Hawtreyan 'credit deadlock' or Keynesian 'liquidity trap'? Lessons for Japan from the great depression

This paper outlines the ideas of Ralph Hawtrey and Lauchlin Currie on the need for monetised fiscal deficit spending in 1930s USA to combat the deep depression into which the economy had been allowed to sink. In such exceptional circumstances of 'credit deadlock' in which banks were afraid to lend and households and business afraid to borrow, the deadlock could best be broken through the spending of new money into circulation via large fiscal deficits. This complementarity of fiscal and monetary policy was shown to be essential, and as such indicates the potential power of monetary policy - in contrast to the Keynesian "liquidity trap" view that it is powerless This lesson was not learned by the Japanese authorities in their response to the asset price collapse of 1991-92, resulting in a lost decade as ballooning fiscal deficits were neutralised throughout the 1990s by unhelpfully tight monetary policy with the Bank of Japan refusing to monetise the deficits

Geographically weighted elastic net logistic regression

This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters