Testing Dependence Among Serially Correlated Multicategory Variables

By Hashem Pesaran, Allan G. Timmermann. Social Science Research Network, 2009

274 citations recorded by the source corpus.

The contingency table literature on tests for dependence among discrete multicategory variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist on the effect of serial dependency—a problem that is important in areas such as economics, finance, medical trials, and meteorology. This article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests allow for an arbitrary number of categories as well as multiway tables of arbitrary dimension and are robust in the presence of serial dependencies that take the form of finite-order Markov processes. For three-way or higher order tables we propose new tests of joint and marginal independence. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to microeconomic survey data on firms' forecasts of changes to their production and prices demonstrates the importance of correcting for serial dependencies in predictability tests.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/128eeb4f09a05d1dc23430b7884e896e53f72a77

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “The statistical significance of the directional accuracy is gauged by the Pesaran and Timmermann (2009) test, under the null hypothesis of no directional accuracy.”
Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach (2015, methodology): “Tests of the null of no directional accuracy are conducted using the test of Pesaran and Timmermann (2009).”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “(2017) and Degiannakis and Filis (2017), we consider the Directionof-Change (DoC) (Pesaran and Timmermann, 2009) as a third forecasting evaluation criterion.”
Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, methodology): “…specification with = 1 imposed does not suffer from parameter estimation uncertainty, allowing the use of conventional -tests of equal MSPEs (see Diebold and Mariano 1995).7 The statistical significance of gains in directional accuracy is evaluated using the test of Pesaran and Timmermann (2009).”
Do Oil Price Increases Cause Higher Food Prices (2014, unknown): “7 This assessment is based on the statistical test for directional accuracy proposed in Pesaran and Timmermann (2009).”