To understand the processes affecting the abundance of wild populations is a fundamental goal of ecology and a prerequisite for the management of living resources. Variable abundance, however, makes the investigation of ecological processes challenging. Recruitment, the process whereby new individuals enter a given stage of a ?sh population, is a highly variable entity. I have confronted this issue by developing methodologies speci?cally designed to account for, and ecologically interpret, patterns of variability in recruitment. To provide the necessary context, Chapter 2 begins with a review of the history of recruitment science. I focus on the major achievements as well as present limitations, particularly regarding environmental drivers. Approaches that include explicit environmental information are contrasted with time-varying parameter techniques. In Chapter 3, I ask what patterns of variability in pre-recruit survival can tell us about the strength of density-dependent mortality. I provide methods to investigate the presence of density-dependent mortality where this has previously been hindered by highly variable data. Stochastic density-independent variability is found to be attenuated via density dependence. Sources of recruitment variability are further partitioned in Chapter 4. Using time-varying parameter techniques, signi?cant temporal variation in the annual reproductive rate is found to have occurred in many Atlantic cod populations. Multivariate state space models suggest that populations in close proximity typically have a shared response to environmental change whereas marked differences occur across latitude. Hypotheses that could result in consistent changes in productivity of cod populations are tested in Chapter 5. I focus on a meta-analytical investigation of potential interactions between Atlantic cod and small pelagic species, testing aspects of the cultivation-depensation hypothesis. The ?ndings suggest that predation or competition by herring and mackerel on egg and larval cod could delay recovery of depleted cod populations. Chapter 6 concludes with a critical re?ection on: the suitability of the theories employed, the underlying assumptions of the empirical approaches, and the quality of the data used in my thesis. Application of ecological insights to ?sheries management is critically evaluated. I then propose future work on recruitment processes based on methods presented herein.

I detta examensarbete presenteras en ny metod för att göra schemaläggningsbeslut inom processindustrin och samtidigt ta hänsyn till det dynamiska beteendet hos processen. En modell av en luftsepareringsanläggning som producerar kvävgas och utnyttjar ett rörligt elpris användes för att exemplifiera denna metod. Modellen omfattade en kryogenisk destillationskolonn med en integrerad återloppskokare /kondensator, en multiströms värmeväxlare, en kompressor, två turbiner och en kondensator. Den innehöll 5079 ekvationer och 437 differentiella variabler. Dynamisk optimering användes för att approximera det dynamiska beteendet hos processen vid skiftningar mellan olika driftpunkter. Den registrerade data utnyttjades sedan för att identifiera en reducerad modell som fångade det transienta beteendet hos relevanta processvariabler. Den reducerade modellen bestod av 525 ekvationer och 67 differentiella variabler. Den identifierade modellen visade på god matchning mellan relevanta processvariabler i de simulerade övergångarna och den reducerade modellen. Den reducerade modellen användes för att optimera schemaläggningen av luftsepareringsprocessen så att elkostnaden över en tredagars period minimerades. De optimala resultaten visade på en minskning av kostnaden på 2.6 % jämfört med en konstant produktionstakt. Schemat implementerades och simulerades i den fullt dynamiska modellen över de första 24 timmarna för att jämföra relevanta processvariabler med den reducerade modellen. Resultaten visade på god matchning mellan de båda modellerna. Detta examensarbete visar att en exakt reducerade modell kan användas för att snabbt hitta ett optimalt schema över ett större processystem. Detta genom att kraftigt minska systemets storlek utan att offra noggrannhet av det dynamiska beteendet. / A novel framework for making plant scheduling decisions while considering the plant process dynamics is presented in this thesis. A model of an air separation unit built to supply nitrogen gas and subject to time-varying electricity prices was used to illustrate this framework. The model includes a cryogenic distillation column with an integrated reboiler/condenser, a multi-stream heat exchanger, a compressor, two turbines, and a liquefier. It consisted of 5079 equations and 437 differential variables. The dynamic behavior of the process during operating point transitions was determined using dynamic optimization. This data were used to establish a reduced order dynamic model of the system which captures the transient behavior of relevant process variables. The reduced order model consisted of 525 equations and 67 differential variables. The identified model showed a good fit between the relevant process variables in the simulated transitions and the reduced order model. The air separation unit process schedule was optimized using the reduced order model to minimize electricity cost over a three day time horizon. The optimal result showed a 2.6 % reduction in electricity cost compared to a flat production rate. The optimal schedule was implemented and simulated in the full dynamic model for the first 24 hours to compare the relevant process variables to the reduced model predictions. The result displayed good match between the reduced model and the full dynamic model. This thesis shows that an accurate reduced order dynamic model can be used for quickly finding the optimal schedule of large process systems. This by greatly reducing the size and complexity of the system without sacrificing accuracy of the dynamic behavior. Furthermore, it also shows the economic benefits of the integrating scheduling and control to count for the dynamic behavior of the system.

The paper examines the hypothesis that the devaluation of the domestic currency leads to the higher exchange rate pass-through at the zero lower bound since the interest rate channel cannot offset effects of the depreciation in that situation. Time-varying vector autoregression with stochastic volatility is used to identify the development of the pass-through. The hypothesis is tested on the Czech dataset because the Czech Republic is considered as the prototypical small open economy with inflation targeting. The assumption of higher pass-through to consumer prices at the zero lower bound is rejected. Obtained results confirm that the deprecation stimulates output growth slightly more when the interest rate is close to zero. Our estimations imply that the exchange rate commitment of the Czech National Bank increased the price level by 0.116 % and contributed to the output growth by 0.781 %.

My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

Le premier chapitre consiste en une brève revue de littérature dont les éléments sont repris dans les différentes introductions des études empiriques proposées dans la suite de la thèse. L'objet de cet état des lieux est de fixer le cadre général des analyses macro-économétriques opérées dans la thèse. Ce cadre nous permet d'une part, d'envisager une adéquate intégration des anticipations des agents économiques dans le raisonnement ayant mené aux modèles keynésiens actuels et d'autre part, d'effectuer des estimations des principales versions de la courbe de Phillips introduites dans la littérature macro-économique post-seconde guerre mondiale. Dans cette optique, la thèse est constituée de trois études empiriques. Dans la première de ces études, nous nous plaçons au sein d'un cadre uni-varié et tentons de discriminer entre plusieurs spécifications, proposant différentes caractérisations économétriques de la dynamique du taux d'inflation U.S. Essentiellement, trois types de spécifications, théoriquement associés à trois évolutions possibles du taux d'inflation espéré (anticipé), sont mis à l'épreuve. Les résultats de cette première étude montrent que la dynamique du taux d'inflation peut être pertinemment décrite à l'aide d'un modèle à changements de (trois) régimes markoviens dans les dérives (Intercepts) d'un processus autorégressif (d'ordre deux), soit le modèle MSI(3)-AR(2). La deuxième étude s'opère dans le cadre multi-varié d'une Nouvelle Courbe de Phillips Keynésienne à Inflation tendancielle Positive (NKPC-PI). Au sein de ce cadre, la relation d'arbitrage Inflation/Activité réelle est estimée suivant une procédure en deux étapes. Dans la première, nous identifions des régimes distincts du taux d'inflation U.S. à l'aide d'un modèle à changements de (trois) régimes markoviens dans les dérives d'un processus vectoriel autorégressif (d'ordre deux), soit le modèle MSI(3)-VAR(2). Dans la seconde étape, nous estimons les paramètres structurels de cette économie keynésienne afin d'extraire la courbe de Phillips résultante des changements de régimes initialement identifiés. Les résultats de cette deuxième étude nous amènent à conclure à une non-négligeable instabilité de la courbe de Phillips au cours de la période post-seconde guerre mondiale. La troisième étude se présente comme un prolongement et/ou un approfondissement des deux premières. Aussi, dans sa première partie, nous revenons sur les dynamiques tendancielles individuelles des quatre variables intervenant dans le cadre de modélisation NKPC-PI. Les résultats issus de ces premières estimations en contextes uni-variés montrent que seule la dynamique du taux d'inflation et, dans une moindre mesure, celle du coût marginal réel semble obéir à des changements de régimes. La spécification retenue pour l'inflation est celle de la première étude (MSI(3)-AR(2)), tandis que la dynamique du coût marginal réel pourrait être approchée à l'aide d'un modèle à changements de (deux) régimes dans les dérives d'un processus autorégressif (d'ordre deux), soit le modèle MSI(2)-AR(2). Les dynamiques du taux d'actualisation nominal et du taux de croissance de l'output (les deux autres variables du modèle NKPC-PI) semblent, quant à elles, être assez bien caractérisées par des spécifications linéaires autorégressives à deux retards (AR(2)). Sur la base de ces premiers résultats, nous estimons, dans la deuxième partie de l'étude, la nouvelle courbe de Phillips keynésienne en considérant que les processus générateurs des quatre séries du modèle peuvent répondre à de possibles intégrations fractionnelles. Les résultats de ces dernières estimations montrent que la prise en compte simultanée des changements de régimes et de la longue mémoire dans les dynamiques des variables du modèle apporte certains éclairages sur l'évolution du débat mené autour de la relation d'arbitrage post-seconde guerre mondiale. / This PhD thesis proposes, through her three articles, a macro-econometric framework of integrating, in the most adequate way to our sense, the expectations of the economic agents in the reasoning having led to current New-Keynesian models. Upon this specified frame of analysis, we evaluate the effectiveness of various versions of the Phillips curve introduced into the macroeconomic literature. The first study of this thesis takes place in a univariate context and we seek to determine an econometric model leading to best characterize the U.S inflation rate dynamic. In order to achieve this, three types of specifications, associated with three possible evolutions of the expected rate are considered. The first allows an overall instability of the trend or the expected inflation rate. The second considers an alternative specification in which the expected inflation rate is unstable in periodic segments of the sample. Finally, the last specification allows instability of a "mixed type" in which the trend inflation rate is assumed to be random or subject to a probability schema. The results of our study indicate that this last specification is the one that gives the most adequate characterization of the inflation rate dynamic. The inflation rate then appears generated by a second order autoregressive process with, on the one hand, unchanging lag coefficients and, on the other, an unconditional mean which switch between three global regimes of different frequencies of accession. Based on these first results, we extend the analysis in a multivariate framework. The main topics of the second paper are to challenge the rational nature of the agents expectations and the structural effectiveness of the behaviorally micro-based New Keynesian Phillips Curve with a Positive steady state Inflation (NKPC-PI). We then model the trade-off between the U.S inflation rate and a Unit Labor Cost-based measure of the real activity through Markov Switching - Vectorial AutoRegressive (MS-VAR) specifications. These specifications allow to adequately capturing the rationality in the agents expectations process as they underlie a finite number of expected inflation rate regimes, which highlight the agents adaptive beliefs on the achievements of these regimes. Moreover, the results confirm the structural stability of the NKPC-PI over the inflation rate regimes as its deep parameters seem to be unaffected by the regimes switching (Cogley & Sbordone (2005) and Groen & Mumtaz (2008)). In the third study, we extend the analysis of the Phillips curve trade-off. First, we look at determining econometrics models leading to characterize the dynamics of all the variables underlying the trade-off in univariate contexts. As a result, it appears that an adequate way to characterize the agents expectations regarding the dynamics of these variables is to consider a combination of some fixed levels (regimes) in the variables evolutions with an agents adaptive beliefs notion. Finally, based on the implied expectations values of the variables, we show that the Phillips curve seems to disappear when the impact of the expected inflation rate on its current value converges to its long-term value.

Les incertitudes épistémiques peuvent être réduites via des études supplémentaires (mesures labo, in situ, ou modélisations numériques, etc.). Nous nous concentrons ici sur celle "paramétrique" liée aux difficultés à évaluer quantitativement les paramètres d’entrée du modèle utilisé pour l’analyse des aléas géotechniques. Une stratégie de gestion possible est l’analyse de sensibilité, qui consiste à identifier la contribution (i.e. l’importance) des paramètres dans l’incertitude de l’évaluation de l’aléa. Des approches avancées existent pour conduire une telle analyse. Toutefois, leur application au domaine des aléas géotechniques se confronte à plusieurs contraintes : 1. le coût calculatoire des modèles numériques (plusieurs heures voire jours) ; 2. les paramètres sont souvent des fonctions complexes du temps et de l’espace ; 3. les données sont souvent limitées, imprécises voire vagues. Dans cette thèse, nous avons testé et adapté des outils statistiques pour surmonter ces limites. Une attention toute particulière a été portée sur le test de faisabilité de ces procédures et sur la confrontation à des cas réels (aléas naturels liés aux séismes, cavités et glissements de terrain) / Importance ranking of parameter uncertainties in geo-hazard assessments Epistemic uncertainty can be reduced via additional lab or in site measurements or additional numerical simulations. We focused here on parameter uncertainty: this corresponds to the incomplete knowledge of the correct setting of the input parameters (like values of soil properties) of the model supporting the geo-hazard assessment. A possible option tomanage it is via sensitivity analysis, which aims at identifying the contribution (i.e. the importance) of the different input parameters in the uncertainty on the final hazard outcome. For this purpose, advanced techniques exist, namely variance-basedglobal sensitivity analysis. Yet, their practical implementation faces three major limitations related to the specificities of the geo-hazard domain: 1. the large computation time cost (several hours if not days) of numerical models; 2. the parameters are complex functions of time and space; 3. data are often scarce, limited if not vague. In the present PhD thesis, statistical approaches were developed, tested and adapted to overcome those limits. A special attention was paid to test the feasibility of those statistical tools by confronting them to real cases (natural hazards related to earthquakes, cavities and landslides)

La tesi di dottorato, composta da tre capitoli, si concentra sulla valutazione delle dinamiche di inflazione in Georgia e sulla previsione dei tassi di cambio nominali per i Paesi della European Eastern Partnership attraverso l’utilizzo di moderne tecniche econometriche. Nel primo capitolo, abbiamo svolto un’indagine sui modelli di previsione dei tassi di cambio e dell’inflazione. Questa indagine rivela che i modelli “factor-based and time-varying parameter” generano migliori previsioni rispetto ad altri modelli. Nel secondo capitolo, abbiamo approfondito le dinamiche di inflazione in Georgia utilizzando la New Keynesian Phillips Curve ibrida, inserita all’interno di un quadro di un modello “time-varying parameter (TVP)”. Una stima del modello TVP con volatilità stocastica mostra la persistenza di un’inflazione bassa durante il periodo 1996-2012. Un’analisi più approfondita dal 2003 mostra una volatilità crescente dell’inflazione. Inoltre, le stime del parametro evidenziano che la componente forward-looking del modello è importante a seguito dell’adozione di inflation targeting da parte della NBG a partire dal 2009. Nel terzo capitolo, abbiamo costruito dei modelli fattoriali, “Factor Vector Autoregressive” per prevedere i tassi di cambio nominali per i Paesi dell’European Eastern Partnership. Questi modelli prevedono meglio i tassi di cambio nominali rispetto ad un processo naïve come il random walk. / The Ph.D. thesis consist of three chapters on evaluating inflation dynamics in Georgia and modeling and forecasting nominal exchange rates for the European Eastern Partnership (EaP) countries using modern applied econometric techniques. In the first chapter, we survey of models those produce high predictive powers for forecasting exchange rates and inflation. This survey reveals that the factor-based and time-varying parameter (TVP) models generate superior forecasts relative to all other models. In the second chapter, we study inflation dynamics in Georgia using a hybrid New Keynesian Phillips Curve (NKPC) nested within a time-varying parameter (TVP) framework. Estimation of a TVP model with stochastic volatility shows low inflation persistence over the entire time span (1996-2012), while revealing increasing volatility of inflation shocks since 2003. Moreover, parameter estimates point to the forward-looking component of the model gaining importance following the National Bank of Georgia (NBG) adoption of inflation targeting in 2009. In the third chapter, we construct Factor Vector Autoregressive (FVAR) models to forecast nominal exchange rates for the EaP countries. This study provides better forecasts of nominal exchange rates than those produced by the random walk process.

Stavové modely jsou neobyčejně užitečné v mnoha inženýrských a vědeckých oblastech. Jejich atraktivita vychází především z toho faktu, že poskytují obecný nástroj pro popis široké škály dynamických systémů reálného světa. Nicméně, z důvodu jejich obecnosti, přidružené úlohy inference parametrů a stavů jsou ve většině praktických situacích nepoddajné. Tato dizertační práce uvažuje dvě zvláště důležité třídy nelineárních a ne-Gaussovských stavových modelů: podmíněně konjugované stavové modely a Markovsky přepínající nelineární modely. Hlavní rys těchto modelů spočívá v tom, že---navzdory jejich nepoddajnosti---obsahují poddajnou podstrukturu. Nepoddajná část požaduje abychom využily aproximační techniky. Monte Carlo výpočetní metody představují teoreticky a prakticky dobře etablovaný nástroj pro řešení tohoto problému. Výhoda těchto modelů spočívá v tom, že poddajná část může být využita pro zvýšení efektivity Monte Carlo metod tím, že se uchýlíme k Rao-Blackwellizaci. Konkrétně, tato doktorská práce navrhuje dva Rao-Blackwellizované částicové filtry pro identifikaci buďto statických anebo časově proměnných parametrů v podmíněně konjugovaných stavových modelech. Kromě toho, tato práce adoptuje nedávnou particle Markov chain Monte Carlo metodologii pro návrh Rao-Blackwellizovaných částicových Gibbsových jader pro vyhlazování stavů v Markovsky přepínajících nelineárních modelech. Tyto jádra jsou posléze použity pro inferenci parametrů metodou maximální věrohodnosti v uvažovaných modelech. Výsledné experimenty demonstrují, že navržené algoritmy překonávají příbuzné techniky ve smyslu přesnosti odhadu a výpočetního času.