Economic conditions exert a strong influence on regional migration. On the one hand, strong labour market conditions, as exemplified by low unemployment rates and high earnings, draw migrants into regions. On the other hand, strong housing market conditions can prevent movement since expensive housing can deter migrants and commuting may often be an alternative. This can be thought of as giving rise to a migration equilibrium, where high house prices choke off migration caused by strong labour market conditions. Expected capital gains in housing and expected earnings growth however, can offset high levels of house prices, effects ignored in previous literature. Migration can also be influenced more directly by the availability of housing relative to population without this being mediated through prices. This paper presents evidence from a 28 year panel on net and gross migration for the regions of Britain that is broadly in accord with these expectations.
This paper investigates the bubbles hypothesis with a dynamic panel data model of British regional house prices between 1972 and 2003. The model consists of a system of inverted housing demand equations, incorporating spatial interactions and lags and relevant spatial parameter heterogeneity. The results are data consistent, with plausible long-run solutions and include a full range of explanatory variables. Novel features of the model include transaction cost effects influencing the speed of adjustment, and interaction effects between an index of credit availability and real and nominal interest rates. No evidence for a recent bubble is found.
We examine how crossed markets create potential arbitrage opportunities in Nasdaq stocks. On average, actively traded Nasdaq listed stocks are crossed approximately 0.5% of the trading day. The incidence of crosses is higher in more fragmented markets. When crosses occur, the mean duration is three seconds, the value of the cross is around one cent, and the offer side has approximately 2,000 shares available for trading. Our simulated trading analysis shows that institutional traders, who act fast and pay little in transaction costs, can potentially exploit the arbitrage opportunities presented by market crosses.
Recent and Forthcoming Publications
This paper investigates the bubbles hypothesis with a dynamic panel data model of British regional house prices between 1972 and 2003. The model consists of a system of inverted housing demand equations, incorporating spatial interactions and lags and relevant spatial parameter heterogeneity.The results are data consistent, with plausible long-run solutions and include a full range of explanatory variables. Novel features of the model include transaction cost effects influencing the speed of adjustment and interaction effects between an index of credit availability and real and nominal interest rates. No evidence for a recent bubble is found.
A relatively simple and convenient score test of normality in the bivariate probit model is derived. Monte Carlo simulations show that the small sample performance of the bootstrapped test is quite good. The test may be readily extended to testing normality in related models.
We investigate the procedure used by Ané and Geman (AG, Journal of Finance, 2000) to recover the moments of information flow from high frequency data in a model which generalizes the subordinated or mixture of distributions process in Clark (1973). Using Monte Carlo experiments we show that the third and higher moments of the latent information flow cannot be accurately recovered using this procedure. We explain why this happens. We also show that, contrary to the claims in AG, returns conditioned on the recentered number of trades are not approximately Gaussian.
The OECD believes that UK house prices are significantly overvalued and warns of the danger of a protracted period of large house price falls, with implications for a slowdown in consumer spending. The last OECD Economic Outlook suggests that UK house prices are overvalued by 30% or more. Our research suggests this view is wrong.