A Bayesian Framework for Estimating Seismic Wave Arrival Time

Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we conducted a simulation study where we could evaluate the predictive performance of the model framework. The results show that our method has acceptable performance of arrival time prediction with accounting for the uncertainty

Time Series Analysis

We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain

Time Series Analysis

We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain, Research Methods/ Statistical Methods,

The Role of Fundamentals in the Price of Housing. Theory and Evidence

Despite the extensive research in the field of housing economics, the role of some ‘fundamental’ economic variables, such as income, interest rates or stock of houses per capita, on the real price of housing is still not fully understood. In this paper we develop a dynamic general equilibrium model to micro-fundament the price of housing in a market without renting. In this framework we underline the dual role of housing both as a good that produces valuable services and as an investment asset that can be resold in a future date. To test the theoretical results obtained, we analyze the Spanish housing market from 1995 to 2006 as it seems to satisfy the theoretical assumptions in practice. We examine the extent to which real house prices at the regional level are driven by fundamentals by applying Panel Cointegration methods such as Common Correlated Effects, Dynamic Ordinary Least Squares and Vector Error Correction. Results are fully consistent with the theory and underline the importante of both long-run adjustment and persistence processes to explain the dynamic behaviour of prices.Panel Cointegration, housing prices, adjustment dynamics, macroeconomic model