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We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time- varying forecast uncertainty and risk for the real price of oil over the period 1974–2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.

There are few computational and methodological tools available for the analysis of general multi-state models with interval censoring. Here, we propose a general framework for parametric inference with interval censored multi-state data. Our framework can accommodate any parametric model for the transition times, and covariates may be included in various ways. We present a general method for constructing the likelihood, which we have implemented in a ready-to-use R package, smms, available on GitHub. The R package also computes the required high-dimensional integrals in an efficient manner. Further, we explore connections between our modelling framework and existing approaches: our models fall under the class of semi-Markovian multi-state models, but with a different, and sparser parameterisation than what is often seen. We illustrate our framework through a dataset monitoring heart transplant patients. Finally, we investigate the effect of some forms of misspecification of the model assumptions through simulations.

Shale oil producers respond positively and significantly to favourable oil price signals. This finding is established using a novel proprietary data set consisting of more than 200,000 shale wells across ten U.S. states spanning almost two decades. We document large heterogeneity in the estimated responses across the various shale wells, suggesting that aggregation bias is an important issue for this kind of analysis. We find responses to be stronger for the largest oil producing firms, among wells that are spaced further apart and in regions where the density of shale wells is higher. The response also depend on the level of production. Our empirical results calls for new models that can account for a growing share of shale oil in the U.S., the inherent flexibility of shale extraction technology in production and the role of shale oil in transmitting oil price shocks to the U.S. economy.

Today, public areas across the globe are monitored by an increasing amount of surveillance cameras. This widespread usage has presented an ever-growing volume of data that cannot realistically be examined in real-time. Therefore, efforts to understand crowd dynamics have brought light to automatic systems for the detection of anomalies in crowds. This thesis explores the methods used across literature for this purpose, with a focus on those fusing dense optical flow in a feature extraction stage to the crowd anomaly detection problem. To this extent, five different deep learning architectures are trained using optical flow maps estimated by three deep learning-based techniques. More specifically, a 2D convolutional network, a 3D convolutional network, and LSTM-based convolutional recurrent network, a pre-trained variant of the latter, and a ConvLSTM-based autoencoder is trained using both regular frames and optical flow maps estimated by LiteFlowNet3, RAFT, and GMA on the UCSD Pedestrian 1 dataset. The experimental results have shown that while prone to overfitting, the use of optical flow maps may improve the performance of supervised spatio-temporal architectures.

We assess the importance of residential investment for the prediction of economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1–2014Q4. Our approach is to estimate various probit models with different leading indicators and evaluate their relative prediction accuracies using the area under the receiver operating characteristic curve as our forecasting performance metric. We document that residential investment contains information that is useful for predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer confidence surveys and oil prices. It is shown that residential investment is particularly useful for the prediction of recessions for countries with high home-ownership rates. Finally, in a separate exercise for the US, we show that the predictive ability of residential investment is — in a broad sense — robust to employing real-time data.

Formation of solid CO2 hydrates during geosequestration of CO2 may offer increased storage capacity and provide extra sealing in cold aquifers. Evaluation of the integrity and distribution of the formed hydrate seal requires comprehensive knowledge about the CO2 flow and hydrate growth pattern within sedimentary pores. We address this knowledge gap by pore- to core-scale visualization of hydrate formation during liquid CO2 injection in water-filled sandstone (P = 70 bar, T = 1–2 °C). Pore-level hydrate growth is analyzed by direct imaging of the pore space in a micromodel chip that is an analog to the pore network in a sandstone. Flow visualization of CO2-water drainage followed by hydrate formation is also presented at core-scale in Bentheim sandstone using high-field MR imaging. The image results verified hydrate nucleation both at the liquid-liquid interface and in the water phase alone due to the presence of dissolved CO2. The pore-filling hydrate growth pattern effectively reduced the sandstone permeability and showed the potential of CO2 hydrate as a sealing mechanism during CO2 sequestration.

We evaluate the impact of mortgage regulation on child and parent household balance sheets, highlighting important trade-offs in terms of financial vulnerability. Using Norwegian tax data, we show that loan-to-value caps reduce house purchase probabilities, debt and interest expenses – thereby improving household solvency. Moreover, parents of first-time buyers also reduce their debt uptake, suggesting that concerns about regulatory arbitrage are unwarranted. However, the higher downpayment requirement also leads to a persistent deterioration of household liquidity. We show that this reduction in liquid buffers coincides with larger house sale propensities given unemployment, as households become more vulnerable to adverse income shocks.

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.

Do inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global market for crude oil? We answer this question with a novel structural vector autoregressive model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through the real price of oil to both expected and realized inflation. Our main insight is that US households form their expectations of inflation differently when faced with long sustained increases in the price of oil, such as the early millennium oil price surge of 2003 to 2008, as compared to short and sharp price fluctuations that characterized much of the twentieth century. We also find that oil demand and supply shocks can explain a large proportion of expected and realized inflation dynamics during multiple periods of economic significance, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

Describing progression of a disease or the life history of an individual with multi-state models has been a topic of interest for many years. A challenge with these studies is that the data are often not continuously observed, i.e. the transition times are not recorded precisely and therefore interval-censored. The aim of this thesis is to introduce modeling of transition times as the threshold crossing times for Gamma processes in multi-state models for interval-censored data. To make this possible, we construct a suitable likelihood framework, where we set up a general likelihood for the three-state progressive model, the illness-death model, the four-state progressive model and a four-state illness-death model. The likelihood framework we create is general, meaning the transition times can be modeled by any parametric survival model. The fitting of our parametric models and the large-sample properties of the maximum likelihood estimates are also investigated using simulated data. Another central theme in this thesis is the Markov property. Multi-state models with interval-censored data often rely on the Markov property, and we therefore investigate the Markov property in our model framework. By calculating the transition probabilities, we prove that our model framework does not necessarily rely on the Markov property. For example, when we model the transition times as the threshold crossing times for Gamma processes, the Markov property does not hold. However, if the transition times are exponentially distributed, the Markov property is satisfied and we end up with a homogeneous Markov model. For application purposes, we consider a dataset on CAV (coronary allograft vasculopathy), a post-transplant complication. The disease progression of CAV is described with a four-state illness-death model. We model the transition times as the threshold crossing times for Gamma processes, and calculate the maximum likelihood estimates. In the end, we compare our results to homogeneous and inhomogeneous Markov models, both with and without covariates. Our findings indicate that the models with Gamma processes are preferred over both the homogeneous and inhomogeneous Markov models. This holds both with and without covariates.

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.

Do inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global market for crude oil? We answer this question with a novel structural vector autoregressive model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through the real price of oil to both expected and realized inflation. Our main insight is that US households form their expectations of inflation differently when faced with long sustained increases in the price of oil, such as the early millennium oil price surge of 2003 to 2008, as compared to short and sharp price fluctuations that characterized much of the twentieth century. We also find that oil demand and supply shocks can explain a large proportion of expected and realized inflation dynamics during multiple periods of economic significance, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of the first quarter of 2020, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic.

Recent developments in US house prices mirror those of the 1996–2006 boom, but the recovery in construction activity has been weak. Using data for 254 US metropolitan areas, we show that housing supply elasticities have fallen markedly in recent years. Consistent with this, we find that monetary policy shocks have a stronger effect on house prices during the recent recovery than the previous boom. At the same time, building permits respond less. Finally, we find that housing supply elasticities have declined more in areas where land-use regulation has tightened the most, and in areas that experienced the sharpest housing busts.