scholarly journals INVESTMENTS IN AGRICULTURAL MACHINERY AND ITS EFFICIENCY IN UKRAINE

Ekonomika ◽  
2017 ◽  
Vol 96 (1) ◽  
pp. 113-130 ◽  
Author(s):  
Andriy Stavytskyy ◽  
Oleksandra Prokopenko
Keyword(s):  
Economic Effect ◽  
Custom Union ◽  
Agricultural Machinery ◽  
Trade Routes ◽  
Currency Exchange ◽  
Oil Crops ◽  
Currency Exchange Rate ◽  
Farm Mechanization ◽  
Fixed Prices ◽  
Crops Yield

One of the major conditions of effective agriculture production is sufficient farm mechanization. However, the unstable economic situation in Ukraine, combined with bureaucratic problems, an unstable currency exchange rate, and sharply changed trade routes (which has caused major losses to a number of farms and traders working with the Custom Union) created significant obstacles for investing in machinery in Ukraine. It is especially topical for small and medium farms that usually function in poor economic conditions without any adequate access to the credit market. Consequently, Ukrainian agriculture producers often have an inadequate mechanization rate. As a result, the productivity of Ukrainian farms is significantly lower as compared to other countries that have similar natural conditions in terms of temperatures, precipitation and quality of agricultural lands.A no less important problem is the lack of awareness of small and medium farms, which may not realize the effect that investment has in agriculture machinery. Thus, in order to provide specific numbers for potential investors and prove the efficiency of this fund placement, an expected direct economic effect from machinery investment (as an increased profit from higher yield) was estimated. The first step was to define those types of agricultural machinery that have significant impact on the yield and productivity levels for each of the most important crop types: grain, oil crops, vegetables, fruits, etc. Then, an impact of additional investment in various machinery means on crops yield was estimated. Finally, based on fixed prices and a discount rate, an expected additional profit generated by newly purchased machinery on an average farm was estimated. The model proved especially high profitability of investment in such machinery as ploughs, fertilizers spreaders, harvesters, tractors, and machines for irrigation – most of them are paid off (on a land parcel with area around 2000 ha) in three years or less.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

scholarly journals ANALISIS PENGARUH INFLASI, BI RATE, CURRENCY EXCHANGE (SEBAGAI VARIABEL INTERVENING) TERHADAP PEMBIAYAAN MURABAHAH PADA BANK SYARIAH MANDIRI TAHUN 2013-2018

2019 ◽  
Vol 3 (1) ◽  
pp. 1-30
Author(s):  
Rahmawati Rahmawati ◽  
Eko Budianto ◽  
Putri Sri Anita
Keyword(s):  
Significant Influence ◽  
Quantitative Research ◽  
Financial Statements ◽  
Quantitative Research Methods ◽  
Independent Variables ◽  
Is Research ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Currency Exchange Rate ◽  
Testing Data

The title of this research is: "Analysis of the Influence of Inflation, Currency Exchange Rates and BI Rate on Murabahah Financing at Bank Syariah Mandiri (BSM) in 2013-2018." The formulation of the problems in this study are: 1) What is the effect of inflation on murabaha financing in Bank Mandiri Syariah (BSM) in 2013-2018? ?, 3) What is the effect of the BI Rate on murabaha financing at Bank Mandiri Syariah (BSM) in 2013-2018 ?. 4) Does inflation, currency rates and bi rates have a simultaneous effect on murabaha financing at Bank Mandiri Syariah (BSM) in 2013-2018?. While the research method used is descriptive quantitative research methods in the form of time series that is research on financial statements, and testing data using the SPPS program. The results obtained are: First, t arithmetic (1,731)> t table (1,724), so that Ha is accepted and Ho is rejected, it can be concluded that inflation has a significant and significant influence on murabaha financing at PT. Bank Syariah Mandiri for the period March 2013 to December 2018. Second, t arithmetic (0.212) <t table (1.724), so that Ha is rejected and Ho is accepted, it can be concluded that the currency exchange rate has no significant and significant effect on murabahah financing. third, t arithmetic (0.303) <t table (1.724), so that Ha is rejected and accepts Ho, it can be concluded that the persial bi rate has no effect and is not significant to murabaha financing. And F count> F table (3.168> 2.87) with a significant value of 0.000 <α = 5% (0.05). And Fourth: This means that there is a significant influence jointly between all independent variables (inflation, currency rates and bi rates) on the dependent variable (murabahah financing) at PT. Bank Syariah Mandiri for the period March 2013 to September 2018.

Can altcoins become viable alternatives to African fiat currencies?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seyram Pearl Kumah ◽  
Jones Odei-Mensah
Keyword(s):  
Quantile Regression ◽  
Currency Crises ◽  
Ensemble Empirical Mode Decomposition ◽  
Content Type ◽  
Frequency Space ◽  
Time Frequency ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Mode Decomposition ◽  
Daily Frequency

Purpose The paper aims to examine the asymmetric response of three major altcoins to shocks in six African fiat currencies in a time-frequency space. Design/methodology/approach Data are for the period 10th August 2015 to 2nd February 2019 at a daily frequency. The authors capture the time and frequency information in the return series of the currencies using the ensemble empirical mode decomposition. The authors implemented quantile regression and quantile-in-quantile regression on the decomposed series to test the response of altcoins to both positive and negative shocks in the fiat currencies across time to see if the altcoins are viable alternatives to African fiat currencies. Findings The outcome of the study suggests that altcoins behave differently from African fiat currencies and are viable alternative digital currencies and good hedges for African fiat currencies from the medium-term. Research limitations/implications Policymakers in Africa and across the globe can follow this paper to mitigate currency crises by adopting altcoins as alternatives to fiat currencies. Forex traders can also mitigate trade risk by using altcoins to hedge dollar/African fiat currency exchange rate risk. Originality/value The research was conducted by the authors and has not been published in any journal.

Determining Influencing Factors of Currency Exchange Rate for Decision Making in Global Economy Using MARS Method

2018 ◽  
pp. 261-273 ◽  
Author(s):  
Hasan Dinçer ◽  
Ümit Hacıoğlu ◽  
Serhat Yüksel
Keyword(s):  
Economic Growth ◽  
Decision Making ◽  
Exchange Rate ◽  
Current Account ◽  
Global Economy ◽  
Current Account Deficit ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
Turkish Lira

The aim of this study is to identify the determinants of US Dollar/Turkish Lira currency exchange rate for strategic decision making in the global economy. Within this scope, quarterly data for the period between 1988:1 and 2016:2 was used in this study. In addition to this aspect, 10 explanatory variables were considered in order to determine the leading indicators of US Dollar/Turkish Lira currency exchange rate. Moreover, Multivariate Adaptive Regression Splines (MARS) method was used so as to achieve this objective. According to the results of this analysis, it was defined that two different variables affect this exchange rate in Turkey. First of all, it was identified that there is a negative relationship between current account balance and the value of US Dollar/Turkish Lira currency exchange rate. This result shows that in case of current account deficit problem, Turkish Lira experiences depreciation. Furthermore, it was also concluded that when there is an economic growth in Turkey, Turkish Lira increases in comparison with US Dollar. While taking into the consideration of these results, it could be generalized that emerging economies such as Turkey have to decrease current account deficit and investors should focus on higher economic growth in order to prevent the depreciation of the money in the strategic investment decision.

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