E jurnal Matematika

Number Vol.3 (1), 2014

1-7
APLIKASI REGRESI DATA PANEL DENGAN PENDEKATAN FIXED EFFECT MODEL (STUDI KASUS: PT PLN GIANYAR)

Authors: NI PUTU ANIK MAS RATNASARI, I PUTU EKA NILA KENCANA, G. K. GANDHIADI

Abstract

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Number of views: 599

Panel data regression has three approaches. One of these approaches is Fixed Effect Model (FEM). FEM is common estimated using Least Square Dummy Variable. The use of dummy variable in FEM is based on assumption that slope coefficients are constant but intercept varies over individuals. One of application of FEM is to find out motivation of employees at PT PLN Gianyar for non-outsourcing and outsourcing employees based on existence, relatedness, and growth. This research yields the following two models: 〖Mot〗_(〖non〗_it )=-0,05+0,56〖EX〗_it 〖Mot〗_(〖out〗_it )=0,21-0,00〖EX〗_it+0,85〖GR〗_it with 67% motivation non-outsourcing employees represented by existenceand73% motivation non-outsourcing employees represented by existence and growth. Keywords: Panel Data Regression, Fixed Effect Model, Motivation

8-16
PENERAPAN REGRESI AKAR LATEN DALAM MENANGANI MULTIKOLINEARITAS PADA MODEL REGRESI LINIER BERGANDA

Authors: DWI LARAS RIYANTINI, MADE SUSILAWATI, KARTIKA SARI

Abstract

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Number of views: 554

Multicollinearity is a problem that often occurs in multiple linear regression. The existence of multicollinearity in the independent variables resulted in a regression model obtained is far from accurate. Latent root regression is an alternative in dealing with the presence of multicollinearity in multiple linear regression. In the latent root regression, multicollinearity was overcome by reducing the original variables into new variables through principal component analysis techniques. In this regression the estimation of parameters is modified least squares method. In this study, the data used are eleven groups of simulated data with varying number of independent variables. Based on the VIF value and the value of correlation, latent root regression is capable of handling multicollinearity completely. On the other hand, a regression model that was obtained by latent root regression has R_adj^2 value of 0.99, which indicates that the independent variables can explain the diversity of the response variables accurately. Keywords: Multiple Linear Regression, Multicollinearity, Latent Root Regression, Least Squares Method Modified

17-24
PENERAPAN MODEL ARBITRAGE PRICING THEORY DENGAN PENDEKATAN VECTOR AUTOREGRESSION DALAM MENGESTIMASI EXPECTED RETURN SAHAM (Studi Kasus: Saham-Saham Kompas100 Periode 2010-2013)

Authors: VIAN RISKA AYUNING TYAS, KOMANG DHARMAWAN, MADE ASIH

Abstract

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Number of views: 716

The Arbitrage Pricing Theory (APT) is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB), and theinterest rateof Bank Indonesia(SBI) are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340% Keywords: Arbitrage Pricing Theory, Granger causality test, optimal lag test, Portmanteau test, stationary test, Vector Autoregression

25-32
PEMILIHAN KRITERIA DALAM PEMBUATAN KARTU KREDIT DENGAN MENGGUNAKAN METODE FUZZY AHP

Authors: JOKO HADI APRIANTO, G. K. GANDHIADI, DESAK PUTU EKA NILAKUSMAWATI

Abstract

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Number of views: 504

The rise of credit card users, make banks compete to provide a wide range of offers to attract customers. This study aims to determine the priority criteria selected customers for establishment credit cards by using a fuzzy AHP method. Method fuzzy AHP is a combination of the AHP method and fuzzy method. Fuzzy AHP approach particularly triangular fuzzy number approach to the AHP scale should be able to minimize uncertainty for the results obtained are more accurate. The criteria used for this study is the interest rate , the promo/discount, limit, and annual dues. Based on the steps of calculation of data obtained fuzzy AHP respondents have value CR = 0.049, which means consistent because it meets the standards set CR < 0.10 and that became the order of priority are limit, promo/discount, interest rate, and continued with weights of priorities are 0408, 0.28, 0.16, and 0.152. Keywords: AHP, Fuzzy Analytic Hierarchy Process (FAHP), Criteria of Credit Card, Consistensy Ratio , Weight Priority

33-37
MENGATASI MASALAH HETEROSKEDASTISITAS DENGAN MENGASUMSIKAN VARIANS VARIABEL GANGGUANNYA PROPORSIONAL DENGAN X_i^2 DAN [E(Y_i)]^2

Authors: MADE ADI GUNAWAN, LUH PUTU IDA HARINI, MADE ASIH

Abstract

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Number of views: 618

The volatilities of time series data often experience heteroscedastic problems. Heteroscedasticity is a nuisance variable in the regression equation having a variance that is not constant. Therefore, methods to analyze the problem of heteroscedasticity are needed. This study aims to demonstrate how to eliminate the problem of heteroscedasticity. The method used is, GoldFeld-Quandt. As for eliminating the problem of heteroscedasticity by assuming the variance of interference variables proportional to E〖(Y_i)〗^2. Keywords: Arbitrage Pricing Theory, Granger causality test, optimal lag test, Portmanteau test, stationary test, Vector Autoregression