Model Dinamis: Autoregressive Dan Distribusi Lag - Muhajir Choir Nurahman; Sri Wahyuningsih; Desi Yuniarti (EKSPONENSIAL, 2016)
Regression model using time series data not only use the effect of changing the independent variables on the dependent variable in the same period and for the same period of observation, but also use the period of time before. The purpose of this study was to determine the dynamic model autoregressive and distribution lag by type of infinite lag, and to know the effect of US dollar exchange rate against GDP in 1993-2013. Based on the analysis of data has that GDP and US dollar exchange rate has a rising trend pattern, and obtained by a simple regression model. But this model can not be used because of two assumptions have not been met and that there are heteroscedasticity and autocorrelation. So this model should be transformed using log, and log transformation model is obtained from a simple regression. The transportation model can be used as desiredint his model is only one assumption are not met and that there are autocorrelation. Then sub sequently estimating models and obtained Koyckas well as all assumptions are met, namely residual normal distribution, no problem heteroscedasticity and autocorrelation. Thus, the obtained dynamic distribution models also lag within finite lag types.