The future is uncertain. We therefore make predictions and forecasts of the future in order to be able to plan and react to future events. For this purpose, financial analysts are argued to have a responsibility towards investors and the market, in helping to keep the market efficient. Given that financial analysts act in a rational way we argue that analysts should strive to maximize forecast accuracy. The purpose of this study is to investigate how accurate financial analysts’ forecasts of Swedish firms’ future values are, and what information that analysts use that significantly affect the analysts’ forecast accuracy. To investigate this we first examine whether financial analysts contribute with value to investors by comparing their forecast precision against a simple time-series model. Our findings show that financial analysts produce significantly more accurate forecasts than a time-series model in the short term. Furthermore, given that rational analysts act in their own best interest while making accurate forecasts, we argue that analysts will incorporate and use the information that is available to them for the purpose of maximizing forecast accuracy. We investigate this by testing if the analysts’ forecast accuracy is affected by; the forecast horizon, the number of analysts following a firm, the firm size, the corporate visibility, the predictability of earnings, and trading volume. We find that the forecast accuracy is better when the amount of analysts following a firm is high, the firm size is larger, the forecasted company’s corporate visibility in the news is more frequent, and the predictability of earnings is higher. The trading volume does not have a significant effect on analysts’ forecast accuracy. To conclude, we question the value of financial analysts’ forecasts for longer forecast horizons.

In an effort to assess the predictive ability of exchange rate models when data on African countries is sampled, this paper studies nonlinear modelling and prediction of the nominal exchange rate series of the United States dollar to currencies of thirty-eight African states using the smooth transition autoregressive (STAR) model. A three step analysis is undertaken. One, it investigates nonlinearity in all nominal exchange rate series examined using a chain of credible statistical in-sample tests. Significantly, evidence of nonlinear exponential STAR (ESTAR) dynamics is detected across all series. Two, linear models are provided another chance to make it right by shuffling to data on African countries to investigate their predictive power against the tough random walk without drift model. Linear models again failed significantly. Lastly, the predictive ability of nonlinear models against both the random walk without drift and the corresponding linear models is investigated. Nonlinear models display useful forecasting gains over all contending models.

In this study, neutral real interest rate gap and output gap are estimated jointly under two different multivariate unobserved components models with the motivation to provide empirical measures that can be used to analyze the amount of stimulus that monetary policy is passing on to the economy, and to understand historical macroeconomic developments. In the analyses, Kalman filter technique is applied to a small-scale macroeconomic model of the Turkish economy to estimate the unobserved variables for the period 1989-2005. In addition, two alternative specifications for neutral real interest rate are used in the analyses. The first model uses a random walk model for the neutral real interest rate, whereas the second one employs more structural specification, which specifically links the neutral real rate with the trend growth rate and the long-term course of the risk premium. Comparison of the models developed by using various performance criteria clearly indicates the use of more structural specification against random walk specification. Results suggest that though there is relatively high uncertainty surrounding the neutral real interest rate estimates to use them directly in the policy-making process, estimates appear to be very useful for ex-post monetary policy evaluations.

No description available.

This thesis aims to increase our understanding of mechanisms that influence larval dispersal in marine benthic invertebrates, particularly in the absence of strong oceanographic features (e.g. estuarine plumes, upwelling events, or markedly different water masses). Laboratory experiments identified behavioural mechanisms that regulate the vertical distribution of larvae in response to thermal stratification, and field studies in St. George’s Bay, Nova Scotia (NS), Canada, examined the relationship between larval abundance and physical variables (temperature, salinity, fluorescence, etc) and identified mechanisms that regulate larval distributions in situ. In the laboratory, I demonstrated that thermal stratification affects the vertical distribution of larvae by acting as a barrier to migration, or through temperature-dependent vertical swimming velocities. I also developed a random walk based model which highlighted that the key to successfully simulating larval response to temperature was 1) determining the temperature-dependent distribution of vertical swimming velocities and 2) the temporal autocorrelation in these velocities. In the field, the most striking pattern was that the larval distributions for species with similar swimming abilities were significantly correlated to one another at all scales (0.5 to 40 km). This suggests a common mechanism, related to larval swimming ability, which greatly influences the horizontal larval distribution. I found that the spatial scale of variability in larval distributions (~ 3 km) matches that in both the environmental variables and of coherent structures in current velocities (i.e. the tidal excursion). Results from an aggregation-diffusion model suggest that horizontal larval swimming could not be responsible for the observed level of aggregation in the larval horizontal distributions. I suggest that these horizontal patterns are the result of 1) an aggregative process (i.e. larvae swimming against a vertical current and maintaining their vertical position) and 2) a diffusive process which scales the aggregations to the scale of the coherent structures in current velocity (i.e. tidal excursion). In conclusion, this thesis increases our understanding of larval behaviour and its effects on larval dispersal. The results will be particularly useful to those who are interested in mechanisms regulate population connectivity, particularly those using bio-physical models to model dispersal trajectories.

The key objective of this thesis is the outline of an alternative capital market modeling framework, the Volatility Asset Pricing Model, VAPM, inspired by the innovative dual approach of Mandelbrot and Hudson using the method based on synthesis of two seemingly antagonistic factors -- the volatility of market prices and their serial dependence determining the capital markets' dynamics. The pilot tests of this model in various periods using the market index as well as a portfolio of selected securities delivered generally satisfactory results. Firstly, the work delivers a brief recapitulation regarding the concepts of a consumer/investor choice under general conditions of hypothetical certainty. Secondly, this outline is then followed by a description of the "classical" methodologies in the risky environment of uncertainty, with assessment of their corresponding key models, i.e. the CAPM, SIM, MIM, APTM, etc., notwithstanding results of the related testing approaches. Thirdly, this assessment is based on evaluation of the underlying doctrine of Efficient Market Hypothesis in relation to the so called Random Walk Model. Fourthly, in this context the work also offers a brief exposure to a few selected tests of these contraversial concepts. Fifthly, the main points of conteporary approaches such as the Fractal Dimension and the Hurst Exponent in the dynamic framework of information entropy are subsequently described as the theoretical tools leading to development of the abovementioned model VAPM. The major contribution of this thesis is considered its attempt to apply the abovementioned concepts in practice, with the intention to possibly inspire a further analytical research.

本文主要是研究浮動匯率期間匯率波動對出口產業的影響。一般認為，匯率波動匯會使出口廠商的利潤風險增加，所以波動對於出口量的影響是為負的效果。不過，由於許多國外的研究的結果並不一定支持這種看法。本文針對台灣1984到1995年的資料進行實證研究，並且分別就不同出口產業對匯率波動的反應程度做討論，包括紡織類，塑膠化學類，電子類，機械類及基本金屬類五種產業，主要分為兩個架構分析： 　　（一）衡量匯率波動因子：對於匯率波動的衡量分成兩種方法：一種是以過去匯率變動的方式來衡量，另一種是以本期匯率預測的誤差來衡量，大部份的文獻都是採用前者。在此，為了將廠商事先避險的行為引入，所以採用後者的方法，將預測到的波動與未預測到的波動分離開來。 　　（二）匯率波動對各產業出口量的影響：將所有符合I(1)性質的變數用Johansen的方法做長期共整合關析的估計，再利用Granger Representation Theorem導出短期誤差修正模型，並將符合I(0)性質的波動因子引入模型當中，以便觀察匯率波動對出口量的影響。結果發現，各產業的出口量皆與匯率波動間存在明顯的負相關，其中以電子產業的影響最顯著，紡織類次之，基本金屬類影響最小，根據產品的特性分析可發現：當出口競爭愈激烈者，或是出口彈性愈大者，相對來講，會對匯率波動的反應較敏感。

本篇論文擴充Ang et al. (2007)之基本架構,分別建構台灣各式月資料與季資料的物價指數預測模型,並進行預測以及實證分析。我們用以衡量通貨膨脹率的指標為 CPI 年增率與核心CPI 年增率。我們比較貨幣模型、成本加成模型、6 種不同設定的菲力浦曲線模型、3 種期限結構模型、隨機漫步模型、 AO 模型、ARIMA 模型、VAR 模型、主計處(DGBAS)、中經院(CIER) 及台經院(TIER) 之預測。藉由此研究,我們可以完整評估出文獻上常用之各式月資料及季資料預測模型的優劣。 我們實證結果顯示,在月資料預測模型樣本外預測績效表現方面, ARIMA 模 型對 2 種通貨膨脹率指標的樣本外預測能力表現最好。至於季資料預測模型樣本外預測績效表現, ARIMA 模型對未來核心 CPI 年增率的樣本外預測能力表現最好; 然而,對於 CPI 年增率為預測目標的預測模型則不存在最佳的模型。此外,實證分析中我們也發現本研究所建構的模型預測表現仍遜於主計處的預測,但部份模型的樣本外預測能力表現則比中經院與台經院的預測為佳。 / This paper compares the forecasting performance of inflation in Taiwan. We conduct various inflation forecasting methods (models) for two inflation measures(CPI growth rate and core-CPI growth rate) by using monthly and quarterly data. Besides the models of Ang et al. (2007), we also consider some macroeconomic models for comparison. We compare some Monetary models, Mark-up models, six variants of Phillips curve models, three variants of term structure models, a Random walk model, an AO model, an ARIMA model, and a VAR model. We also compare the forecast ability of these model with three different survey forecasts (the DGBAS, CIER, and TIER surveys). We summarized our findings as follows. The best monthly forecasting model for both inflation measures is ARIMA model. For quarterly core-CPI inflation, ARIMA model is also the best model; however, when comparing the quarterly forecasts for CPI inflation, there does not exist the best one. Besides, we also found that the DGBAS survey outperforms all of our forecasting methods/models, but some of our forecasting models are better than the CIER and TIER surveys in terms of MAE.

Dans le cadre des recherches de la qualité des ressources en eau, l’étude du processus de transfert de masse du sol vers la nappe phréatique constitue un élément primordial pour la compréhension de la pollution de cette dernière. En effet, les éléments polluants solubles à la surface (produits liés aux activités humaines tels engrais, pesticides...) peuvent transiter vers la nappe à travers le milieu poreux qu’est le sol. Ce scénario de transfert de pollution repose sur deux phénomènes : la pluie qui génère la masse d’eau à la surface et la dispersion de celle-ci à travers le milieu poreux. La dispersion de masse dans un milieu poreux naturel comme le sol forme un sujet de recherche vaste et difficile aussi bien au plan expérimental que théorique. Sa modélisation constitue une préoccupation du laboratoire EMMAH, en particulier dans le cadre du projet Sol Virtuel dans lequel un modèle de transfert (modèle PASTIS) a été développé. Le couplage de ce modèle de transfert avec en entrée un modèle décrivant la dynamique aléatoire de la pluie est un des objectifs de la présente thèse. Ce travail de thèse aborde cet objectif en s’appuyant d’une part sur des résultats d’observations expérimentaux et d’autre part sur de la modélisation inspirée par l’analyse des données d’observation. La première partie du travail est consacrée à l’élaboration d’un modèle stochastique de pluie. Le choix et la nature du modèle sont basés sur les caractéristiques obtenus à partir de l’analyse de données de hauteur de pluie recueillies sur 40 ans (1968-2008) sur le Centre de Recherche de l’INRA d’Avignon. Pour cela, la représentation cumulée des précipitations sera assimilée à une marche aléatoire dans laquelle les sauts et les temps d’attente entre les sauts sont respectivement les amplitudes et les durées aléatoires entre deux occurrences d’événements de pluie. Ainsi, la loi de probabilité des sauts (loi log-normale) et celle des temps d’attente entre les sauts (loi alpha-stable) sont obtenus en analysant les lois de probabilité des amplitudes et des occurrences des événements de pluie. Nous montrons alors que ce modèle de marche aléatoire tend vers un mouvement brownien géométrique subordonné en temps (quand les pas d’espace et de temps de la marche tendent simultanément vers zéro tout en gardant un rapport constant) dont la loi de densité de probabilité est régie par une équation de Fokker Planck fractionnaire (FFPE). Deux approches sont ensuite utilisées pour la mise en œuvre du modèle. La première approche est de type stochastique et repose sur le lien existant entre le processus stochastique issu de l’équation différentielle d’Itô et la FFPE. La deuxième approche utilise une résolution numérique directe par discrétisation de la FFPE. Conformément à l’objectif principal de la thèse, la seconde partie du travail est consacrée à l’analyse de la contribution de la pluie aux fluctuations de la nappe phréatique. Cette analyse est faite sur la base de deux relevés simultanées d’observations de hauteurs de pluie et de la nappe phréatique sur 14 mois (février 2005-mars 2006). Une étude statistique des liens entre les signaux de pluie et de fluctuations de la nappe est menée comme suit : Les données de variations de hauteur de nappe sont analysées et traitées pour isoler les fluctuations cohérentes avec les événements de pluie. Par ailleurs, afin de tenir compte de la dispersion de masse dans le sol, le transport de la masse d’eau pluviale dans le sol sera modélisé par un code de calcul de transfert (modèle PASTIS) auquel nous appliquons en entrée les données de hauteurs de pluie mesurées. Les résultats du modèle permettent entre autre d’estimer l’état hydrique du sol à une profondeur donnée (ici fixée à 1.6m). Une étude de la corrélation entre cet état hydrique et les fluctuations de la nappe sera ensuite effectuée en complément à celle décrite ci-dessus pour illustrer la possibilité de modéliser l’impact de la pluie sur les fluctuations de la nappe / Within the research quality of water resources, the study of the process of mass transfer from soil to groundwater is a key element for understanding the pollution of the latter. Indeed, soluble contaminants to the surface (related to human activities such fertilizers, pesticides products ...) can transit to the web through the porous medium that is the ground. This scenario transfer pollution based on two phenomena: the rain that generates the body of water to the dispersion and the surface thereof through the porous medium. The dispersion of mass in a natural porous medium such as soil forms a subject of extensive research and difficult both experimental and theoretical grounds. Its modeling is a concern EMMAH laboratory, particularly in the context of Virtual Sol project in which a transfer model (PASTIS model) was developed. The coupling of this transfer model with input a model describing the dynamics of random rain is one of the objectives of this thesis. This thesis addresses this goal by relying in part on the results of experimental observations and also on modeling inspired by the analysis of observational data. The first part of the work is devoted to the development of a stochastic model of rain. The choice and nature of the model are based on the features obtained from the analysis of data collected rainfall over 40 years (1968-2008) on the Research Centre INRA Avignon. For this, the cumulative rainfall representation will be treated as a random walk in which the jumps and waiting times between jumps are the amplitudes and durations between two random occurrences of rain events. Thus, the probability jumps (log-normal distribution) and that of waiting between jumps (Law alpha-stable) time is obtained by analyzing the laws of probability amplitudes and occurrences of rain events. We show that the random walk model tends towards a subordinate in time geometric Brownian motion (when space step and time step walking simultaneously tend to zero while maintaining a constant ratio), the law of probability density is governed by a Fokker Planck fractional (FFPE). Two approaches are then used to implement the model. The first approach is based on stochastic type and the relationship between the stochastic process derived from the differential equation of Itô and FFPE. The second approach uses a direct numerical solution by discretization of the FFPE. Accordance with the main objective of the thesis, the second part of the work is devoted to the analysis of the contribution of rain to fluctuations in groundwater. We approach this analysis on the basis of two simultaneous records of observations of rainfall amounts and groundwater over 14 months (February 2005-March 2006). A statistical study of the relationship between the signals of rain and fluctuating water will be conducted. Data sheet height variations are analyzed and processed to isolate coherent fluctuations with rain events. In addition, to take account of the mass dispersion in the soil, the mass transport of storm water in the soil layer is modeled by a calculation code transfer (PASTIS model) which we apply input data measured heights of rain. The model results allow between another estimate soil water status at a given depth (here set at 1.6m). A study of the correlation between the water status and fluctuating water will then be performed in addition to that described above to illustrate the ability to model the impact of rain on the water table fluctuations

The thesis is aimed on measurement and modeling of MEMS gyroscopes based on input-output characteristics. The first part briefs the state of the art. The second part is dedicated to measurement methodology. Critical points and sources of uncertainty are discussed and evaluated using measurements or simulations. The last part shows key characteristics of MEMS gyroscopes based on the survey of a group of different sensor types. The results have revealed significant influence of supply voltage that causes bias drift of the gyroscope and bias drift of the internal temperature sensor. The error can be comparable to temperature drift; however, this effect is not addressed in the literature. The second observed effect is temperature dependency of angle random walk. In the last part, a general model of a MEMS gyroscope is rewritten to reflect observed effects. Moreover, the structure is selected to be easily extendable and the coefficients are expressed to allow a comparison of nominal parameters of different sensors.