testing residual distribution, heteroscedasticity, for Arch effect and serial correlation
, we can observe that the model is valid, since residuals are normally distributed.
When controlling for serial correlation
using Cochrane-Orcutt AR(1) regressions in the Democratic field, the most significant finding is that Bernie Sanders electability falls significantly to 14.
As Beenstock, Dickinson, and Khajuria (1986) observe, serial correlation
is very likely to be an issue in life insurance data because of the considerable slice of recurring payment policies, so that any shock to premiums in one given province and year is bound to persist in subsequent years, albeit with decreasing intensity.
AR(1) and AR(2) are first and second order serial correlation
The F-statistic and value of of Breusch-Godfrey Serial Correlation
LM test imply that the null hypothesis of no serial correlation
cannot be rejected.
The Breusch-Godfrey/ Wooldridge test was implemented to test serial correlation
in disturbance terms.
To correct for the serial correlation
, Mark (1995) uses Newey-West robust estimators, and to counter the finite sample issues with endogenous and persistent regressors he uses Gaussian and non-parametric bootstrap distributions generated under the null hypothesis that the exchange rate is unpredictable.
The fraction of jumps and the serial correlation
in changes of position are most informative about the size and nature of the costs of adjustment.
When Poterba and Summers (1988) examined mean reversion in US stock prices using the variance ratio test on monthly data they found positive serial correlation
in stock returns for periods of less than one year, and negative serial correlation
in longer horizon returns.
And when a Cochrane-Orcutt correction is made for serial correlation
in the CPI equation, the coefficients become altogether insignificant (Hall and Hart 2012: 69, fn.
2002, among many others), and that (ii) the presence of trends alters the estimation of the serial correlation
(YUE et al.
It also introduces differences between time series data from other forms of data and explains basic ideas like autoregression, autocorrelation, serial correlation
, stationarity, exogeneity, weak dependence, trending, seasonality, structural breaks, and stability.