This dissertation extends previous studies on firms' differential earnings response coefficients. It provides further theoretical explanation and empirical evidence for the differential earnings response coefficients across firms and time. The empirical evidence found by Ball & Brown (1968) that the sign of unexpected earnings is positively correlated with the sign of market reactions is used to improve the control of measurement errors on investors' prior belief. Revisions of financial analysts' forecasts (FAFs) for firms' future earnings per share (EPS) are used as the event information. Both the impact of FAFs quality on investors' earnings belief revision and the mapping from EPS to security price are considered. Investors are assumed to be Bayesians who are homogeneous in belief. They use FAFs as information for making portfolio investment decisions. FAFs with smaller contemporary dispersion relative to the variance of investors' prior belief are considered to have higher quality. It is proposed that investors have stronger faith on the forecasts with higher information quality. A non-normative approach is used to map EPS into security prices. The market price over (expected) earnings ratio (P/E) is used as a linear approximation for the security valuation function. The major advantage of this approach is that non-earnings factors that have price effect on securities are implicitly controlled. The model predicts that ceteris paribus, the earnings response coefficient adjusted for the differential P/E is positively correlated with the quality of FAFs. Cross-sectional and time series samples of 1097 FAFs revisions from Standard & Poor's Earnings Forecaster in the years 1981 to 1985 are used in the empirical test. The empirical results are consistent with the theoretical implication. The quality of FAFs is found to be positively correlated with the P/E adjusted earnings response coefficient at one percent significance level. The results are robust across event day windows, the estimation periods for market model parameters and the price reaction measurements.

Three empirical studies are conducted examining the efficiency of S&P 500 futures prices and the pricing of these futures contracts. In the first study, the ability of futures prices to predict the realized spot S&P 500 index prices on the expiration date is examined for near term contracts. The futures prices are found to be unbiased predictors of the realized spot index prices for the nineteen quarterly contracts from 1982 to 1986. Previous studies report significant deviations in S&P SOO futures prices from theoretically determined Cost of Carry Model (CCM) prices. In the second study, it is found that the CCM using the federal funds rate, a proxy for the overnight repurchase rate, provides relatively better estimates of the S&P S(x) futures prices over the 1984-1986 period. The futures mispricing also reflects the weekend effect anomaly: futures prices are "over-priced" relative to CCM prices on Mondays, whereas the opposite occurs on Fridays. The futures over-pricing (under-pricing) is characterized by "bull" ("bear") financial markets and the extent of price changes are relatively greater in the futures market. The futures under-pricing is supported by strong future market volume and open-interest positions. The basis and changes in it over the futures contract period are measures of how well integrated the futures market and the underlying spot market are. In the third study, based on daily closing prices for the S&P 500 index and index futures for the 1984-1986 period, it is found that the basis decreases over the contract period but the rate of decrease is independent of the time to expiration. The change in basis on Mondays is generally positive which also reflects the weekend effect anomaly. The daily basis is negative on 107 days, which generally occurs during strong futures market trading volume and open interest positions. It is doubtful whether the negative basis can be attributed to a negative net financing cost, where the dividend yield 0.1 the spot index exceeds the cost of financing the spot index forward.

Long-term tree-ring series of Cedrus atlantica have been utilized to reconstruct and characterize past drought occurrences in Morocco. Based on the close relationship between tree growth and rainfall variations, the best tree-ring indicator of drought has been found to be, x₀ = μ -.75σ, with μ and σ the mean and standard deviation of the tree-ring series. In Morocco, drought is a short event lasting 1.7 years on average with 84% of the events lasting less than 2 years. The average maximum duration exceeds 4 years. It is associated with a 40% deficit about normal moisture availability which can add up to more than 125% when the anomalies persist. The average empirical recurrence interval of drought is 8 years. Long-term persistence has been characterized by the Hurst coefficient. Over the domain of study, the mean Hurst coefficient is .765. The tree-ring series show a periodic behavior with two major peaks at 22 and 66 years. Drought characteristics have been extended from the tree-ring sites to other regions such as the agricultural plains using geostatistical methods. The drought prone areas are located in central Morocco, including the Sais, the Gharb and central Middle Atlas, which experience longer, more severe and more persistent droughts. The northern latitudes experience milder and shorter drought events. The cost for spatial extension of the results was greater kriging errors which make the estimates less reliable. However, cokriging significantly improved the reliability and the quality of the estimates. A regionalization, using cluster analysis, was found to follow the natural climatic and orographic zones of north central Morocco. The tree-ring data were used to reconstruct spatial patterns of moisture anomalies for the period 1845-1974. Three atmospheric factors appear to be important: (1) the relative strength and position of the Azores High, (2) the importance and location of the local cyclogenesis centers, (3) the importance of the northeastern perturbations coming from the Mediterranean sea. The periods 1860-1890, 1925-1950 and the 1970's are associated with dry anomalies while the periods 1900-1920 and 1950-1970 are associated with wet anomalies. They exhibit a rhythmic succession with a 20-year periodicity.

In this thesis I study two fields of empirical finance: market integration and economic forecasting. The first two chapters focus on studying regional integration of Mexican and U.S. equity markets. In the third chapter, I propose the use of the daily term structure of interest rates to forecast inflation. Each chapter is a free-standing essay that constitutes a contribution to the field of empirical finance and economic forecasting. In Chapter 1, I study the ability of multi-factor asset pricing models to explain the unconditional and conditional cross-section of expected returns in Mexico. Two sets of factors, local and foreign factors, are evaluated consistent with the hypotheses of segmentation and of integration of the international finance literature. Only one variable, the Mexican U.S. exchange rate, appears in the list of both foreign and local factors. Empirical evidence suggests that the foreign factors do a better job explaining the cross-section of returns in Mexico in both the unconditional and conditional versions of the model. This evidence provides some suggestive support for the hypothesis of integration of the Mexican stock exchange to the U.S. market. In Chapter 2, I study further the integration between Mexico and U.S. equity markets. Based on the result from chapter 1, I assume that the Fama and French factors are the mimicking portfolios of the underlying risk factors in both countries. Market integration implies the same prices of risk in both countries. I evaluate the performance of the asset pricing model under the hypothesis of segmentation (country dependent risk rewards) and integration over the 1990-2004 period. The results indicate a higher degree of integration at the end of the sample period. However, the degree of integration exhibits wide swings that are related to both local and global events. At the same time, the limitations that arise in empirical asset pricing methodologies with emerging market data are evident. The data set is short in length, has missing observations, and includes data from thinly traded securities. Finally, Chapter 3, coauthored with John Maheu and Alex Maynard, studies the ability of daily spreads at different maturities to forecast inflation. Many pricing models imply that nominal interest rates contain information on inflation expectations. This has lead to a large empirical literature that investigates the use of interest rates as predictors of future inflation. Most of these focus on the Fisher hypothesis in which the interest rate maturity matches the inflation horizon. In general, forecast improvements have been modest. Rather than use only monthly interest rates that match the maturity of inflation, this chapter advocates using the whole term structure of daily interest rates and their lagged values to forecast monthly inflation. Principle component methods are employed to combine information from interest rates across both the term structure and time series dimensions. Robust forecasting improvements are found as compared to the Fisher hypothesis and autoregressive benchmarks.

Wind power is one of the most promising renewable energy resources to replace conventional generation which carries high carbon footprints. Due to the abundance of wind and its relatively cheap installation costs, it is likely that wind power will become the most important energy resource in the near future. The successful development of wind power relies heavily on the ability to integrate wind power effciently into electricity grids. To optimize the value of wind power through careful power dispatches, techniques in forecasting the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. As wind is intermittent and wind turbines have non-linear power curves, this is a challenging task and many ongoing studies relate to the topic of wind power forecasting. For this reason, this thesis aims at contributing to the literature on wind power forecasting by constructing and analyzing various time series models and spatiotemporal models for wind power production. By exploring the key features of a portfolio of wind power data from Ireland and Denmark, we investigate different types of appropriate models. For instance, we develop anisotropic spatiotemporal correlation models to account for the propagation of weather fronts. We also develop twostage models to accommodate the probability masses that occur in wind power distributions due to chains of zeros. We apply the models to generate multi-step probability forecasts for both the individual and aggregated wind power using extensive data sets from Ireland and Denmark. From the evaluation of probability forecasts, valuable insights are obtained and deeper understanding of the strengths of various models could be applied to improve wind power forecasts in the future.

Five statistical and dynamical tropical cyclone intensity guidance techniques available at the National Hurricane Center during the 2003 and 2004 Atlantic and Eastern North Pacific seasons were evaluated within three intensity phases: (I) formation; (II early intensification; and (III) decay. During the formation phase, the Decay Statistical Hurricane Intensity Prediction (DSHIPS) technique was the best technique in both basins. When the forecast errors during formation exceed +/- 10 kt, the statistical techniques tend to over-forecast and the dynamical models tend to under-forecast. Whereas DSHIPS was also the best technique in the Atlantic during the early intensification stage, the Geophysical Fluid Dynamics Laboratory model was the best in the Eastern North Pacific. All techniques under-forecast periods of rapid intensification and the peak intensity, and have an overall poor performance during decay-reintensification cycles in both basins. Whereas the DSHIPS was the best technique in the Atlantic during decay, none of the techniques excelled during the decay phase in the eastern North Pacific. All techniques tend to decay the tropical cyclones in both basins too slowly, except that the DSHIPS performed well (13 of 15) during rapid decay events in the Atlantic. Similar error characteristics had been found in the western North Pacific.

TRACT (maximum 200 words) The Northern Oscillation Index (NOI), an atmospheric climate index relating climate variations in the tropical Pacific and Northeast Pacific was used to selectively average output from the Parallel Ocean Climate Model (POCM 4C) for 1979-1998. Composites, or smart climatologies, were made representing El Nino (EN) and La Nina (LN) conditions, as well as a long term mean (LTM) average or traditional climatology, for November to March. Conditions in the California Current System (CCS) in the smart climatologies were consistent with large scale features noted in previously published studies of EN and LN. Overall, the patterns of anomalies (POCM 4C Smart Climatology minus POCM 4C Traditional Climatology) in salinity, temperature, and currents were opposite in sign and magnitude between the EN and LN composites. This was expected for opposite phases of the same climate variation, and many of the model's EN/LN differences were found to be statistically significant. Therefore, POCM 4C smart climatologies provide better estimates of ocean state and circulation patterns than traditional climatology. Such smart climatologies offer improved environmental information to Naval operational and strategic planners. They are also useful for studying climate variations, and in improving boundary and initial conditions for ocean and atmosphere models.

Extended Range Munitions (ERMs) are gun-launched rocket-boosted munitions having an effective range ocer 27 km. In accordance with Sea power 21 and the Marine Corps's requirements for sea-based fire support, three ERMs are being developed. The purpose of this work is to increase the range and lethality of these munitions by applying environmental effects when computing the projectiles' trajectory. A broad review of artillery and munitions literature reveals that historically 66% of ballistic error can be attributed to meteorological factors. The most important factors are wind (speed and direction), temperature, and pressure. It has also been shown that global atmospheric numerical weather presictions (NWP) data typically outperforms the traditional radiosonde data and is suitable for use in ballistic corrections. Forecasted NWP products provided by the Fleet Numerical Meteorology and Oceanographic Center (FNMOC) are integrated using the Joint Meteorology and Oceanographic (METOC) Broker Language (JMBL) into a Five Degree of Freedom (5DOF) aerodynamic model within the Autonomous Unmanned Vehicle (AUV) Workbench producing a ballistic correction (BALCOR) for the munition. This new capability can significantly enhance naval gunfire effectiveness since the BALCOR increase the munitions' range and the ability apply kinetic energy onto the target rather than using it to maneuver to the target.

Based on recent advances, skilled objectively-determined probabilistic forecasts of some weather phenomena may be provided to operational decision-makers. Objective probabilistic forecasts that are generated from ensemble prediction systems (EPS) are attractive as a forecast methodology for Department of Defense (DoD) applications for three reasons: first, atmospheric scientists understand that the atmosphere has a limit of predictability, which means that traditional deterministic forecasts lack important uncertainty information; second, it has been demonstrated that quantifying uncertainty may improve a weather forecast user's ability to make a better decision based on their own utility function, which translates to better operational risk management (ORM) for the DoD; and finally, progress points towards a future with machine-to-machine warfare. These assertions are examined by applying probabilistic forecasts from an ensemble-based aircraft-scale turbulence forecast system to several cases and scenarios. Results clearly demonstrate the advantage of using ensemble-based probabilistic forecasts versus deterministic forecasts. Additionally, application of ensemble-based probabilistic forecast information to DoD operations is shown to be possible through its ORM programs. Specifically, air refueling scenarios are identified that demonstrate the integration of probabilistic turbulence forecast guidance into the U.S. Air Force ORM process.

Accurately forecasting future personnel inventory levels by rank and occupational specialty is a fundamental prerequisite for development of an effective and functional staffing plan. This thesis develops and evaluates univariate time series models to create six- and twelve-month forecasts of Marine Corps enlisted manpower levels. Models are developed for 44 representative population groups using Holt-Winters exponential smoothing, multiplicative decomposition, and Box-Jenkins autoregressive integrated moving average (ARIMA) forecasting methods. The forecasts are evaluated against actual, out-of-sample inventory levels using several goodness-of-fit indicators including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Sum of Squared Errors (SSE). Among the modeling techniques evaluated, the multiplicative decomposition performed the best overall and represents an improvement over the Marine Corps' current forecasting method. This thesis recommends Marine Corps Systems Command, Total Force Information Technology Systems develop and introduce a multiplicative decomposition forecasting model into the Enlisted Staffing Goal Model. This forecasting technique should be implemented in phases, starting with the E-1 through E-4 population groups.