Research in the field of forecasting suggests that judgmental forecasts are typically subject to a number of biases. These biases may be related to the statistical characteristics of the data series, or to the characteristics of the forecasting task. Here, a number of understudied forecasting paradigms have been investigated and these revealed interesting ways of improving forecasting performance. In a series of experiments, by controlling parameters such as the horizon and direction of the forecasts or the length, scale and presentation format of the series, I demonstrate that forecasting can be enhanced in several ways. In Chapter 3, I examine forecasting direction as well as the use of an end-anchor to the forecasting task (Experimental Studies 1-2). In Chapter 4, I examine the way the length of the series affects forecasting performance of various types of time series (Experimental Studies 3-4). Dimensional issues related to the forecasting task are further investigated in Chapter 5, where graphs’ scale is now manipulated in series with different types of noise (Experimental Studies 5-6). Task characteristics are further explored in dynamic settings in Chapter 6, in a number of experiments (Experimental Studies 7-12), where a new experimental paradigm for judgmental forecasting is introduced. Here, I test already identified robust forecasting biases in this dynamic setting and compare their magnitude and direction with those found in static environments. I conclude that forecasting performance is affected by data series’ and task characteristics in the following ways i) end-anchoring and backwards direction in forecasting tasks enhance accuracy ii) longer lengths are preferable for a number of series’ types iii) dynamic settings may offer specific enhancements to the forecasting task. The implications of these findings are discussed with respect to judgmental forecasting and corresponding cognitive mechanisms, while, directions for future research, towards the development of a unified framework for judgmental forecasting, are suggested.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:626720 |
| Date | January 2014 |
| Creators | Theochari, Z. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1421420/ |
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