Thesis
Chasing yesterday : nowcasting economic activity with timely indicators
- Creator
- Awarding institution
- University of Strathclyde
- Date of award
- 2016
- Thesis identifier
- T14248
- Person Identifier (Local)
- 201274437
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- The thesis Chasing Yesterday: Nowcasting Economic Activity with Timely Indicators presents three separate essays rooted in the topic of nowcasting that have been written since 2013. A variety of research themes drawn from the nowcasting literature are covered, with the essays pulled together through an underlying link of the usefulness of timely economic indicators to policymakers, investors and researchers. Following an introduction to nowcasting and the broad research themes covered in the thesis, Chapter 2 is titled "The Importance of Being Timely", a version of which has been recently published in the Journal of Forecasting. The research in the chapter is concerned with understanding the contribution quickly-released survey data make to tracking economic activity in nowcasting models. Generally speaking, policymakers want to know about real-time economy performance. However, closely watched macroeconomic time series produced by national statistics offices are published infrequently, with a time lag and are subject to revision. Such issues create uncertainty in tracking economic developments, a by-product of which is to raise the value of business and consumer surveys. Although providing less granularity than official data series, the surveys are released in a timelier manner and are generally not revised. Using real-time data sourced from the Deutsche Bundesbank, the OECD and the Office for National Statistics, an assessment of the role that the popular and widely used Purchasing Managers' Index (PMI) play in reducing forecasting errors in a simple "nowcasting" framework is undertaken. The empirical exercise is conducted for five developed economies and also covers the period of the Great Recession. The conclusion is clear: timing matters. The third chapter "Nowcasting UK GDP during the Depression" reviews the performance of several statistical techniques in nowcasting preliminary estimates of UK GDP, particularly during the recent depression. Traditional bridging equations, MIDAS regressions and factor models are all considered. While there are various theoretical differences and perceived advantages for each technique, replicated real-time out-of-sample testing shows that, in practice, there is in fact little to choose between methods in terms of end-of-period nowcasting accuracy. The analysis also reveals that none of the aforementioned statistical models can consistently beat a consensus of professional economists in nowcasting preliminary GDP estimates. This inability of statistical models to beat the consensus may reflect several factors, one of which is the revisions and re-appraisal of trends inherent in UK GDP statistics. The suggestion is that these changes impact on observed relationships between GDP and indicator variables such as business surveys, which impairs nowcasting performance. Indeed, using a synthetic series based purely on observed preliminary GDP estimates, which introduces stability to the target variable series, the nowcasting accuracy of regressions including closely-watched PMI data is improved by 25-40 percentage points relative to a naive benchmark. The final research chapter, "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data", a version of which is due to be published in the Journal of Forecasting, changes tack somewhat by assessing the potential of internet search data as a useful source of information for policymakers when formulating decisions based on their understanding of the current economic environment. The chapter builds on earlier literature and the ideas generated in chapters 2 and 3 via a structured value assessment of the data provided by Google Trends. This is done through two empirical exercises related to the forecasting of changes in UK unemployment. Firstly, economic intuition provides the basis for search term selection, with a resulting Google indicator tested alongside survey-based variables in a traditional forecasting environment. Secondly, this environment is expanded into a pseudo-time nowcasting framework which provides the backdrop for assessing the timing advantage that Google data have over surveys. The framework is underpinned by a MIDAS regression which allows, for the first time, the easy incorporation of internet search data at its true sampling rate into a nowcast model for predicting unemployment.
- Resource Type
- DOI
- Date Created
- 2016
- Former identifier
- 1248449
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PDF of thesis T14248 | 2021-07-02 | 公开 | 下载 |