Detecting long memory co-movements in macroeconomic time series

Cointegration analysis tests for the existence of a significant long-run equilibrium among some economic variables. Standard econometric procedures to test for cointegration have proven unreliable when the long-run relation among the variables is characterized by non-linearities and persistent fluctuations around the equilibrium. As a consequence, many intuitive economic relations are empirically rejected. In this paper we propose a simple approach to account for non-linearities in the cointegrating equilibrium and possible long memory fluctuations from such equilibrium. We show that our correction allows us to test robustly for the presence of cointegration both under the null and alternative hypotheses. We apply our procedure to the Johansen-Juselius PPP-UIP database, and unlike the standard case, we do not fail to reject the null of no cointegration.Cointegration analysis, long memory

Estimating DGSE models with long memory dynamics

Recent literature clams that key variables such as aggregate productivity and inflation display long memory dynamics. We study the impllications of this high degree of persistence on the estimation of Dynamic Stochastic General Equilibrium (DGSE) models. We show that long memory data produce substantial bias in the deep parameter estimates when a standard Kalman Filter-MLE procedure is used. We propose a modification of the Kalman Filter procedure, we mainly augment the state space, which deals with this problem. By the means of the augmented state space we can consistently estimate the model parameters as well as produce more accurate out-of-sample forecasts compared to the standard Kalman filter.

Real time forecasts of inflation: the role of financial variables

We present a mixed-frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real-time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed-frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives.forecasting inflation, real time forecasts, dynamic factor models, MIDAS regression, economic derivatives

Real time forecasts of inflation: the role of financial variables

INTRODUCTION;ROLE OF FINANCIAL VARIABLES;A TWO-STEP APPROACH TO MODEL INFLATION ; MODELLING LONG-MEDIUM TERM COMPONENT OF INFLATION ;A MIXED-FREQUENCY MODEL FOR REAL-TIME FORECASTS OF INFLATION; 4 TWO FORECASTING APPLICATIONS IN REAL-TIME; REAL-TIME FORECASTS OF MONTHLY INFLATION; MODEL FORECASTS VS MARKET EXPECTATIONS; CONCLUDING REMARKS; REFERENCES; APPENDIXforecasting inflation, real-time forecasts, dynamic factor models, MIDAS regression, economic derivatives.

Essays on non-linear aggregation in macroeconomics

In this PhD thesis I investigate the implications of heterogeneity and aggregation in macroeconomic models. The importance of aggregation lies ~n the fact that when heterogeneity is allowed, we cannot expect macro models to have the same characteristics as the underlying micro models. In particular, a direct consequence of aggregation is that the dynamic properties of the micro model do not hold in general for the macr,? model. Despite this problem, modern macroeconomics tends to model aggregate data alone, through the construction of models where the individual consumer or firm is related to aggregate data under the guise of a 'representative agent'. In this thesis, I present a heterogeneous real business cycle model where I allow for cross sectional heterogeneity in the dynamics of the firm productivities. I show that heterogeneity allows the model to generate very persistent dynamics that can mimic impressively those of actual data. This is because, the dynamics of the model are now the result of the interactions between heterogeneous firms. Another problem that often arises with heterogeneity is that through aggregation, the dynamics that describe the co-movements between two variables can be more persistent and complex than the dynamics observed for the individual behaviour. Standard co-integration techniques are not able to deal with such persistent co-movements since they cannot distinguish between persistent deviations from the equilibrium and spurious relations. Therefore, many intuitive economic relations are often empirically rejected. To this purpose, I introduce in the thesis a methodology which can test robustly for co-integration between two variables, which deviate persistently from their long-run equilibrium. I test for a co-integration in the Uncovered Interest Parity and the Purchasing Power Parity with my approach and, unlike the standard approaches, it does not reject the hypothesis that they hold in the long run

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

Dielectric elastomers are a promising technology for wave energy harvesting. An optimal system operation can allow maximizing the extracted energy and, simultaneously, reducing wear that would lead to a reduction in the wave harvester lifetime. We pursue a model-based optimization approach to identify optimal controls for wave energy harvesters based on dielectric elastomers. First, a direct method is used for time-discretization of the dielectric elastomer wave energy harvester in the optimal control problem. The two conflicting objectives are considered in a multiobjective optimization framework. Considering a periodic, sinusoidal wave excitation, the optimal solution shows turnpike properties for the optimal periodic mode of operation. However, since real wave motion is neither monochromatic nor predictable on longer time horizons, further extensions are pursued. First, we introduce a stochastic wave excitation. Second, an iterative model-predictive control scheme is designed. Due to multiple objectives, the control scheme has to include an automated adaption of the corresponding priorities. Here, we propose and evaluate a heuristic rule-based adaption in order to maintain the damage below target levels. The approach presented here might be used in the future to guarantee for autonomous operation of farms of wave energy harvesters

Finite element modeling and validation of a soft array of spatially coupled dielectric elastomer transducers

Dielectric elastomer (DE) transducers are suitable candidates for the development of compliant mechatronic devices, such as wearable smart skins and soft robots. If many independently-controllable DEs are closely arranged in an array-like configuration, sharing a common elastomer membrane, novel types of cooperative and soft actuator/sensor systems can be obtained. The common elastic substrate, however, introduces strong electro-mechanical coupling effects among neighboring DEs, which highly influence the overall membrane system actuation and sensing characteristics. To effectively design soft cooperative systems based on DEs, these effects need to be systematically understood and modeled first. As a first step towards the development of soft cooperative DE systems, in this paper we present a finite element simulation approach for a 1-by-3 silicone array of DE units. After defining the system constitutive equations and the numerical assumptions, an extensive experimental campaign is conducted to calibrate and validate the model. The simulation results accurately predict the changes in force (actuation behavior) and capacitance (sensing behavior) of the different elements of the array, when their neighbors are subjected to different electro-mechanical loads. Quantitatively, the model reproduces the force and capacitance responses with an average fit higher than 93% and 92%, respectively. Finally, the validated model is used to perform parameter studies, aimed at highlighting how the array performance depends on a relevant set of design parameters, i.e. DE-DE spacing, DE-outer structure spacing, membrane pre-stretch, array scale, and electrode shape. The obtained results will provide important guidelines for the future design of cooperative actuator/sensor systems based on DE transducers

Predictors of cut-out after cephalomedullary nail fixation of pertrochanteric fractures: a retrospective study of 813 patients

BACKGROUND: Cut-out is the most common mechanical complication of the osteosynthesis of pertrochanteric fractures. This complication determines a significant increase in morbidity in elderly patient. Cut-out is defined as the varus collapse of the femoral head-neck fragment with the extrusion of the cephalic screw. Surgical treatment of cut-out might lead to further complications, longer rehabilitation, increased social burden and healthcare system costs. The aim of the study is to identify the predictors of cut-out to prevent its occurrence. MATERIALS AND METHODS: Study population included all patients affected by extracapsular fracture of the proximal femur who were admitted and treated with short cephalomedullary nailing at the Cattinara Hospital-ASUITS of Trieste between 2009 and 2014. A retrospective analysis of clinical and radiographic data was carried out and cut-out cases recorded. The data collected on the study population were analyzed to find an eventual correlation with the occurrence of cut-out. The independent variables were age, gender, side of the fracture, ASA class, Evans classification, nailing system, quality of reduction, TAD, CalTAD, and Parker ratio. RESULTS: The study population counted 813 cases, with an F:M ratio of 4:1 and a mean age of 84.7 years. The cut-out was recorded in 18 cases (2.2%). There was no statistically significant association between cut-out and age, sex, side of fracture, ASA class, and nailing system. The Evans classification, the quality of reduction, the TAD, the CalTAD, and the Parker's ratio demonstrated a significant correlation at univariate analysis with cut-out. The results of multivariate analysis confirmed that TAD, Parker AP, and quality of reduction were independently significantly correlated to cut-out. CONCLUSION: The results of the present study demonstrate that good quality of reduction and correct position of the lag screw are likely to decrease the risk of cut-out complication. A nomogram for cut-out prediction is proposed for clinical validation

Point cloud processing techniques and image analysis comparisons for boat shapes measurements

Photomodelling is a new and fast solution for 3D modelling, based on the same principles of photogrammetry. The comparison between photomodelling and the metrological technique of structured light 3D scanning, provided by the Creaform Go Scan 50 with metrological certification, is the aim of this paper, defining performances and verifying the potential of this innovative, simple and economical technique