Özet:

Ordinary least squares (OLS) regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the OLS residuals usually are correlated over time. It is not desirable to use OLS analysis for time series data since the assumptions on which the classical linear regression model is based will usually be violated. Violation of the independent errors assumption has three important consequences for ordinary regression: First, statistical tests of the significance of the parameters and the confidence limits for the predicted values are not correct. Second, the estimates of the regression coefficients are not efficient. Third, since the ordinary regression residuals are not independent, they contain information that can be used to improve the prediction of future values. This study attempts to explain the effects of macroeconomic variables (such as gold prices, S&P index, currency parity and interest rates) and capital flows (such as foreign direct investment and foreign portfolio investment) on ISE-100 index using the 553 weekly time series data between 07.01.2005 to 03.02.2012 and compare OLS and autoregression results

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