# serial correlation

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Related to serial correlation: multicollinearity

## Serial Correlation

In technical analysis, a measure of how well past occurrences predict future occurrences. Most importantly, serial correlation checks whether and how often a particular price movement will result in a different price movement. Serial correlation lies at the heart of technical analysis. It is also called autocorrelation.

## serial correlation

The relationship that one event has to a series of past events. In technical analysis, serial correlation is used to test whether various chart formations are useful in projecting a security's future price movements.
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It presents a problematic situation as common techniques of panel analysis are incapable of handling both cross sectional dependence and serial correlation simultaneously.
While serial correlation is well understood, it has been largely ignored by researchers using DD estimation.
The serial correlation LM test along with ACF and PACF plot results reveals that serial correlation in the residuals not exists.
All independent variables in model 4 are consistent with theories and the model have no severe problem of serial correlation, multicollinearity, unit root and heteroscedasticity which fulfil the BLUE properties.
The result from LM test proves that there is no serial correlation among the variables as shown in Table-4.
Heteroskedasticity and serial correlation Heteroskedasticity Serial correlation Model P-Value [Chi.sup.2] P-Value F value Model 1 0.63 0.23 0.57 0.31 Model 2 0.12 2.33 0.54 0.37 Model 3 0.31 1.00 0.24 1.39 Model 4 0.23 1.40 0.24 1.39 Model 5 0.22 1.47 0.30 1.06 Table 4.
Bounds and cointegration tests in rows (a) and (b) are all highly significant, and the null of no serial correlation cannot be rejected by any p value in row (c).
Table 5 Diagnostics Tests of the Models (P-Values) UM RWD RWDT Benchmark Models Serial Correlation [0.45] [0.18] [0.30] Heteroskedasticity [0.40] [0.93] [0.97] Normality [0.35] [0.81] [0.59] Models with Survey Expectations Serial Correlation [0.46] [0.12] [0.1] Heteroskedasticity [0.40] [0.74] [0.72] Normality [0.36] [0.65] [0.54] AR MA ARIMA Benchmark Models Serial Correlation [0.96] [0.1] [0.54] Heteroskedasticity [0.55] [0.53] [0.11] Normality [0.38] [0.56] [0.68] Models with Survey Expectations Serial Correlation [0.96] [0.29] [0.27] Heteroskedasticity [0.67] [0.92] [0.10] Normality [0.38] [0.32] [0.46] The tests for serial correlation, heteroscedasticity and normality are Breusch-Godfrey Serial Correlation LM Test, White Test and Jarque Berra, respectively.
In conclusion, for a VEC estimation model, considering a dependent variable, LOG_ASSETS and an independent variable, RETURN, residuals are normally distributed, the model does not have an ARCH effect and there is no serial correlation, which is desirable in all 3 cases, so we have a proper regression model.
We test for serial correlation using the method discussed in Wooldridge (2002).
The Arellano-Bond test for first-order serial correlation in the disturbances--in differences--rejects the null that there is no first-order serial correlation, but it does not reject the null of no second-order serial correlation, as shown in Tables 1 and 2.
Tables 7 and 8 report the output of regressions that use the Cochrane-Orcutt technique to correct for serial correlation.

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