Latent factor models for large and mixed-frequency data in finance and macroeconomics

My thesis considers new latent factor models, and their estimation methodologies, suitable for settings relatively unexplored in the econometric literature as (i) a nonlinear model for the joint dynamics of a large cross-sectional distribution of asset returns, and the persistence of the ranks of the individuals inside it; (ii) approximate linear latent factor models for large panels of mixed-frequency data; and (iii) small scale state space models featuring multiple time series with stochastic volatility and observed at different frequencies. The thesis is articulated in four chapters: Chapter 1 summarizes the motivation and the objectives of the thesis, while the remaining three chapters correspond to three different articles. Chapter 2 presents a new type of asset allocation strategies based on a novel dynamic model of the cross-sectional distribution of returns and the ranks of assets inside the same cross-sectional distribution. These strategies are implemented on a large panel of US stocks, and are shown to perform well compared to traditional asset allocation strategies. Chapter 3 proposes a new class of approximate latent factor models suitable for large panels of data observed at different frequencies. An empirical application uncovers the common components of monthly data on output growth rates of the US industrial production sectors, and the yearly output growth rates of all the remaining sectors of the US economy, mainly services. Chapter 4 introduces indirect inference estimators for state space models featuring mixed frequency observables and stochastic volatility, and considers an application to forecasting quarterly European GDP using monthly macroeconomic indicators

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease

Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains

Change-point Problem and Regression: An Annotated Bibliography

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Forested Watersheds and Water Supply: Exploring Effects of Wildfires, Silviculture, and Climate Change on Downstream Waters

Drinking water supplies for much of society originate in forests. To preserve the capability of these forests to produce clean and easily treatable water, source water supply and protection strategies focus in particular on potential disturbances to the landscape, which include prescribed forest harvesting and wildfires of varying intensity. While decades of work have revealed relationships between forest harvesting and stream flow response, there is a considerable lack of synthesis disentangling the interactions of climate, wildfires, stream flow, and water quality. Revealing the mechanisms for impacts on downstream waters after disturbances of harvesting and wildfire will greatly improve land and water management. In this dissertation, I combined synthesis of previously published or available data, novel mathematical analyses, and deterministic modeling to disentangle various disturbance effects and further our understanding of processes in forested watersheds. I broadly sought to explore how streamflow and water quality change after forest disturbances, and how new methods and analyses can provide insight into the biogeochemical and ecohydrologic processes changing during disturbances. First, I examined the effect of wildfire on hydrology, and developed a novel Budyko decomposition method to separate climatic and disturbance effects on streamflow. Using a set of 17 watersheds in southern California, I showed that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect from wildfire due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The method was used to estimate hydrologic recovery timescales that varied between 5 and 45 years, with an increase of about 4 years of recovery time per 10% of the watershed burned. Next, in Chapter 3 I used a meta-analysis approach to examine the effect of wildfire on water quality, using data from 121 catchments around the world. Analyzing the changes in concentrations of stream water nutrients, including carbon, nitrogen, and phosphorus, I showed that concentrations generally increased after fire. While a large amount of variability existed in the data, we found concurrent increases in the constituents after fire highlighting tight coupling of the biogeochemical cycles. Most interestingly, we found fire to increase the concentrations of biologically active nutrients like nitrate and phosphate at a greater rate than total nitrogen and phosphorus, with median increases of 40-60% in the nitrate to TN, and SRP to TP ratios. Next, I conducted an analysis of both water quality and hydrology together after fire in Chapter 4, using a set of 29 wildfire-impacted watersheds in the United States. Concentration-discharge relationships can be used to reveal pathways and sources of elements exported from watersheds, and my overall hypothesis was that these relationships change in post-fire landscapes. I developed a new methodology, using k-means clustering, to classify watersheds as chemostatic, dilution, mobilization and chemodynamic, and explored how their position within the cluster changed in post-fire landscapes. I found that the behavior of nitrate and ammonium was increasingly chemostatic after fire, while behavior of total nitrogen, phosphorus, and organic phosphorus was increasingly mobilizing after fire. Finally, I developed a coupled hydrology-vegetation-biogeochemistry model to simulate and elucidate processes controlling the impact of harvesting on downstream waters. I focused on the Turkey Lakes watershed where a significant amount of data has been collected on vegetation and soil nutrient dynamics, in addition to traditional streamflow and water quality metrics, and developed a novel multi-part calibration process that used measured data on stream, forest, and soil characteristics and dynamics. Future work would involve using the model to explore the data driven relationships that have been developed in the earlier chapters of the paper. The work presented in this dissertation highlights new small and large-scale relationships between disturbances in forested watersheds and effects on downstream waters. With more threats predicted to escalate and overlap in the coming years, the novel results and methodologies that I have presented here should contribute to improving land and water management

Applied Econometrics

Although the theme of the monograph is primarily related to “Applied Econometrics”, there are several theoretical contributions that are associated with empirical examples, or directions in which the novel theoretical ideas might be applied. The monograph is associated with significant and novel contributions in theoretical and applied econometrics; economics; theoretical and applied financial econometrics; quantitative finance; risk; financial modeling; portfolio management; optimal hedging strategies; theoretical and applied statistics; applied time series analysis; forecasting; applied mathematics; energy economics; energy finance; tourism research; tourism finance; agricultural economics; informatics; data mining; bibliometrics; and international rankings of journals and academics

Celebrated Econometricians: Katarina Juselius and Søren Johansen

This Special Issue collects contributions related to advances in the theory and practice of Econometrics induced by the research of Katarina Juselius and Søren Johansen, whom this Special Issue aims to celebrate. The papers in this Special Issue provide advances on several topics, and they are grouped in the following areas, with three to four papers per group). The first group provides a historical perspective on Katarina’s and Søren’s contributions to Econometrics. The second group of papers concentrates on representation theory, while the third focuses on estimation and inference. The fourth group explores extensions of CVARs for modelling and forecasting, and the fifth and final group is centered on empirical applications