Unit root test for stationary of dataThe major purpose for conducting  terjemahan - Unit root test for stationary of dataThe major purpose for conducting  Bahasa Indonesia Bagaimana mengatakan

Unit root test for stationary of da

Unit root test for stationary of data

The major purpose for conducting unit root test is that if we use the data without checking their stationarity properties, the results derived from the regression models would produce the so called spurious results (Datta and Kumar, 2011). Before estimating our modified model in the equation

(2) it was very important to test out stochastic properties of the variables to be estimated. Habitually this task is realised by conducting unit root test. However, one of the weaknesses of unit root test is related to small number of observations and that a minimum number of 20 observations are required so as to get reliable results which can be made inference (Gujarati and Porter, 2009; Gujarati, 2004). The analysis was done using the Dickey-Fuller (DF) or more convenient ADF that is Augmented Dickey-Fuller and Phillips-Perron unit root test. The study proceeded with the estimation of the model in equation (2). The null hypothesis for the two tests was unit root or the time series was non-stationary (i.e. δ = 0) while the alternative hypothesis states that there is no unit root or the time series was stationary (i.e.   0 ).The general form of DF and ADF is estimated by using the following models:
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Unit root test for stationary of dataThe major purpose for conducting unit root test is that if we use the data without checking their stationarity properties, the results derived from the regression models would produce the so called spurious results (Datta and Kumar, 2011). Before estimating our modified model in the equation(2) it was very important to test out stochastic properties of the variables to be estimated. Habitually this task is realised by conducting unit root test. However, one of the weaknesses of unit root test is related to small number of observations and that a minimum number of 20 observations are required so as to get reliable results which can be made inference (Gujarati and Porter, 2009; Gujarati, 2004). The analysis was done using the Dickey-Fuller (DF) or more convenient ADF that is Augmented Dickey-Fuller and Phillips-Perron unit root test. The study proceeded with the estimation of the model in equation (2). The null hypothesis for the two tests was unit root or the time series was non-stationary (i.e. δ = 0) while the alternative hypothesis states that there is no unit root or the time series was stationary (i.e.   0 ).The general form of DF and ADF is estimated by using the following models:
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Uji unit root untuk stasioner data Tujuan utama untuk melakukan uji unit root adalah bahwa jika kita menggunakan data tanpa memeriksa sifat stasioneritas mereka, hasil yang diperoleh dari model regresi akan menghasilkan apa yang disebut hasil palsu (Datta dan Kumar, 2011). Sebelum mengestimasi model kita diubah dalam persamaan (2) itu sangat penting untuk menguji sifat stokastik dari variabel yang akan diperkirakan. Biasanya tugas ini diwujudkan dengan melakukan uji unit root. Namun, salah satu kelemahan dari uji akar unit terkait dengan sejumlah kecil pengamatan dan bahwa jumlah minimum 20 pengamatan yang diperlukan sehingga untuk mendapatkan hasil yang dapat diandalkan yang dapat dilakukan inferensi (Gujarati dan Porter, 2009; Gujarati, 2004). Analisis dilakukan dengan menggunakan Dickey-Fuller (DF) atau ADF lebih nyaman yang Augmented Dickey-Fuller dan Phillips-Perron uji unit root. Penelitian ini melanjutkan dengan estimasi model dalam persamaan (2). Hipotesis nol untuk dua tes itu unit root atau time series adalah non-stasioner (yaitu δ = 0) sedangkan hipotesis alternatif menyatakan bahwa tidak ada unit root atau time series adalah stasioner (yaitu   0) .suatu umum bentuk DF dan ADF diperkirakan dengan menggunakan model berikut:




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