Macroeconomic Volatility Trade-off and Monetary Policy Regime in the Euro Area

This research uncovers a well-defined monetary policy regime starting in 1986 in the aggregate Euro Area. Both alternative solution-estimation methods employed - optimal control cum GMM, and dynamic programming cum FIML - identify a regime of strict inflation targeting with interest rate smoothing. The unemployment gap, properly estimated as quasi real-time information, is a relevant element in the information set of the monetary authority, despite not being included in its preferences. The emergence of the regime relates to the improvement of the volatility trade-off between inflation and unemployment gap since the mid-80s. Additional improving factors have been milder supply shocks and better ability of policymakers to set the interest rate closer to optimum.Monetary Policy Regime, Euro Area, Optimal Control, Dynamic Programming, GMM, FIML.

Testing for Asymmetries in the Preferences of the Euro-Area Monetary Policymaker

This paper tests for asymmetries in the preferences of the Euro-Area monetary policymaker with 1995:I-2004:III data from the last update of the ECB's Area-wide database. Following the relevant literature, we distinguish between three types of asymmetry: precautionary demand for expansions, precautionary demand for price stability and interest rate smoothing asymmetry. Based on the joint GMM estimation of the Euler equation of optimal policy and the AS-AD structure of the macroeconomy, we find evidence of precautionary demand for price stability in the preferences revealed by the monetary policymaker. This type of asymmetry is consistent with the ECB’s definition of price stability and with the priority of credibility-building by a recently created monetary authority.Central Bank Preferences, Asymmetry, Euro Area, Optimal Control, GMM.

Growth Cycles in XXth Century European Industrial Productivity: Unbiased Variance Estimation in a Time-varying Parameter Model

This note applies the median unbiased estimation of coefficient variance, proposed by Stock and Watson (1998), to the extraction of the time-varying trend growth rate of industrial productivity in fifteen European countries, over most of the XXth Century, by means of an unobservable components univariate decomposition. In addition to the description of the procedure, this illustration is particularly useful in explaining why the method is especially appropriate for comparison of trends growth rates extracted from time series with diverse degrees of variability.unobservable components model; industrial productivity; growth cycles; Europe.

Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments

Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. In opposition, the so-called local methods enforce connectivity but lack robustness to deal with challenging situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. In this paper, we address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional content agree with potential line segments. As a result, our method, which we call STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in several situations where current methods fail.Comment: Submitted for publicatio

Revisiting Complex Moments For 2D Shape Representation and Image Normalization

When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem. In the paper, we show how to uniquely define the orientation of an arbitrary 2D shape, in terms of what we call its Principal Moments. We show that a small subset of these moments suffice to represent the underlying 2D shape and propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize grey-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating the method with real images.Comment: 69 pages, 20 figure

D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization

We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at each node. The goal is to minimize the sum of all the cost functions, constraining the solution to be in the intersection of all the constraint sets. D-ADMM is proven to converge when the network is bipartite or when all the functions are strongly convex, although in practice, convergence is observed even when these conditions are not met. We use D-ADMM to solve the following problems from signal processing and control: average consensus, compressed sensing, and support vector machines. Our simulations show that D-ADMM requires less communications than state-of-the-art algorithms to achieve a given accuracy level. Algorithms with low communication requirements are important, for example, in sensor networks, where sensors are typically battery-operated and communicating is the most energy consuming operation.Comment: To appear in IEEE Transactions on Signal Processin

Distributed Optimization With Local Domains: Applications in MPC and Network Flows

In this paper we consider a network with $P$ nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function depends only on some components of the variable. Consequently, each node is interested in knowing only some components of $x^\star$, not the entire vector. This allows for improvement in communication-efficiency. We apply our algorithm to model predictive control (MPC) and to network flow problems and show, through experiments on large networks, that our proposed algorithm requires less communications to converge than prior algorithms.Comment: Submitted to IEEE Trans. Aut. Contro

Distributed Basis Pursuit

We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP). BP finds the least L1-norm solution of the underdetermined linear system Ax = b and is used, for example, in compressed sensing for reconstruction. Our algorithm solves BP on a distributed platform such as a sensor network, and is designed to minimize the communication between nodes. The algorithm only requires the network to be connected, has no notion of a central processing node, and no node has access to the entire matrix A at any time. We consider two scenarios in which either the columns or the rows of A are distributed among the compute nodes. Our algorithm, named D-ADMM, is a decentralized implementation of the alternating direction method of multipliers. We show through numerical simulation that our algorithm requires considerably less communications between the nodes than the state-of-the-art algorithms.Comment: Preprint of the journal version of the paper; IEEE Transactions on Signal Processing, Vol. 60, Issue 4, April, 201