Land-price dynamics and macroeconomic fluctuations

We argue that positive comovements between land prices and business investment are a driving force behind the broad impact of land-price dynamics on the macroeconomy. We develop an economic mechanism that captures the comovements by incorporating two key features into a DSGE model: we introduce land as a collateral asset in firms' credit constraints, and we identify a shock that drives most of the observed fluctuations in land prices. Our estimates imply that these two features combine to generate an empirically important mechanism that amplifies and propagates macroeconomic fluctuations through the joint dynamics of land prices and business investment.

Land-price dynamics and macroeconomic fluctuations

We argue that positive co-movements between land prices and business investment are a driving force behind the broad impact of land-price dynamics on the macroeconomy. We develop an economic mechanism that captures the co-movements by incorporating two key features into a DSGE model: We introduce land as a collateral asset in firms’ credit constraints and we identify a shock that drives most of the observed fluctuations in land prices. Our estimates imply that these two features combine to generate an empirically important mechanism that amplifies and propagates macroeconomic fluctuations through the joint dynamics of land prices and business investment.Real property

Do credit constraints amplify macroeconomic fluctuations?

Previous studies on financial frictions have been unable to establish the empirical significance of credit constraints in macroeconomic fluctuations. This paper argues that the muted impact of credit constraints stems from the absence of a mechanism to explain the observed persistent comovements between housing prices and business investment. We develop such a mechanism by incorporating two key features into a dynamic stochastic general equilibrium model: We identify shocks that shift the demand for collateral assets and allow productive agents to be credit-constrained. A combination of these two features enables our model to successfully generate an empirically important mechanism that amplifies and propagates macroeconomic fluctuations through credit constraints.

Deep Extreme Multi-label Learning

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result