Potatoes are a capital-intensive crop. A farmer who is considering expanding his potato acreage must carefully consider revenue requirements to offset the high costs of raising the crop. A method to forecast annual farm potato prices would be useful not only to the farmer, who is considering potato acreage expansion (or contraction), but also to the potato buyers. Seven forecasting models were considered: (1) a simultaneous equation model (with five equations); (2) a Box-Jenkins type ARIMA model; (3) an exponential smoothing model; (4) a moving-ave rage model; (5) a trend model; (6) an "opposite" model; and (7) a current. or naive, model. The results reveal the following three things: (I) The "best" model was the trend model. This model gave the most accurate one-period out-of-sample forecasts of the models tested (as measured by the mean absolute error (MAE), the root mean squared error (RMSE), and Theil's U2 statistics). The simultaneous equation model could be considered as the next best model. (2) The forecast for the average Utah farm potato price for 1992 was about $5.40 per cwt. (3) The average Utah farm potato price for 1993 should be in the $5.51 to $5.95 range (the forecasts from the trend and simultaneous equation models, respectively).

In this thesis, the usefulness of search engine data to nowcast the unemployment rate of Sweden is evaluated. Four different indices from Google Trends based on keywords related  to unemployment are used in the analysis and six different regARIMA models are  estimated and evaluated. The results indicate that the fit is improved for models when data  from Google Trends is included. To evaluate the nowcast ability of models, one-step-ahead  predictions are calculated. Although the prediction error is lower for the models with data  from Google Trends, Diebold-Mariano tests do not indicate that the predictions are  significantly better compared topredictions from a model without data from Google Trends.  It is therefore concluded that one cannot state that data from Google Trends improves  nowcasts of the unemployment rate of Sweden. Additionally, predictions are calculated for  longer forecast horizons. This analysis indicates that Google search data could be useful to  forecast the unemployment rate of longerforecast horizons.

This research will contribute to the literature of managerial ability and analyst following as well as narrative disclosure in the following ways. This study is the first to investigate the association between managerial ability and external information intermediaries such as financial analysts to the best of our knowledge. Most of the earlier studies on managerial ability focus on firms’ internal information environment such as operating and financial decisions, and limited studies examine the relation between managerial ability with external perception of the information environment and narrative disclosures. We extend this literature by examining how managerial ability impacts the firm's external information environment, affecting informational intermediaries' work processes, such as financial analysts. We find that managers' higher ability leads to better performance by financial analysts regarding their forecast error, dispersion, and willingness to provide coverage on the firm. We also step further by employing more advanced and novel measures to assess managerial ability's impact on market intermediaries’ external work and perception. Able managers impact reporting informativeness, response time, and the uncertainty of the forecasts from financial analysts. Further, we examine informational channels or mediators (i.e., analyst following and readability of narrative disclosure), highlighting how managerial ability can be linked the better performance by financial analysts. We intend to show how variables like disclosure readability and analyst following mediate between managerial ability and analyst forecast properties (i.e., error and Dispersion). In the last part of the research, we answer how analysts' better performance can be a channel to help able managers increase their firms' value (i.e., analyst’s forecast error acts as the channel from the managerial ability to firm’s performance).

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Reliability is one of the properties that customers of heavy trucks value highest.Dependent on all parts and functions of the vehicle, reliability is a complexproperty, which can normally be measured only towards the end of a developmentproject. At earlier development stages, forecasts can give valuable decision supportfor project planning.The main function of a heavy truck is to transport goods, but the truck also hasinteractive functions as the working environment of the driver. Interactivefunctions are functions experienced by the driver. They are subjective, in the senseof being person dependent, so that a system can be experienced as inadequate byone user but satisfactory by another. Examples of interactive functions of heavytrucks are climate comfort and ergonomics, which are experienced differently bydifferent drivers. Failures of these functions lead to costs and limited availabilityfor the customer. Therefore it is important to include them in reliability forecasts.The work described in this thesis concerns some elements of the system reliabilityforecast. Two studies are presented, one proposing a qualitative systemarchitecture model and the other reviewing and testing methods for evaluating theimpact of varying operating conditions. Two case studies of a truck cab in a systemreliability test were made. The first case study shows that the system architecturemodel supports reliability forecasts by including interactive functions as well asexternal factors, human and environmental, which affect function performance.The second case study shows that modelling uncertainty is crucial for interactivefunctions and recommends a method to forecast function performance while takingvarying operating conditions into account. / <p>QC 20170602</p>

This thesis provides a model for forecasting coal production rates in southwest Virginia. A multiple linear regression model is developed for the forecasting process. The model includes six independent variables: Virginia coal price times Virginia coal mining productivity (x₁), Virginia mining company production levels (x₂), Virginia coal reserves (x₃), U.S. domestic electricity consumption (x₄), U.S. coal exports (x₅), and U.S. domestic industrial (includes coke) coal consumption (x₆). Historical values for the six variables from 1979 to 1993 were used in generating the multiple linear regression model coefficients. The model captures 86.87% of the variation in Virginia coal production over this period. The forecast from 1994 to 2010 was generated by using forecasted values for the six independent variables. The sensitivity of the model was tested by slightly changing the values of selected independent variables. The results indicate a decline in Virginia coal production to approximately 32 million tons in 2010. Under more favorable conditions, the model results indicate that the coal production levels will remain approximately steady. Under less favorable economic conditions, the model results indicate that Virginia coal production levels will be reduced by more than 50% by the year 2010. These results were also supported by a curve fitting exercise based on the work of M. King Hubbert (1969 and 1973). Hubbert states that the production of a non-renewable natural resource follows a normal or lognormal curve. The peak production level will occur when approximately have of the reserves have been mined or slightly before. Approximately 2 billion tons of coal has been mined in southwest Virginia. Current recoverable reserves are estimated at 1.5 billion tons. / Master of Science

Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040. / Master of Science