Testing the Random Walk Hypothesis: Power versus Frequency of Observation

Power functions of tests of the random walk hypothesis versus stationary first order autoregressive alternatives are tabulated for samples of fixed span but various frequencies of observation. For a t -test and normalized test, power is found to depend, for a substantial range of parameter values, more on the span of the data in time than on the number of observations. For a runs test, power rapidly declines as the number of observations is increased beyond a certain point

Testing the Random Walk Hypothesis: Power versus Frequency of Observation

Power functions of tests of the random walk hypothesis versus stationary first order autoregressive alternatives are tabulated for samples of fixed span but various frequencies of observation.

On infimum Dickey–Fuller unit root tests allowing for a trend break under the null

Trend breaks appear to be prevalent in macroeconomic time series. Consequently, to avoid the catastrophic impact that unmodelled trend breaks have on power, it is standard empirical practice to employ unit root tests which allow for such effects. A popularly applied approach is the infimum ADF-type test. Its appeal has endured with practitioners despite results which show that the infimum ADF statistic diverges to −∞−∞ as the sample size diverges, with the consequence that the test has an asymptotic size of unity when a break in trend is present under the unit root null hypothesis. The result for additive outlier-type breaks in trend (but not intercept) is refined and shows that divergence to −∞−∞ occurs only when the true break fraction is smaller than 2/32/3. An alternative testing strategy based on the maximum of the original infimum statistic and the corresponding statistic constructed using the time-reversed sample data is considered

Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point

In this paper we consider the problem of testing for the co-integration rank of a vector autoregressive process in the case where a trend break may potentially be present in the data. It is known that un-modelled trend breaks can result in tests which are incorrectly sized under the null hypothesis and inconsistent under the alternative hypothesis. Extant procedures in this literature have attempted to solve this inference problem but require the practitioner to either assume that the trend break date is known or to assume that any trend break cannot occur under the co-integration rank null hypothesis being tested. These procedures also assume the autoregressive lag length is known to the practitioner. All of these assumptions would seem unreasonable in practice. Moreover in each of these strands of the literature there is also a presumption in calculating the tests that a trend break is known to have happened. This can lead to a substantial loss in finite sample power in the case where a trend break does not in fact occur. Using information criteria based methods to select both the autoregressive lag order and to choose between the trend break and no trend break models, using a consistent estimate of the break fraction in the context of the former, we develop a number of procedures which deliver asymptotically correctly sized and consistent tests of the co-integration rank regardless of whether a trend break is present in the data or not. By selecting the no break model when no trend break is present, these procedures also avoid the potentially large power losses associated with the extant procedures in such cases

Pouvons-nous réduire la dose d’azote après un retour de prairie?

Affiche présentée dans le cadre du Colloque de l'ARC, «Des racines et des ailes pour la recherche collégiale», dans le cadre du 85e Congrès de l’Acfas, Université McGill, Montréal, les 8 et 9 mai 2017.Au Québec, les émissions de gaz à effet de serre (GES) d’origine agricole représentent près de 8 % des émissions totales. Environ 40 % des émissions agricoles découle de l’usage des engrais minéraux et des engrais de ferme. Tout apport excédentaire d’engrais azoté dans les cultures se traduit par des émissions supplémentaires d’oxyde nitreux (N2O), un puissant gaz à effet de serre qui contribue aux émissions de GES. Plusieurs études ont démontré que la culture de maïs n’avait pas besoin d’apports importants d’azote en postlevée, après des retours de prairie. En collaboration avec des producteurs agricoles, 16 sites d’essais de fertilisation azotée ont été implantés sur des retours de prairie ayant reçu des engrais de ferme. Les traitements consistaient à apporter 4 doses d’azote minéral (0, 40, 80 et 120 kg N/ha). La teneur en nitrates du sol ainsi que les rendements à la récolte ont été évalués. La teneur en nitrates a confirmé l’effet significatif de la prairie et des engrais de ferme sur la teneur en azote disponible pour le maïs. L’apport d’azote minéral n’a pas eu d’effet significatif sur les rendements en azote, à l’exception d’un site. Le maïs cultivé sur un retour de prairie ne nécessite pas un ajout d’engrais minéral. Éliminer l’apport d’azote réduit tant les émissions de GES que les dépenses en engrais pour les producteurs

Robust tests for a linear trend with an application to equity indices

In this paper we develop a testing procedure for the presence of a deterministic linear trend in a univariate time series which is robust to whether the series is I(0) or I(1) and requires no knowledge of the form of weak dependence present in the data. Our approach is motivated by the testing procedures of Vogelsang [1998, Econometrica, vol 66, p123–148] and Bunzel and Vogelsang [2005, Journal of Business and Economic Statistics, vol 23, p381–394], but utilises an auxiliary unit root test to switch between critical values in the exact I(1) and I(0) environments, rather than using this unit root test to scale the test statistic as is done in the aforementioned procedures. We show that our proposed tests have uniformly greater local asymptotic power than the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) when the error process is exact I(1), identical local asymptotic when the error process is I(0), and have better overall local asymptotic power when the error process is near I(1). Our proposed tests also display superior finite sample power to the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) and are competitive in finite samples with tests designed to be optimal in both the exact I(1) and I(0) environments. We apply our test procedures to a number of equity indices and find that these series appear to have a significant upward deterministic trend, yet are also highly persistent about this long run growth path

Machine Translation and Automated Analysis of Cuneiform Languages (MTAAC)

Project Abstract: Ancient Mesopotamia, birthplace of writing, has produced vast numbers of cuneiform tablets that only a handful of highly specialized scholars are able to read. The task of studying them is so labor intensive that the vast majority have not yet been translated, with the result that their contents are not accessible either to historians in other fields or to the wider public. This project will develop and apply new computerised methods to translate and analyse the contents of some 67,000 highly standardised administrative documents from southern Mesopotamia from the 21st century BC. By automating these basic but labor-intensive processes, we will free up scholars’ time. The tools that we will develop, combining machine learning, statistical and neural machine translation technologies, may then be applied to other ancient languages. Similarly, the translations themselves, and the historical, social and economic data extracted from them, will be made publicly available on the web