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Assessing probabilistic forecasts about particular situations

Assessing probabilistic forecasts about particular situations

In Exhibit 4 I have framed the average skill scores with values of zero (no skill) or less. The SESS finds no skill or worse in forecasts for which the outcome option allocated a probability of 0.75 or greater occurs 50% of the time. An evaluator using the SESS might therefore mistakenly reject a method or forecaster as having no skill. The AESS, on the other hand, would not mislead an evaluator in this way, and would lead to the sensible conclusion that any set of forecasts likely to lead a decision maker to correctly anticipate actual outcomes more frequently than chance, should be preferred to the equal-likelihood forecast.
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Forecasts of the Scottish economy [November 2009]

Forecasts of the Scottish economy [November 2009]

of 2009, and in line with our revision downwards of our central scenario forecasts for Scottish GVA in 2009. Until the recent economic downturn, the Scottish labour market had been outperforming that of the UK when measured by employment rate, and had seen historically high levels of employment and low levels of unemployment. Of crucial importance to realised levels of unemployment will be the extent to which people who lose employment switch into the unemployed, or move into labour inactivity, i.e. unemployed but not available for work. As of November 2009, and as reported in the Chief Economic Advisor’s report, the increase in the Scottish ILO unemployment rate in Scotland over the last year (2.7 percentage points) was greater than the increase in the UK as a whole (up 2.4 percentage points).
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Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

The European Centre for Medium-range Weather Fore- casts (ECMWF) produces seasonal forecasts from GCM simulations (Molteni et al., 2011). Weisheimer and Palmer (2014) evaluated the reliability of the precipitation forecasts issued by ECMWF System 4 on a scale ranging from “dan- gerous” to “perfect”. Over the world, forecasts often fell within the “marginally useful” category. In France, they were ranked as “marginally useful” during wet winters and sum- mers, “not useful” in dry winters and “dangerous” in dry summers. Kim et al. (2012) also evaluated the skill of Sys- tem 4 precipitation and temperature forecasts at the global scale. Despite good overall performances, they identified sys- tematic biases, e.g. a warm bias in the North Atlantic. Sev- eral studies have proposed to bias correct ECMWF System 4 forecasts in different contexts. Di Giuseppe et al. (2013) applied a spatially based precipitation bias correction to im- prove malaria forecasts. Trambauer et al. (2015) applied a linear scaling method to forecast hydrological droughts in southern Africa. In the same context, Wetterhall et al. (2015) applied a quantile mapping method to daily precipitation val- ues, and showed that bias correction was able to improve the skill of dry spell forecasts.
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Quantile forecasts of daily exchange rate returns from
forecasts of realized volatility

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

The increasing availability of high-frequency intraday data for …nancial variables such as stock prices and exchange rates has fuelled a rapidly growing research area in the use of realized volatility estimates to forecast daily, weekly and monthly returns volatilities and distributions. Andersen and Bollerslev (1998) showed that using realized volatility (obtained by summing the squared intraday returns) as the measure of unobserved volatility for the evaluation of daily volatility forecasts from ARCH/GARCH models 1 , instead of the usual practice of proxy- ing volatility using daily squared returns, suggests such forecasts are more accurate than had hitherto been found. Recent contributions have gone beyond the use of realized volatility as a measure of actual volatility for evaluation purposes, and consider the potential value of intraday returns data for forecasting volatility at lower frequencies (such as daily). Andersen, Bollerslev, Diebold and Labys (2003b) set out a general framework for modelling and forecasting with high-frequency, intraday return volatilities, drawing on contributions that include Comte and Renault (1998) and Barndor¤-Nielsen and Shephard (2001). 2 The (log of) the realized volatility
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Experimental Reservoir Storage Forecasts Utilizing Climate-Information Based Streamflow Forecasts

Experimental Reservoir Storage Forecasts Utilizing Climate-Information Based Streamflow Forecasts

By using spatially downscaled and temporally disaggregated precipitation forecasts from the ECHAM4.5 GCM along with the climatological forcings from the NLDAS-2 dataset, this study developed retrospective seasonal streamflows for the period of 20 years (from 1991 to 2010) over the US Sunbelt based on two LSMs - Noah3.2 and CLM2. We then performed a quantitative assessment of the different sources of errors in developed seasonal streamflow forecasts. To quantify these errors, our study employed Root Mean Square Error (RMSE) to compare the relative magnitudes of errors resulting from temporal disaggregation, spatial downscaling, large-scale ECHAM4.5 precipitation forecasts, and climatological forcings, excluding precipitation. Our analysis based on the decomposition metrics described in section 3.2 shows that disaggregation scheme introduces more errors in streamflow forecasting in comparison to spatial downscaling approach. The maximum and minimum errors due to downscaling and disaggregation procedures are found during the summer and the winter, respectively. The errors due to the disaggregation approach alone was dominant during dry seasons as well as in dry regions (i.e., semiarid and desert regions). This is due to the poor performance of the disaggregation method in reproducing peak rainfall events that are frequently observed during convective storms in arid regions. Analysis on errors arising from climatological forcings in streamflow simulations reveals that a significant portion of the errors are due to using climatological precipitation forcings in comparison to the errors arising from the climatological forcings excluding precipitation. In addition, we realized that some regions (e.g., the West) provide reliable skill in streamflow forecasting primarily due to the updated initial hydrological conditions (IHCs), even when the models are forced with climatological forcings. This is in agreement to other studies such as Shukla and Lettenmaier (2011), Sinha and
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Forecasts of the Scottish economy [June 2009]

Forecasts of the Scottish economy [June 2009]

Manufacturing jobs fall in 2009 by almost 19,400 in the central scenario, with a range from 24,000 to 13,000 in the Low and High growth scenarios respectively. Within this broad sector, the most heavily hit sectors in 2009 will be those which rely upon export markets for the destination of their output, and as the forecasts for GVA declines, falls in employment are forecast in metals and non-metal products (down 2,400), mechanical engineering (down 2,000) and mining and quarrying (down 2,200). Key to the response in the labour market will be the extent to which labour hoarding occurs in the face of the recession, and large falls in employment in the first half of 2009 would be indicative of low rates of labour hoarding. This might suggest that the impact on jobs could be more significant, with employment being lower than previous historical highs for a number of years into the future.
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The Accuracy of USDA's Export Forecasts

The Accuracy of USDA's Export Forecasts

overestimate change seems counterintuitive, while a tendency to underestimate change seems plausible (β>1). The difficulty economists have in predicting turning points in the economy has been widely documented. It is not too surprising that _ models, which must be estimated with historical data, fail to anticipate changing circumstances. Also, since time ser ies are by nature strongly correlated with past values, a similar tendency for forecasts of time series data is rational. One would be suspicious of evidence that implies that USDA overanticipates events. However, some of the estimates for β in table 12 suggest USDA overestimates the amount of annual change by 100 percent. 11 This can be reconciled by more closely examining how β can vary from 1.
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Energy forecasts: some issues

Energy forecasts: some issues

The Official Forecasts for 1990 The discussion document2, Energy-Ireland, states in the chapter on energy forecasting: "The best estimates available at present indicate that total energy[r]

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A framework for evaluating epidemic forecasts

A framework for evaluating epidemic forecasts

Nsoesie et al. [16] reviewed different studies in the field of forecasting influenza outbreaks and presented the features used to evaluate the performance of proposed methods. Eleven of the sixteen forecasting methods stud- ied by the authors predicted daily/weekly case counts [16]. Some of the studies used various distance functions or errors as a measure of closeness between the predicted and observed time-series. For example, Viboud et al. [17], Aguirre and Gonzalez [18], and Jiang et al. [19] used cor- relation coefficients to calculate the accuracy of daily or weekly forecasts of influenza case counts. Other studies evaluated the precision and “closeness” of predicted activ- ities to observed values using different statistical measures of error such as root-mean-square-error (RMSE), per- centage error [19, 20], etc. However, defining a good distance function which demonstrates closeness between the surveillance and predicted epidemic curves is still a challenge. Moreover, the distance function provides a gen- eral comparison between the two time-series and ignores the epidemiological relevance between them, which are more significant and meaningful from the epidemiolo- gist perspective; these features could be better criteria to compare epidemic curves together rather than simple distance error. Cha [21] provided a survey on different distance/similarity functions for calculating the closeness between two time-series or discrete probability density functions. Some other studies have analyzed the overlap or difference between the predicted and observed weekly activities by graphical inspection [22]. Epidemic peak is one of the most important quantities of interest in an out- break, and its magnitude and timing are important from the perspective of health service providers. Consequently, accurately predicting the peak has been the goal of some
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Victoria University Employment Forecasts

Victoria University Employment Forecasts

, clerical and administrative workers, 2010-17. Source: VU model simulation .................................................................................................................................. 17 Figure 11: Average annual occupation bias, sales workers, 2010-17. Source: VU model simulation .. 18 Figure 12: Average annual change in import preference variable, 2010-17, manufacturing input-output categories. Source: VUEF model. ......................................................................................................... 19 Figure 13: Mining commodity export volumes, year-on-year percentage change, 2010-2022. Source: Commonwealth Department of Industry, 2017. .................................................................................. 20 Figure 14: Net share of workforce with new qualification, level of qualification by age and sex, average 2008-2016. Source ABS 6227.0 and author's calculations .................................................................... 21 Figure 15: Net share of workforce with new qualification, field of qualification by age and sex, average 2008-2016. Source ABS 6227.0 and author's calculations .................................................................... 22 Figure 16: Projected employment growth rates by skill, 2017-2025. Source: author's calculations ... 23 Figure 17: Projected contribution to employment growth by skill, 2017-2025. Source: author's calculations ........................................................................................................................................... 23 Figure 18: National employment by industry division (historical data is smoothed), 1992-2025. Sources: ABS (1992-2017) and VUEF model (2018-2025) .................................................................... 25 Figure 19: Employment in Division C Manufacturing, original and filtered data, 1991-2017. Source: ABS and author's calculations. ..................................................................................................................... 28 Figure 20: National employment by occupation major group (historical data is smoothed), 1992-2025. Sources: ABS (1992-2017) and VUEF model (2018-2025) .................................................................... 30 Figure 21: Employment growth rate forecasts, 2017-2025, capital cities and other regions. Source: VUEF Model .......................................................................................................................................... 31 Figure 22: Contributions to employment growth, industry and region, 2017-2025. Source: VUEF model .............................................................................................................................................................. 31
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Global warming: Forecasts by scientists versus scientific forecasts

Global warming: Forecasts by scientists versus scientific forecasts

Pilkey and Pilkey-Jarvis (2007) concluded that the long-term climate forecasts they examined were based only on the opinions of the scientists. The scientists’ opinions were expressed in complex mathematical terms without any evidence on the validity of chosen approach. The authors provided the following quotation on their page 45 to summarize their assessment: “Today’s scientists have substituted mathematics for experiments, and they wander off through equation after equation and eventually build a structure which has no relation to reality (Nikola Telsa, inventor and electrical engineer, 1934).” While it is sensible to be explicit about beliefs and to formulate these in a model, forecasters must also demonstrate that the relationships are valid.
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A Comparison of USDA's Agricultural Export Forecasts with ARIMA based Forecasts

A Comparison of USDA's Agricultural Export Forecasts with ARIMA based Forecasts

ARIMA forecasts of aggregate commodities, like Grains and feeds, are the sum of forecasts by ARIMA models for each component of the aggregate. This includes a forecast of residuals for aggregate groupings. USDA’s published forecast for Total U.S. agricultural exports is essentially a sum of its published forecasts of each component of agricultural trade. However, USDA’s forecast of Grains and feeds exports (for example) is larger than its published forecasts of specific categories of grains and feeds. Therefore, there is an implied forecast of the remaining products. Table 2 indicates that USDA’s RMSE has been at least twice as large as the error that ARIMA-based forecasting would have realized for this grains and feeds residual.
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Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Next to an assessment of performance, information on the relative importance of uncertainty sources in the forecasts is essential to improving the forecasts effectively (Yossef et al., 2013). A number of studies have reported on how errors in the meteorological forecasts and the hydrological model contribute to errors in medium-range hydrological forecasts. Demargne et al. (2010) showed that hydrological model un- certainties (model parameters, initial conditions and model structure) are most significant at short lead times. The extent depends on the streamflow category: hydrological model un- certainties significantly degrade the evaluation score up to a lead time of 7 days for all flows, whereas this is only up to a lead time of 2 days for very high-streamflow events. Ren- ner et al. (2009) found an underprediction of low forecast probabilities (few ensemble members over a high-streamflow threshold), which they attributed to the meteorological fore- casts having insufficient variability. In contrast, the high fore- cast probabilities (low threshold) are overpredicted, which Renner et al. (2009) attributed to both the hydrological model and the meteorological input data. Olsson and Lindström (2008) found an underdispersion of ensemble flood forecasts, which decreases with lead time. The meteorological forecasts and the hydrological model have a comparable contribution to this. In addition, Olsson and Lindström (2008) showed an overprediction of forecast probabilities over high thresh- olds, which they primarily attributed to the meteorological forecasts. Demirel et al. (2013a) concluded that the uncer- tainty of the hydrological model parameters has the largest effect and meteorological input uncertainty has the smallest effect on low-streamflow forecasts. Based on those studies, we can say that for high-streamflow forecasts uncertainties
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Why are survey forecasts superior to model forecasts?

Why are survey forecasts superior to model forecasts?

Survey expectations are often found to be superior to model-based forecasts, where ‘the’ survey forecast is often taken to be the median of the individual respondents’forecasts. For example, Ang, Bekaert and Wei (2007) show that surveys outperform other methods for forecasting annual in‡ation one-year ahead. 1 Ang et al. (2007, p.1207) attribute this as being likely due to a combination of ‘the pooling of large amounts of information; the e¢ cient aggregation of that information; and the ability to quickly adapt to major changes in the economic environment such as the great moderation.’ However, the quotation from Ang et al. (2007) is rather too general and would appear to be true almost by de…nition. In this paper we wish to discover what are the speci…c characteristics of survey forecasts that account for their relative superiority. Hence we begin with the median or consensus forecast, and then consider the extent to which the characteristics of these forecasts, which enhance accuracy, are also a characteristic of the individual forecasts. Or is it that the aggregation per se is instrumental in delivering the greater accuracy?
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Earnings Predictability And Broker-Analysts’ Earnings Forecast Bias

Earnings Predictability And Broker-Analysts’ Earnings Forecast Bias

Research suggests, however, that issuing intentionally optimistic earnings forecasts is not always an effective means for pleasing managers. Ke and Yu (2006) and Richardson et al. (2004) suggest that managers prefer initial forecast optimism followed by pessimistic forecasts immediately before the earnings announcements. Pessimistic forecasts and subsequently meeting or beating analysts’ earnings expectations have been linked with positive market responses (Bartov et al. 2002; Kinney, Burgstahler & Martin 2002; Kasznick & McNichols 2002; Lopez & Rees 2002; Skinner & Sloan 2002). Matsumoto (2002) argues that managers prefer pessimistic forecasts to avoid adverse market reactions to negative earnings surprises and provides evidence that firms guide analyst forecasts downward to avoid missing expectations. We argue that with more unpredictable earnings analysts will not necessarily increase their forecast optimism and they will be more susceptible to following management guidance of earnings due to the relative paucity of other information. 1 Consequently, managers will be more able and likely to guide analysts’ forecasts downward to greater pessimism. Thus, in contrast with the arguments relating low earnings predictability and analyst forecast optimism, we anticipate that unpredictable earnings will contribute to greater relative forecast pessimism for short-horizon forecasts. 2
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Do better governed firms make more informative disclosures? Canadian evidence

Do better governed firms make more informative disclosures? Canadian evidence

Many studies have examined the impact of corporate governance. For a sample of 2,106 US firms, Larcker et al. (2005) examined 39 measures of corporate governance, reducing them to 14 factors using principal components analysis. They concluded (p. 4) that corporate governance has “some ability to explain managerial decisions and firm performance and valuation.” Using the G-index to measure CGQ, Gompers et al. (2003) concluded better corporate governance was associated with superior stock returns during the 1990s. 1 Firms with strong managerial rights were found to have “significantly higher returns, were valued more highly and had better operating performance”, indicating the value of corporate governance (Gompers et al. 2003: 108). Core et al. (2006) investigated the results of Gompers et al. to determine if the market is surprised by the performance of firms with poor governance. Their results suggest this is not the case. They found analysts understand the implications of weak governance and adjust their forecasts accordingly, concluding that the results in Gompers et al. were probably sample-specific. Cremers and Nair (2005) found long term positive abnormal returns and profitability are associated with how well internal and external governance mechanisms complement each other. In particular, blockholding has an impact on stock returns, but only when firms are vulnerable to takeover. Therefore, CGQ as reflected in the G-index is only part of the governance story.
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Worst-Case Scenarios in Forecasting: How Bad Can Things Get?

Worst-Case Scenarios in Forecasting: How Bad Can Things Get?

To generate a forecast for September 2008 onwards, we need a model for car sales. I have used a conventional time-series repre- sentation using data back to 1980, a seasonal ARIMA model. The forecasts from this model are shown on Figure 2, in the form that is generated by most standard business- software packages. This shows expected sales (red line) and upper and lower bounds to the 95% prediction interval. The idea is that, in 95% of forecast months, sales should lie within these limits. Sales are forecast to be around 600 thousand cars per month, with some seasonal fluctuations. The bad case is for sales to fall below 494 thousand in September. The estimated reliability of the central forecast is reflected in its standard • Standard statistical models provide mislead-
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DEA Scores’ Confidence Intervals with Past Present and Past Present Future Based Resampling

DEA Scores’ Confidence Intervals with Past Present and Past Present Future Based Resampling

Table 13 reports 2009 forecasts by the weighted average model with Lucas weights and Table 14 shows the actuals and the forecasts of 2009 scores along with confidence intervals.. In this[r]

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How quickly do forecasters incorporate news? Evidence from cross country surveys

How quickly do forecasters incorporate news? Evidence from cross country surveys

There can be many plausible explanations for the observed difference between the US and some European countries in terms of the speed of information usage. One may be related to the structural changes that the European economies have been going through in the 1990s. In addition, the unification of Germany might have created additional uncertainty about the underlying data generating process, which can lead to stickiness in information usage. Another explanation may come not from the inefficiency of the forecasters but that of the statistical agencies processing the available information to produce GDP figures. If the data used by the forecasters are revised in a serially correlated fashion, the forecast revisions will be correlated even if the forecasters are using all the available information. Faust, Rogers and Wright (2005) found that data revisions produced by the statistical agencies of UK, Italy, and Japan are highly predictable; they are less so for the US. This implies that some part of the observed forecaster inefficiency may be due to the inefficiency of the statistical agencies. For all these reasons, the relative stickiness in some of the European forecasts may not necessarily mean that these forecasters are less skillful than their American counterparts.
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Travel Budgets – A Review of Evidence and Modelling
Implications.

Travel Budgets – A Review of Evidence and Modelling Implications.

1.2 The suggestion that forecasts of travel patterns might sensibly commence with direct forecasts of total amounts of travel to be made by different population gmups was first made by 9[r]

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