Maximum Smoothed Likelihood Component Density Estimation in Mixture Models with Known Mixing Proportions

In this paper, we propose a maximum smoothed likelihood method to estimate the component density functions of mixture models, in which the mixing proportions are known and may differ among observations. The proposed estimates maximize a smoothed log likelihood function and inherit all the important properties of probability density functions. A majorization-minimization algorithm is suggested to compute the proposed estimates numerically. In theory, we show that starting from any initial value, this algorithm increases the smoothed likelihood function and further leads to estimates that maximize the smoothed likelihood function. This indicates the convergence of the algorithm. Furthermore, we theoretically establish the asymptotic convergence rate of our proposed estimators. An adaptive procedure is suggested to choose the bandwidths in our estimation procedure. Simulation studies show that the proposed method is more efficient than the existing method in terms of integrated squared errors. A real data example is further analyzed

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.