This paper examines the forecast accuracy of an unrestricted Vector Autoregressive (VAR) model for GDP, relative to a comparable Vector Error Correction (VEC) model that recognizes that the data is characterized by co-integration. In addition, an alternative forecast method, Intercept Correction (IC), is considered for further comparison. Recursive out-of-sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VEC models consistently outperform the VAR models. Further, IC enhances the forecast accuracy when applied to the VEC model, while there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the VEC IC model compared to the VAR model.

Hurricane Katrina of 2005 was responsible for at least 81 billion dollars of property damage. In planning for such emergencies, society must decide whether to invest in the ability to evacuate more speedily or in improved forecasting technology to better predict the timing and intensity of the critical event. To address this need, we use dynamic programming and Markov processes to model the interaction between the emergency response system and the emergency forecasting system. Simulating changes in the speed of evacuation and in the accuracy of forecasting allows the determination of an optimal mix of these two investments. The model shows that the evacuation improvement and the forecast improvement give different patterns of impact to their benefit. In addition, it shows that the optimal investment decision changes by the budget and the feasible range of improvement.

In this paper, the forecast performance of an unrestricted Vector Autoregressive (VAR) model was compared against the forecast accuracy of a Vector error correction (VECM) model when computing out-of-sample forecasts for Swedish exports. The co-integrating relation used to estimate the error correction specification was based upon an economic theory for international trade suggesting that a long run equilibrium relation among the variables included in an export demand equation should exist. The results obtained provide evidence of a long run equilibrium relationship between the Swedish export volume and its main determinants. The models were estimated for manufactured goods using quarterly data for the period 1975-1999 and once estimated, the models were used to compute out-of-sample forecasts up to four-, eight- and twelve-quarters ahead for the Swedish export volume using both multi-step and one-step ahead forecast techniques. The main results suggest that the differences in forecasting ability between the two models are small, however according to the relevant evaluation criteria the unrestricted VAR model in general yields somewhat better forecast than the VECM model when forecasting Swedish exports over the chosen forecast horizons.

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本研究以I/B/E/S中2004至2005年所有美國公司為樣本，依財務分析師盈餘預測值與所有財務分析師盈餘預測平均值之差異程度，將財務分析師分類為大膽或膽怯的財務分析師，並依財務分析師盈餘預測發布之時點將財務分析師分類為領導型或從眾型財務分析師。針對財務分析師盈餘預測行為大膽及領導特性，探討時效性領導型財務分析師之盈餘預測是否會較大膽，並進一步研究大膽的財務分析師其盈餘預測準確度是否較高，以及時效性之領導型財務分析師其盈餘預測準確度是否較高。 研究結果發現時效性之領導型財務分析師其盈餘預測行為會較大膽，但大膽的財務分析師其盈餘預測準確度較低，且時效性之領導型財務分析師其盈餘預測準確度並未出現較高的現象。 / Security analysts can be characterized as bold or herding based on the absolute distance between their earnings forecasts and the consensus forecast. Security analysts can also be classified as lead or following based on the timeliness of their earnings forecasts. Based on I/B/E/S annual earnings forecasts of all American companies during the period of 2004-2005, this study addresses the association between bold and lead and the relation between bold forecast and forecast accuracy. In addition, the relation between lead forecast and forecast accuracy is investigated as well. It is shown that lead analysts are bolder than following analysts and boldness likelihood increases with the frequency of analysts’ forecast and declines with the analysts’ prior accuracy. Further, bold analysts’ earnings forecasts are less accurate than herding analysts’ and lead analysts’ earnings forecasts are less accurate than following analysts.

Transportation infrastructure improvement projects are typically huge and have significant economic and environmental effects. Forecasts of demand of the facility in the form of traffic level help size the project as well as choose between several alternatives. Inaccuracy in these forecasts can thus have a great impact on the efficiency of the operational design and the benefits accrued from the project against the cost. Despite this understanding, evaluation of traffic forecast inaccuracy has been too few, especially for un-tolled roads in the United States. This study, part of a National Cooperative Highway Research Program (NCHRP) funded project, bridges this gap in knowledge by analyzing the historical inaccuracy of the traffic forecasts based on a database created as part of the project. The results show a general over-prediction of traffic with actual traffic deviating from forecast by about 17.29% on an average. The study also compares the relative accuracy of forecasts on several categorical variables. Besides enumerating the error in forecasts, this exploration presents the potential factors influencing accuracy. The results from this analysis can help create an uncertainty window around the forecast based on the explanatory variables, which can be an alternate risk analysis technique to sensitivity testing.

Defining ambiguity as investor's uncertainty about the precision of the observed information, Chapter One constructs an empirical measure of ambiguity based on analysts' earnings forecast information, and finds that the market tends to react more negatively to highly ambiguous bad news, while it tends to be less responsive to highly ambiguous good news. This result supports the theoretical argument of Epstein and Schneider (2003, 2008) that ambiguity-averse investors take a worst-case assessment of the information precision, when they are uncertain about the information precision. In addition, Chapter One shows that returns on stocks exposed to highly ambiguous and intangible information are more negatively skewed. Chapter Two finds that certain traders are informed about either the forthcoming analysts' forecasts or long-term value of the stock, and informed traders prefer to use medium-size trades to exploit their private information advantage. Specifically, medium-size trade imbalance prior to the forecast announcements is positively correlated with the nature of forecast revisions, while in the days immediately after the forecasts medium-size trade imbalance is positively correlated with future stock returns for up to four months. Small-size trade imbalance is also positively correlated with future returns but only following downward revisions. In contrast, it is also shown that large trades placed right after the forecasts are unprofitable and generate slightly negative profits in the long run. Overall, our results are consistent with the "stealth trading hypothesis" proposed by Barclay and Warner (1993).

I explore whether participation on Estimize.com, a crowdsourced earnings-forecasting platform aimed primarily at novices, improves professional analysts’ forecast accuracy and career outcomes. Estimize provides its contributors with frequent and timely feedback on their forecast performance and offers them a new channel for disseminating their forecasts to a wider public, features that could help analysts improve their forecast accuracy and raise their online visibility. Using proprietary data obtained from Estimize and a difference-in-differences research design, I find that IBES analysts who are active on Estimize improve their EPS forecast accuracy by 13% relative to the sample-mean forecast error, as well as reduce forecast bias. These improvements in performance vary predictably in ways consistent with learning through feedback. Additionally, I find increased market reaction to the positive earnings-forecasts revisions issued by analysts who are active on Estimize. I also find that analysts active on Estimize enjoy incremental positive career outcomes after controlling for forecast accuracy. My results suggest that professional analysts can learn to become better forecasters through online feedback and consequently garner more attention from the market. My results also suggest analysts can improve their career outcomes by gaining additional online visibility. / Business Administration/Accounting