best estimation with high accuracy over time for a small area of influence. However, considerable uncertainty arises when point measurements are extrapolated or interpolated over much larger areas. Satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. Thus, spatial patterns of air temperature can be derived from satellite remote sensing.</p><p>In this study, we evaluate the performance of several satellite-based products of near surface air temperature to determine the best product in estimating daily and monthly air temperatures. Era5 Land, SMAP Level 4, AgERA5, MERRA2 products are used with 1120 ground-based gauge stations for the period 2015-2019 over complex terrain and different climate classes according to Köppen-Geiger climate classification in Turkey. Moreover, several traditional and more sophisticated machine learning downscaling algorithms are applied to increase products’ spatial resolution. The agreement between ground observations and the different products and the downscaled temperature product is investigated using a set of commonly used statistical estimators of mean absolute error (MAE), correlation coefficient (CC), root-mean-square error (RMSE) and bias.</p><p>Performance analysis of satellite-based air temperature products with ground-based observations on monthly time series has shown that ERA5 Land and SMAP L4 products have similar capabilities. However, analysis on daily time series depicted that ERA5 Land is superior to SMAP L4 product. Results indicate that bicubic interpolation performs best on downscaling Era5 Land product daily time series. However, Random Forest algorithm is superior on monthly time series.</p>

Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung

There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis. This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.

Determinant Of Sharia Bank Indonesia Rate With Financing And Deposit In Indonesian Islamic Banking Asset

This study aims to analyze the effect of bank certificates of Indonesia sharia (SBIS), financing to deposit ratio (FDR), and non-performing financing (NPF) against assets of Islamic Banking in Indonesia. The data used in this study are monthly time series data from the period from 2009 until 2013, published by Bank Indonesia of Indonesian Financial Statistics Report. The method of analysis used in this study is the Ordinary Least Square (OLS). The results of this study indicate that the variable certificates Indonesia sharia banks ((5.296593 and 0.0000), and financing to deposit ratio (5.512164 and 0.0000) had significant positive influence on Islamic banking assets in Indonesia. While non-performing financing variables (15.78500 and 0.0000) had significant negative towards Islamic banking assets in Indonesia.

Forecasting Import Demand of Table Grapes: Empirical Evidence from Thailand

The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.

Measuring Social Unrest Using Media Reports

We present a new index of social unrest based on counts of relevant media reports. The index consists of individual monthly time series for 130 countries, available with almost no lag, and can be easily and transparently replicated. Spikes in the index identify major events, which correspond very closely to event timelines from external sources for four major regional waves of social unrest. We show that the cross-sectional distribution of the index can be simply and precisely characterized, and that social unrest is associated with a 3 percentage point increase in the frequency of social unrest domestically and a 1 percent increase in neighbors in the next six months. Despite this, social unrest is not a better predictor of future social unrest than the country average rate.

The Influence of Macroeconomics Indicators to The Growth of Sukuk in Indonesia

<p><em>The purpose of this study is to analyze the influence of macroeconomic on the growth of Islamic bonds (sukuk) in Indonesia period 2013.1-2016.12. This study uses Ordinary Least Square (OLS) method with a monthly time series starting from January 2013 until December 2016. The results of this study indicate that the Production Index (IP) and Bank Indonesia Syariah Certificate (BSBIS) variables positively and significantly affect the development of state sukuk issuance in Indonesia, then inflation negatively and significantly affects the development of state sukuk issuance, while the JII variable negatively and does not significantly affect the development of state sukuk issuance in Indonesia.</em></p>

WHICH VARIABLES PREDICT INDONESIA'S INFLATION?

We use an exhaustive list of Indonesia’s macroeconomic variables in a comparative analysis to determine which predictor variables are most important in forecasting Indonesia’s inflation rate. We use monthly time-series data for 30 macroeconomic variables. Using both in-sample and out-of-sample predictability evaluations, we report consistent evidence of inflation rate predictability using 11 out of 30 macroeconomic variables.