The Effect of Data Revisions on the Basic New Keynesian Model

This paper proposes an extended version of the basic New Keynesian monetary (NKM) model which contemplates revision processes of output and inflation data in order to assess the importance of data revisions on the estimated monetary policy rule parameters and the transmission of policy shocks. Our empirical evidence based on a structural econometric approach suggests that although the initial announcements of output and inflation are not rational forecasts of revised output and inflation data, ignoring the presence of non well-behaved revision processes may not be a serious drawback in the analysis of monetary policy in this framework. However, the transmission of inflation-push shocks is largely affected by considering data revisions. The latter being especially true when the nominal stickiness parameter is estimated taking into account data revision processes.

An empirical comparison of the performance of alternative option pricing models

Published as an article in: Investigaciones Economicas, 2005, vol. 29, issue 3, pages 483-523.

On the temperature dependence of the rate coefficient of formation of C2+ from C + CH+

[EN] We carry out quasi-classical trajectory caculations for theC + CH+ → C2+ + H reaction on an ad hoc computed high-level ab initio potential energy surface. Thermal rate coefficients at the temperatures of relevance in cold interstellar clouds are derived and compared with the assumed, temperature-independent estimates publicly available in kinetic databases KIDA and UDfA. For a temperature of 10 K the database value overestimates by a factor of two the one obtained by us (thus improperly enhancing the destruction route of CH+ in astrochemical kinetic models) which is seen to double in the temperature range 5–300 K with a sharp increase in the first 50 K. The computed values are fitted via the popular Arrhenius–Kooij formula and best-fitting parameters α = 1:32 X 10-9 cm3s-1, β = 0:10 and γ = 2:19 K to be included in the online mentioned databases are provided. Further investigation shows that the temperature dependence of the thermal rate coefficient better conforms to the recently proposed so-called ‘deformed Arrhenius’ law by Aquilanti and Mundim.

SEM and EDS Characterisation of Layering TiOx Growth onto the Cutting Tool Surface in Hard Drilling Processes of Ti-Al-V Alloys

10 p.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.