This study examines the relationship between healthcare expenditure and disposable income in the 50 US states over the period 1966-2009 using fractional integration and cointegration techniques. The degree of integration and nonlinearity of both series are found to vary considerably across...

This paper applies a time-varying parameter vector autoregressive (TVP-VAR) approach to estimate the relative effects of housing and stock prices on US consumption over time. We use annual data from 1890 to 2012 and find that over different horizons and over time, generally the housing price...

This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to...

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...

In this paper, we conduct a comprehensive investigation of the Halloween effect evolution in the US stock market over its entire history. We employ various statistical techniques (average analysis, Student's t-test, ANOVA, and the Mann-Whitney test) and the trading simulation approach to analyse...

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its turning point in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general...

We implement several Bayesian and classical models to forecast housing prices in 20 US states. In addition to standard vector-autoregressive (VAR) and Bayesian Vector Autoregressive (BVAR) models, we also include the information content of 308 additional quarterly series in some models. Several...

We utilize a Dynamic Factor Model with Stochastic Volatility to filter out the national factor from the local components of weekly state-level economic conditions indexes of the United States over the period of April 1987 to August 2021. Then, we forecast the state-level factors. The forecasting...