| dc.creator |
Çuhadar, Murat |
|
| dc.creator |
Dayan, Kübra |
|
| dc.creator |
Demirbaş, Kerime |
|
| dc.date |
2019-06-30T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:36:21Z |
|
| dc.date.available |
2020-10-06T09:36:21Z |
|
| dc.identifier |
2c6a1098-450f-44b3-8d9e-5ab958c30c80 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/2c6a1098-450f-44b3-8d9e-5ab958c30c80/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/56301 |
|
| dc.description |
<p> a measure of the currency's competitive power. Because exchange rateschange over short periods and are often ups and downs, speculators needeffective methods to reduce risks. In this study, it was aimed to determine themethod with the highest estimation performance by comparing the estimationsuccesses of Artificial Neural Network models with different architectures, BoxJenkins and exponential smoothing methods and to produce monthly realeffective exchange rate based on CPI estimates for 2019 with the help of thedetermined model. The study benefit 195 monthly data between January 2003and March 2019 which was obtained from the "Foreign Exchange RatesStatistics" bulletin published by the Central Bank of the Republic of Turkey.Forecasting performances of the models were evaluated by the MAPE statistics.As a result of the analyzes performed, it was found that Box-JenkinsMultiplicaptive-seasonal ARIMA (0,1,1)(1,0,0)12 model was the most successfulone among the alternative models applied. With the help of the selected model,monthly real effective exchange rate forecasts were made for the year 2019.<br></p> |
|
| dc.language |
tur |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
TÜFE Bazlı Reel Efektif Döviz Kurunun Alternatif Yaklaşımlarla Modellenmesi ve Tahminlenmesi |
|
| dc.type |
info:eu-repo/semantics/article |
|