Time Series Analysis, Taxing the Wealthy, and GDP Growth

Although the language of time series analysis can seem specialized, practitioners can maintain a competitive edge by knowing how to interpret the results of time-series studies, says Dennis McLeavey CFA.

In finance, time series are ubiquitous, and so knowledge of such things as what unit roots are or how to control for heteroskedasticity can be useful in evaluating reports.

McLeavey: “Financial reports make claims with investment relevance, and analysts need to know how to evaluate whether the reports provide solid evidence for claims.”

Amid all of the political wrangling in DC over tax rates for the wealthiest Americans, a recent paper from the Congressional Research Service Taxes and the Economy: An Economic Analysis of the Top Tax Rates Since 1945 drew the ire of Senate Republicans.

“In the study, the author, Thomas L. Hungerford, concluded that reductions in the top tax rates has had little association with saving, investment, or productivity growth, but were associated with the increasing concentration of income at the top”, he says, and adds that the conclusion was not overly popular with a certain political party, so the paper came under fire for some of his methods used (or in this case not used) to support its points.

With sixty observations for each year and time series analysis, McLeavey thinks it’s difficult to refute as to support the belief that lower top-tax rates are associated with growth. “Hungerford used a standard set of analytical tools, which are nicely explained in Damodar N. Gujarati’s basic econometrics text or Escudero’s A Primer on Unit Roots and Cointegration.”

For time series regression, the series need to be stationary (constant mean, variance, covariance). Hungerford controlled for heteroskedastic (nonconstant variance) and autocorrelated (correlated with themselves over time) error terms, using the Newey-West method.

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