BibTex Citation Data :
@article{J.Gauss14693, author = {Deden Nanda and Suparti Suparti and Abdul Hoyyi}, title = {ANALISIS PENGARUH JUMLAH UANG BEREDAR DAN NILAI TUKAR RUPIAH TERHADAP INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN PEMODELAN REGRESI SEMIPARAMETRIK KERNEL}, journal = {Jurnal Gaussian}, volume = {5}, number = {3}, year = {2016}, keywords = {Composite Stock Price Index (CSPI), Semiparametric regression, Kernel, GCV}, abstract = { Stocks are one of the many forms of investment chosen by the investor. Investors can use Composite Stock Price Index (CSPI) as one of the indicators that show the movement of stock prices. CSPI fluctuates every day, where one of the causes are macroeconomic factors. Therefore needs to be done a proper analysis to model the CSPI and the factors that influence it. This study is using 1 parametric component variable (money supply) and 1 nonparametric component variable (exchange rate the rupiah against the dollar). So that proper modeling is semiparametric regression. Nonparametric component will be using kernel regression method by selecting the optimal bandwidth using a generalized cross validation method (GCV). This study uses monthly data. Data in sample is used as much as 68 data that is taken from Januari 2010 to August 2015, meanwhile out sample that is used as much as 6 data from September 2015 to February 2016. Based on the results of the analysis that has been done, the best kernel semiparametric regression model is using gaussian kernel function with bandwidth is around 47.94 and GCV=34675.27047. Determination coefficient value is 0.9781. Evaluation result of the model for value of Mean Absolute Percentage Error (MAPE) data out sample is around 4,036%, which indicates that the model is very accurate. Keywords : Composite Stock Price Index (CSPI), Semiparametric regression, Kernel, GCV}, issn = {2339-2541}, pages = {373--382} doi = {10.14710/j.gauss.5.3.373-382}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14693} }
Refworks Citation Data :
Stocks are one of the many forms of investment chosen by the investor. Investors can use Composite Stock Price Index (CSPI) as one of the indicators that show the movement of stock prices. CSPI fluctuates every day, where one of the causes are macroeconomic factors. Therefore needs to be done a proper analysis to model the CSPI and the factors that influence it. This study is using 1 parametric component variable (money supply) and 1 nonparametric component variable (exchange rate the rupiah against the dollar). So that proper modeling is semiparametric regression. Nonparametric component will be using kernel regression method by selecting the optimal bandwidth using a generalized cross validation method (GCV). This study uses monthly data. Data in sample is used as much as 68 data that is taken from Januari 2010 to August 2015, meanwhile out sample that is used as much as 6 data from September 2015 to February 2016. Based on the results of the analysis that has been done, the best kernel semiparametric regression model is using gaussian kernel function with bandwidth is around 47.94 and GCV=34675.27047. Determination coefficient value is 0.9781. Evaluation result of the model for value of Mean Absolute Percentage Error (MAPE) data out sample is around 4,036%, which indicates that the model is very accurate.
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