DELTA merupakana aplikasi yang dikembangkan untuk menyelesaikan permasalahan data panel dan time seris,dan salah satu analisis yang digunakan dalam time series adalah Instrumental Variable Regression untuk menghilangkan inkonsistensi variable pada metode OLS akibat adanya pengaruh simultan
Installation
- Required: Windows OS
- Download Delta.exe
- Install meenggunakan Windows Installer
Screenshoot
Fitur
- estimasi model menggunakan two stages least square
- uji kebaikan model (Rsquare, Adj Rsquare, F, t)
- uji asumsi (Breusch Godfrey, Jarque Berra, dan Durbin Watson)
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Programming Languange icon - Python 3.5.2 (required)
- R 2.7 (required)
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Package Requirement icon - pip
- PyQT5
- numpy+mkl (using .whl file installment)
- Pandas
- Scipy
- statsmodels
- multiprocessing
- matplotlip
Main Architecture
- Delta is an application that has a modular architecture by utilizing the integration function based on dependency declaration on the main framework.
- each module developed must have object oriented properties, so that it can take advantage of classes that have been developed.
- python is used as the main interpreter in delta development, algorithm is formed in python programming language and display on GUI
Development Tools (On this thesis)
- Jetbrain Pycharm Community Version as IDE
- Qt Designer from PyQT5 as GUI Desig Platform
Kickstart for developing preparation
- Open IDE PyCharm community edition
- Select menu settings then choose file → Settings or using shortcut Ctrl + Alt + S.
- Add package using "+" sign, and search on searching box
- install all required package
GUI Development
- open QT Designer Application
- Design using all drop-down fiture in application
- save your design as .ui file, dan convert using pyuic5 by python on command prompt
pyuic5 -x [your .ui file] -o [your .py file]
Code Sample
*pencarian nilai Beta
p = z * np.linalg.inv(np.transpose(z) * z) * np.transpose(z)
b1 = np.linalg.inv(np.transpose(x) * p * x)
b2 = np.transpose(x) * p * y
beta = b1*b2
return beta
*pembentukan summary
def summary(self):
ss = self.sumsquare()
r2 = ss[0] / ss[2] #menghitung R squared
r = np.sqrt(r2) #menghitung R
r2adj = 1 - (self.n - 1) / (self.n - self.p) * (1 - r2) #Menghitung R squared adjusted
se = np.sqrt(ss[1] / (self.n - self.p)) #Menghitung standar error model
#Menghitung statistik Durbin Watson untuk uji autokorelasi
e = np.array(self.resid())
dw = 0
for i in range(1, len(e)):
a = (e[i] - e[i - 1]) ** 2
dw = dw + a
dw = dw / np.sum(e ** 2)
output = np.array([r, r2, r2adj, se, dw])
return (output)