Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index

This paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In many cases, the forecasts based on the original variables result in substantially smaller root mean squared errors than models based on logs. In turn, if forecasts based on logs are superior, the gains are typically small. This outcome sheds doubt on the common practice in the academic literature to forecast inflation rates based on differences of logs.

The effects of strategy instruction on writing strategy use for students of different proficiency levels

Learners’ strategy use has been widely researched over the past few decades. However, studies which focus on the impact of strategy instruction on strategy use, and how far learners of different proficiency levels are able to use the strategies taught in an effective manner, are somewhat rare. The focus of this paper is the impact of writing strategy instruction on writing strategy use of a group of 12 second language learners learning to write in English for Academic Purposes classes. Stimulated recall was used to explore whether this impact differed according to the proficiency level of the students, and revealed that for both high and low proficiency learners’ strategy use developed as a result of the instruction. The implications of these findings for strategy instruction design are discussed

Back to basics: how measures of lexical diversity can help discriminate between CEFR levels

This study contributes to ongoing discussions on how measures of lexical diversity (LD) can help discriminate between essays from second language learners of English, whose work has been assessed as belonging to levels B1 to C2 of the Common European Framework of Reference (CEFR). The focus is in particular on how different operationalisations of what constitutes a “different word” (type) impact on the LD measures themselves and on their ability to discriminate between CEFR levels. The results show that basic measures of LD, such as the number of different words, the TTR (Templin 1957) and the Index of Guiraud (Guiraud 1954) explain more variance in the CEFR levels than sophisticated measures, such as D (Malvern et al. 2004), HD-D (McCarthy and Jarvis 2007) and MTLD (McCarthy 2005) provided text length is kept constant across texts. A simple count of different words (defined as lemma’s and not as word families) was the best predictor of CEFR levels and explained 22 percent of the variance in overall scores on the Pearson Test of English Academic in essays written by 176 test takers.