Another look at sticky prices and output persistence

Price rigidity is the key mechanism for propagating business cycles in traditional Keynesian theory. Yet the New Keynesian literature has failed to show that sticky prices by themselves can effectively propagate business cycles in general equilibrium. We show that price rigidity in fact can (by itself) give rise to a strong propagation mechanism of the business cycle in standard New Keynesian models, provided that investment is also subject to a cash-in-advance constraint. In particular, we show that reasonable price stickiness can generate highly persistent, hump-shaped movements in output, investment and employment in response to either monetary or non-monetary shocks, even if investment is only partially cash-in-advance constrained. Hence, whether or not price rigidity is responsible for output persistence (and the business cycle in general) may not be a theoretical question, but an empirical one.Prices ; Business cycles

Inflation dynamics: a cross-country investigation

We document that "persistent and lagged" inflation (with respect to output) is a world-wide phenomenon in that these short-run inflation dynamics are highly synchronized across countries. In particular, the average cross-country correlation of inflation is significantly and systematically stronger than that of output, while the cross-country correlation of money growth is essentially zero. We investigate whether standard monetary models driven by monetary shocks are consistent with the empirical facts. We find that neither the new Keynesian sticky-price model nor the sticky-information model can fully explain the data. An independent contribution of the paper is to provide a simple solution technique for solving general equilibrium models with sticky information. ; Earlier title: Inflation and money: a puzzleMoney ; Inflation (Finance)

Solving linear difference systems with lagged expectations by a method of undetermined coefficients

This paper proposes a solution method to solve linear difference models with lagged expectations. Variables with lagged expectations expand the model's state space greatly when N is large; and getting the system into a canonical form solvable by the traditional methods involves substantial manual work (e.g., arranging the state vector and the associated coefficient matrices to accommodate variables with lagged expectations), which is prone to human errors. Our method avoids the need of expanding the state space of the system and shifts the burden of analysis from the individual economist/model solver toward the computer. Hence it can be a very useful tool in practice, especially in testing and estimating economics models with a high order of lagged expectations. Examples are provided to demonstrate the usefulness of the method. We also discuss the implications of lagged expectations on the equilibrium properties of indeterminate DSGE models, such as the serial correlation properties of sunspots shocks in these models.Monetary policy ; Macroeconomics

Another Look at Sticky Prices and Output Persistence

Price rigidity is the key mechanism for propagating business cycles in traditional Keynesian theory. Yet the New Keynesian literature has failed to show that sticky prices by itself can effectively propagate business cycles in general equilibrium. This situation may be a direct consequence of the notion that money-in-utility (MIU) and cash-in-advance (CIA) are equivalent mechanisms for generating money demand. They are not. We show that price rigidity in fact can (by itself) give rise to a powerful propagation mechanism of the business cycle under CIA constraint in standard New Keynesian general equilibrium models. In particular, we show that reasonable price stickiness can generate highly persistent, hump-shaped movements in output, investment and employment in response to either monetary or non-monetary shocks. Hence, whether or not price rigidity is responsible for output persistence (and the business cycle in general) is not a theoretical question, but an empirical one.

Occlusion Aware Unsupervised Learning of Optical Flow

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we introduce a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion. Our method shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets. Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.Comment: CVPR 2018 Camera-read