One critical step to successfully integrate wireless data networks to the high-speed wired backbone is the design of network control policies that efficiently utilize resources to provide Quality of Service (QoS) to the users in the integrated networks. Such a design has remained a challenge since wireless networks are time-varying in nature, not only in terms of user/packet arrivals but also in terms of physical channel conditions and access opportunities. In this thesis, we study the stochastic control of time-varying networks to design efficient scheduling and resource allocation policies. In particular, in Chapter 3, we focus on a broad class of control policies that work based on a pick-and-compare principle for networks with time-varying channels. By trading the throughput for complexity and memory requirement, these policies require less complexity compared to the well-investigated throughput-optimal Generalized Maximum Weight Matching (GMWM) policy and also require only linear-memory storage with the number of data-flows. Through Lyapunov analysis tools, we characterize the stability region and delay performance of the studied policies and show how they vary in response to the channel variations. In Chapter 4, we go into further detail and consider the problem of network control from a new perspective through which we carefully incorporate the time-efficiency of underlying scheduling algorithms. Specifically, we develop a policy that dynamically adjusts the time given to the available scheduling algorithms according to queue-backlog and channel correlations. We study the resulting stability region of developed policy and show that the region is at least as large as the one for any static policy. Finally, motivated by the current under-utilization of wireless spectrum, in Chapter 5, we investigate the control of cognitive radio networks as a special example of networks that provide time-varying access opportunities. We assume that users dynamically join and leave the network and may have different utility functions, or could collaborate for a common purpose. We develop a policy that performs joint admission and resource control and works for any user load, either inside or outside the capacity region. Through Lyapunov Optimization techniques, we show that the developed policy can achieve a utility performance arbitrarily close to the optimality with a tradeoff in the average service delay of admitted users.

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.

Gas chromatography (GC) is an analytical chemistry tool used to determine the chemical composition of a gas sample by separating sample analytes as they travel through a GC column. Recent efforts have been made to understand and control gas chromatography separations with a negative thermal gradient on the column. The present work presents results from thermal gradient GC separations on two GC columns in different configurations (serpentine and radial) in a stainless-steel plate. Methods to fabricate the GC systems capable of isothermal, temperature programmed and thermal gradient separations are presented. Isothermal experimental data from the serpentine column were used to fit retention and dispersion parameters in a transport model that simulates GC separation for hydrocarbons C12-C14. Transport model simulated retention times and peak widths matched experimental values well for isothermal, temperature programmed and thermal gradient separations. The validated transport model was used to study the effect of static (not varying temporally) thermal gradients on GC separations with varying injection widths, injection band shapes and stationary phase thickness. Resolution results from different heating conditions were considered comparable if retention times for each analyte were within 5%. An optimal, static thermal gradient is shown to reduce analyte band spreading from axially-varying velocity gradients with resolution improvements over isothermal separations of up to 8% for analytes with similar retention factors. Static thermal gradients have a larger effect on fronting peak shape than tailing peak shape. Stationary phase distribution acts similar to a velocity gradient and can be corrected by a thermal gradient. Another transport model was created from isothermal experimental data on a commercial column for hydrocarbons C12-C20. An optimal, static thermal gradient does not improve resolution for all analyte pairs. An optimal, dynamic (varying tempo-rally) thermal gradient is created by uniformly increasing the temperature on an optimal, static thermal gradient. Improvements in resolution of up to 20% are achievable over temperature programmed GC separation. A dynamic thermal gradient can also correct for a poor sample injection by creating a temperature trap at the beginning of the column.

In this paper we propose a time-varying parameter VAR model for the housing market in the United States, the United Kingdom, Japan and the Euro Area. For these four economies, we answer the following research questions: (i) How can we evaluate the stance of monetary policy when the policy rate hits the zero lower bound? (ii) Can developments in the housing market still be explained by policy measures adopted by central banks? (iii) Did central banks succeed in mitigating the detrimental impact of the financial crisis on selected housing variables? We analyze the relationship between unconventional monetary policy and the housing markets by using the shadow interest rate estimated by Krippner (2013b). Our findings suggest that the monetary policy transmission mechanism to the housing market has not changed with the implementation of quantitative easing or forward guidance, and central banks can affect the composition of an investors portfolio through investment in housing. A counterfactual exercise provides some evidence that unconventional monetary policy has been particularly successful in dampening the consequences of the financial crisis on housing markets in the United States, while the effects are more muted in the other countries considered in this study. (authors' abstract) / Series: Department of Economics Working Paper Series

A large literature has related the failure of interest rate parity in the foreign exchange market to the existence of a time-varying risk premium. Nevertheless, most modern open economy DSGE models imply a (near) perfect interest rate parity condition. This paper presents a stylized two-country incomplete-markets model in which countries have strong precautionary motives because they face international liquidity constraints, the presence of which successfully generates a time-varying risk premium: the country that has accumulated debt after experiencing relative worse times has stronger precautionary motives and its asset carries a risk premium. (author's abstract) / Series: Department of Economics Working Paper Series

This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.

In this study, we examine the dynamic relationship between tourism growth and economic growth, using a newly introduced spillover index approach. Based on monthly data for 10 European countries over the period 1995-2012, our analysis reveals the following empirical regularities. First, the tourism-economic growth relationship is not stable over time in terms of both magnitude and direction, indicating that the tourism-led economic growth (TLEG) and the economic-driven tourism growth (EDTG) hypotheses are time-dependent. Second, the aforementioned relationship is also highly economic event-dependent, as it is influenced by the Great Recession of 2007 and the ongoing Eurozone debt crisis that began in 2010. Finally, the impact of these economic events is more pronounced in Cyprus, Greece, Portugal and Spain, which are the European countries that have witnessed the greatest economic downturn since 2009. Plausible explanations of these results are provided and policy implications are drawn. (authors' abstract)

<p>Travel demand management policies such as congestion charges encourage car-users to change among other things route, mode and departure time. Departure time may be especially affected by time-varying charges, since car-users can avoid high peak hour charges by travelling earlier or later, so called peak spreading effects. Conventional transport models do not include departure time choice as a response. For evaluation of time-varying congestion charges departure time choice is essential.</p><p>In this thesis a transport model called SILVESTER is implemented for Stockholm. It includes departure time, mode and route choice. Morning trips, commuting as well as other trips, are modelled and time is discretized into fifteen-minute time periods. This way peak spreading effects can be analysed. The implementation is made around an existing route choice model called CONTRAM, for which a Stockholm network already exists. The CONTRAM network has been in use for a long time in Stockholm and an origin-destination matrix calibrated against local traffic counts and travel times guarantee local credibility. On the demand side, an earlier developed departure time and mode choice model of mixed logit type is used. It was estimated on CONTRAM travel times to be consistent with the route choice model. The behavioural response under time-varying congestion charges was estimated from a hypothetical study conducted in Stockholm.</p><p>Paper I describes the implementation of SILVESTER. The paper shows model structure, how model run time was reduced and tests of convergence. As regards run time, a 75% cut down was achieved by reducing the number of origin-destination pairs while not changing travel time and distance distributions too much.</p><p>In Paper II car-users underlying preferred departure times are derived using a method called reverse engineering. This method derives preferred departure times that reproduce as well as possible the observed travel pattern of the base year. Reverse engineering has previously only been used on small example road networks. Paper II shows that application of reverse engineering to a real-life road network is possible and gives reasonable results.</p> / Silvester

The intensive care unit treats the most critically ill patients in the hospital, and as such the clinical staff in the intensive care unit have to deal with complex, time-sensitive and life-critical situations. Commonly, patients present with multiple organ dysfunctions, require breathing and cardiovascular support, which make diagnosis and treatment even more challenging. As a result, clinical staff are faced with processing large quantities of often confusing information, and have to rely on experience and trial and error. This occurs despite the wealth of cardiovascular metrics that are available to the clinician. Computer models of the cardiovascular system can help enormously in an intensive care setting, as they can take the monitored data, and aggregate it in such a way as to present a clear and understandable picture of the cardiovascular system. With additional help that such systems can provide, diagnosis can be more accurate and arrived at faster, alone with better optimised treatment that can start sooner, all of which results in decreased mortality, length of stay and cost. This thesis presents a model of the cardiovascular system, which mimics a specific patient’s cardiovascular state, based on only metrics that are commonly measured in an intensive care setting. This intentional limitation gives rise to additional complexities and challenges in identifying the model, but do not stand in the way of achieving a model that can represent and track all the important cardiovascular dynamics of a specific patient. One important complication that comes from limiting the data set is need for an estimation for the ventricular time-varying elastance waveform. This waveform is central to the dynamics of the cardiovascular model and is far too invasive to measure in an intensive care setting. This thesis thus goes on to present a method in which the value-normalised ventricular time-varying elastance is estimated from only metrics which are commonly available in an intensive care setting. Both the left and the right ventricular time-varying elastance are estimated with good accuracy, capturing both the shape and timing through the progress of pulmonary embolism and septic shock. For pulmonary embolism, with the algorithm built from septic shock data, a time-varying elastance waveform with median error of 1.26% and 2.52% results for the left and right ventricles respectively. For septic shock, with the algorithm built from pulmonary embolism data, a time-varying elastance waveform with median error of 2.54% and 2.90% results for the left and right ventricles respectively. These results give confidence that the method will generalise to a wider set of cardiovascular dysfunctions. Furthermore, once the ventricular time-varying elastance is known, or estimated to a adequate degree of accuracy, the time-varying elastance can be used in its own right to access valuable information about the state of the cardiovascular system. Due to the centrality and energetic nature of the time-varying elastance waveform, much of the state of the cardiovascular system can be found within the waveform itself. In this manner this thesis presents three important metrics which can help a clinician distinguish between, and track the progress of, the cardiovascular dysfunctions of pulmonary embolism and septic shock, from estimations based of the monitored pressure waveforms. With these three metrics, a clinician can increase or decrease their probabilistic measure of pulmonary embolism and septic shock.

Mathematical models of physical systems are pervasive in engineering. These models can be used to analyze properties of the system, to simulate the system, or synthesize controllers. However, many of these models are too complex or too large for standard analysis and synthesis methods to be applicable. Hence, there is a need to reduce the complexity of models. In this thesis, techniques for reducing complexity of large linear time-invariant (lti) state-space models and linear parameter-varying (lpv) models are presented. Additionally, a method for synthesizing controllers is also presented. The methods in this thesis all revolve around a system theoretical measure called the H2-norm, and the minimization of this norm using nonlinear optimization. Since the optimization problems rapidly grow large, significant effort is spent on understanding and exploiting the inherent structures available in the problems to reduce the computational complexity when performing the optimization. The first part of the thesis addresses the classical model-reduction problem of lti state-space models. Various H2 problems are formulated and solved using the proposed structure-exploiting nonlinear optimization technique. The standard problem formulation is extended to incorporate also frequency-weighted problems and norms defined on finite frequency intervals, both for continuous and discrete-time models. Additionally, a regularization-based method to account for uncertainty in data is explored. Several examples reveal that the method is highly competitive with alternative approaches. Techniques for finding lpv models from data, and reducing the complexity of lpv models are presented. The basic ideas introduced in the first part of the thesis are extended to the lpv case, once again covering a range of different setups. lpv models are commonly used for analysis and synthesis of controllers, but the efficiency of these methods depends highly on a particular algebraic structure in the lpv models. A method to account for and derive models suitable for controller synthesis is proposed. Many of the methods are thoroughly tested on a realistic modeling problem arising in the design and flight clearance of an Airbus aircraft model. Finally, output-feedback H2 controller synthesis for lpv models is addressed by generalizing the ideas and methods used for modeling. One of the ideas here is to skip the lpv modeling phase before creating the controller, and instead synthesize the controller directly from the data, which classically would have been used to generate a model to be used in the controller synthesis problem. The method specializes to standard output-feedback H2 controller synthesis in the lti case, and favorable comparisons with alternative state-of-the-art implementations are presented.