Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...

Using a new dataset of all Swedish housing transactions over the 2009-2017 period, we find that manufacturing's share employment is positively associated with house price growth volatility and negatively risk-adjusted capital gains. Both effects appear to be related impact on firm concentration volatility. Moreover, they imply manufacturing decline since 1970 could account for 35% reduction in

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squar...

Locally weighted quantile regressions allow the coefficients of hedonic house price functions to vary over space. Using data on all house sales in Cook County, Illinois, for 20002011, I show how the full distribution of appreciation rates changed over time in small geographic areas. The estimates reveal significant spatial variation in appreciation rates both geographically and across the distr...