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This chapter deals with features of data that suggest a certain model or method, but where this suggestion is erroneous. We highlight a few cases in which an econometrician could be directed in the wrong direction, and at the same time we show how this can be prevented from happening. These situations happen in cases where there is no strong prior information on how the model should be specified. The data are then used to guide model construction. This guidance can be in an inappropriate direction. We review a few empirical cases where some data features obscure a potentially proper view of the data and may suggest inappropriate models. We discuss spurious cycles and the impact of additive outliers on detecting ARCH and nonlinearity. We also focus on a time series that may exhibit recessions and expansions, allowing you to (wrongly) interpret the recession observations as outliers. Finally, we deal with structural breaks and trends and unit roots, and see how data with these features can look alike.

The development of stochastic investment models for actuarial and investment applications has become an important area of interest to actuaries. This paper reports the application of some techniques of modern time series and econometric analysis to Australian inflation, share market and interest rate data. It considers unit roots, cointegration and state space models. Some of the results from this analysis are not reflected in the published stochastic investment models.

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8 - Blinded by the Data

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Investment Returns and Inflation Models: Some Australian Evidence

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2 results

8 - Blinded by the Data

Summary

Investment Returns and Inflation Models: Some Australian Evidence