NP-optimal kernels for nonparametric sequential detection rules

An attractive nonparametric method to detect change-points sequentially is to apply control charts based on kernel smoothers. Recently, the strong convergence of the associated normed delay associated with such a sequential stopping rule has been studied under sequences of out-of-control models. Kernel smoothers employ a kernel function to downweight past data. Since kernel functions with values in the unit interval are sufficient for that task, we study the problem to optimize the asymptotic normed delay over a class of kernels ensuring that restriction and certain additional moment constraints. We apply the key theorem to discuss several important examples where explicit solutions exist to illustrate that the results are applicable. --Control charts,financial data,nonparametric regression,quality control,statistical genetics

Jump-preserving monitoring of dependent time series using pilot estimators

An important problem of the statistical analysis of time series is to detect change-points in the mean structure. Since this problem is a one-dimensional version of the higher dimensional problem of detecting edges in images, we study detection rules which benefit from results obtained in image processing. For the sigma-filter studied there to detect edges, asymptotic bounds for the normed delay have been established for independent data. These results are considerably extended in two directions. First, we allow for dependent processes satisfying a certain conditional mixing property. Second, we allow for more general pilot estimators, e.g., the median, resulting in better detection properties. A simulation study indicates that our new procedure indeed performs much more better. --Image processing,Nonparametric regression,Quality Control,Structural Change

The role of the equivalent blackbody temperature in the study of Atlantic Ocean tropical cyclones

Satellite measured equivalent blackbody temperatures of Atlantic Ocean tropical cyclones are used to investigate their role in describing the convection and cloud patterns of the storms and in predicting wind intensity. The high temporal resolution of the equivalent blackbody temperature measurements afforded with the geosynchronous satellite provided sequential quantitative measurements of the tropical cyclone which reveal a diurnal pattern of convection at the inner core during the early developmental stage; a diurnal pattern of cloudiness in the storm's outer circulation throughout the life cycle; a semidiurnal pattern of cloudiness in the environmental atmosphere surrounding the storms during the weak storm stage; an outward modulating atmospheric wave originating at the inner core; and long term convective bursts at the inner core prior to wind intensification

Dynamic modeling of mean-reverting spreads for statistical arbitrage

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.Comment: 34 pages, 6 figures. Submitte

Inflation Dynamics: The Case of Egypt

Inflation as a phenomenon has witnessed remarkable changes starting from mid-eighties of the last century. Inflation rates have become less persistent, less responsive to supply side shocks. In addition, the relative importance of demand pull inflation as one of the major determinants of inflation has decreased due to efficient monetary policies that have been adopted by central banks all over the world to reduce inflation based on anchoring inflation expectations. Moreover, the slope of Phillips curve has flattened as many factors have appeared to be more influential on inflation rather than output gap, namely inflation expectations. These changes constitute in the new economic literature what so called “Inflation Dynamics”. In this context, this study focuses on analyzing inflation dynamics in Egypt in (1980-2009) in order to identify to what extent “Inflation Dynamics” in Egypt is different from or similar to those witnessed globally. The study applied a Vector Auto Regressive model (VAR) and other econometrics models to analyze “Inflation Dynamics” in Egypt in three sub periods: the 1980s, the 1990s and the first decade of the new millennium. The study concluded that Inflation Dynamics in Egypt is completely different from those observed globally. Inflation rates in Egypt have become more persistent especially starting from 2000; Inflation shocks are now lasting longer and have a long-term impact on the future inflation paths. On the other hand, demand bull inflation still considers one of the most important inflation determinants, as it is solely responsible for explaining 30% of the changes in inflation rates. In addition, the study confirmed that inflation rates in Egypt have become more responsive to supply side shocks starting from 2006. As for the slope of Phillips curve, the study confirmed that similar to the changes observed globally, the slope of Phillips Curve for the Egypt economy has flattened reflecting the increasing importance of other inflation determinants rather than output gap.Inflation, Inflation dynamics, Inflation persistence, The Egyptian economy, Demand-pull inflation, Cost-push inflation, Inflation expectations, markets and prices rigidities, Phillips curve, Government debt, Monetary policies, Vector Auto Regression (VAR)

Inflation and Inflation Uncertainty in the Euro Area

This paper estimates a time-varying AR-GARCH model of inflation producing measures of inflation uncertainty for the euro area, and investigates the linkages between them in a VAR framework, also allowing for the possible impact of the policy regime change associated with the start of EMU in 1999. The main findings are as follows. Steady-state inflation and inflation uncertainty have declined steadily since the inception of EMU, whilst short-run uncertainty has increased, mainly owing to exogenous shocks. A sequential dummy procedure provides further evidence of a structural break coinciding with the introduction of the euro and resulting in lower long-run uncertainty. It also appears that the direction of causality has been reversed, and that in the euro period the Friedman-Ball link is empirically supported, implying that the ECB can achieve lower inflation uncertainty by lowering the inflation rate.inflation, inflation uncertainty, time-varying parameters, GARCH models, ECB, EMU

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics