Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman

Possible Causes of Autocorrelation

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Introduction to Econometrics ECO 356 Course Guide and Course Material

INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN

4.2.3.1 Possible Causes of Autocorrelation There is two forms autocorrelation occurrence, which could either be positive and negative. Persistent effects of excluded variables are probably the most frequent cause of positive autocorrelation, the usual type of economic analysis. In Figure 4.1, Y
depends on X and some minor variables not included explicitly in the specification. The disturbance term in the model is generated by the combined effects of these excluded variables. In the first observation, the excluded variables have a net positive effect and the disturbance term is positive. If the excluded variables change slowly, their positive effect will persist, and the disturbance term will remain positive. In time the balance will change, and the net effect of the excluded variables becomes negative. Here, the persistence effect works the other way, and the disturbance term remains negative fora few observations. The duration and amplitude of each positive and negative sequence are essentially random, but overall there will be a tendency for positive values of the disturbance term to be followed by positive ones and for negative values to be followed by negative ones. However, a factor to note is that autocorrelation is on the whole more likely to be a problem for shorter intervals between observations.

INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
120
Figure 4.1 Positive Autocorrelation
Negative autocorrelation means that the correlation between successive values of the disturbance term is negative. A positive value in one observation is more likely to be followed by a negative value than a positive value in the next, and vice versa this is shown by an illustrative scatter diagram in Figure 4.2. Aline joining successive observations to one another would cross the line relating Y to X with greater frequency than one would expect if the values of the disturbance term were independent of each other. Economic examples of negative autocorrelation are relatively uncommon, but sometimes it is induced by manipulations used to transform the original specification of a model into a form suitable for regression analysis.

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