Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

We develop the methodology and a detailed case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the recently introduced foundational framework of BPS to the multivariate setting, with detailed application in the topical and challenging context of multi-step macroeconomic forecasting in a monetary policy setting. BPS evaluates-- sequentially and adaptively over time-- varying forecast biases and facets of miscalibration of individual forecast densities, and-- critically-- of time-varying inter-dependencies among them over multiple series. We develop new BPS methodology for a specific subclass of the dynamic multivariate latent factor models implied by BPS theory. Structured dynamic latent factor BPS is here motivated by the application context-- sequential forecasting of multiple US macroeconomic time series with forecasts generated from several traditional econometric time series models. The case study highlights the potential of BPS to improve of forecasts of multiple series at multiple forecast horizons, and its use in learning dynamic relationships among forecasting models or agents

Deep Learning for Crowd Anomaly Detection

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

Relationship between the forces applied to the starting blocks and block clearance in a sprint start

markdownabstract__Abstract__ We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-tim

Asymmetric effects of monetary policy in regional housing markets

The responsiveness of house prices to monetary policy shocks depends on the nature of the shock – expansionary versus contractionary – and on local housing supply elasticities. These findings are established based on a panel of 263 US metropolitan areas. We test and find supporting evidence for the hypothesis that expansionary monetary policy shocks have a larger impact on house prices when supply elasticities are low. Our results also suggest that contractionary shocks are orthogonal to supply elasticities, as implied by downward rigidity of housing supply. A standard theoretical conjecture is that contractionary shocks have a greater impact on house prices than expansionary shocks, as long as supply is not perfectly inelastic. For areas with high housing supply elasticity, our results are in line with this conjecture. However, for areas with an inelastic housing supply, we find that expansionary shocks have a greater impact on house prices than contractionary shocks. We provide evidence that the direction of the asymmetry is related to a momentum effect that is more pronounced when house prices are increasing than when they are falling

Has the Fed responded to house and stock prices? : a time-varying analysis

En este trabajo utilizamos un modelo VAR estructural con parámetros variables en el tiempo y volatilidad estocástica para investigar si la Reserva Federal ha respondido sistemáticamente a los precios de los activos y si esta respuesta ha cambiado con el tiempo. Para recuperar el componente sistemático de la política monetaria, interpretamos la ecuación de la tasa de interés en el VAR como una regla extendida de política monetaria que responde a la inflación, el output gap, los precios de la vivienda y los precios de las acciones. Detectamos variación temporal en los coeficientes de precios de la vivienda y precios de las acciones, mientras que los coeficientes de la inflación y el output gap son bastante estables en el tiempo. Nuestros resultados indican que el componente sistemático de la política monetaria en Estados Unidos i) tuvo un peso positivo sobre el crecimiento real de los precios de la vivienda, que disminuyó antes de la crisis y eventualmente volvió a aumentar, y ii) solo tuvo en cuenta el crecimiento real de los precios de las acciones en momentos concretos del tiempoIn this paper we use a structural VAR model with time-varying parameters and stochastic volatility to investigate whether the Federal Reserve has responded systematically to asset prices and whether this response has changed over time. To recover the systematic component of monetary policy, we interpret the interest rate equation in the VAR as an extended monetary policy rule responding to infl ation, the output gap, house prices and stock prices. We find some time variation in the coefficients for house prices and stock prices but fairly stable coefficients over time for inflation and the output gap. Our results indicate that the systematic component of monetary policy in the US, i) attached a positive weight to real house price growth but lowered it prior to the crisis and eventually raised it again, and ii) only episodically took real stock price growth into accoun

The Price Responsiveness of Shale Producers: Evidence from Micro Data

We show that shale oil producers respond positively to favourable oil price signals, and that this response is mainly associated with the timing of production decisions through well completion and refracturing, consistent with the Hotelling theory of optimal extraction. 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. Our empirical results call 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 global economy.publishedVersio

Detection of divergent genes in microbial aCGH experiments

BACKGROUND: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach. METHODS: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity. RESULTS: The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae. CONCLUSION: When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes