ARMA model

This paper develops a comparative analysis of different forecasting approaches for the aggregate which is a main target of the monetary policy in Argentina. First we present the results of estimating a conditional EqC model of money demand, which characterizes the whole sample and shows “ex-post” constancy. Then we compare forecasts from this “causal” model with those obtained by other methods: VAR in differences, “naive” (univariate) models and robustified devices, as well as forecasting aggregates by disaggregates and by model and window pooling forecasts. They are evaluated for a period in which there was often uncertainty about the economic regime.

This paper investigates the linear index threshold regression model with endogeneity. We propose a two-step GMM estimation method to estimate the model, which allows both the threshold variable and regressors to be endogenous. We show the consistency of the GMM estimator and derive the asymptotic distribution of the GMM estimator for weakly dependent data. We suggest a test of the exoneneity null hypothesis for both the threshold and the slope regressors. Monte Carlo simulations are used to assess the finite sample performance of our proposed estimator. Finally, we present an empirical application investigating the threshold effect of a linear index between external debt and public debt on economic growth for developing countries. JEL Classification: C13 C32 O40

Abstract—This paper reports on issues arising when applying distributed control to a fully defined practical system, which is the segmented primary mirror of the European Extremely Large Telescope. The controller must stabilize and maintain 984 segments composing the ...

The measure of bullwhip effect (BWE) has always been a challenging task to the researchers. BWE is one of the main performance measures for a supply chain. In the existing literature, the measurement of BWE has been performed based on the simple demand processes such as Moving Average, AR(1) (AutoRegression), AR(2), Exponential Smoothing and ARMA(1,1) (AutoRegressive Moving Average). In this paper, we have derived generalized expressions of BWE based on the generalized ARMA (p,q) demand process under the various replenishment policies. The expressions have been compared both algebraically and numerically in order to find out the appropriate replenishment policy that leads to minimum valued expression for BWE.

Streszczenie. Celem niniejszego artykulu jest przedstawienie zastosowania sztucznych sieci neuronowych do prognozowania cen na gieldzie energii elektrycznej. Na wejście skonstruowanego predykatora neuronowego wprowadzono m.in. przeszle wartości ceny energii i wolumenu obrotu, zmienne określające pogode, tzn. temperature, zachmurzenie, predkośc wiatru oraz zmienne opisujące dzien i godzine transakcji. Wszystkie te zmienne byly mierzone dla kazdej godziny doby handlowej.

This paper introduces a novel design for boring bar with enhanced damping capability. The principle followed in the design phase was to enhance the damping capability minimizing the loss in static stiffness through implementation of composite material interfaces. The newly designed tool has been compared to a conventional tool. The evaluation criteria were the dynamic characteristics, frequency and damping ratio, of the machining system, as well as the surface roughness of the machined workpieces. The use of composite material in the design of damped tool has been demonstrated effective. Furthermore, the autoregressive moving average (ARMA) models presented in this paper take into consideration the interaction between the elastic structure of the machine tool and the cutting process and can therefore be used to characterize the machining system in operational conditions.

In this paper, we consider the backward diffusion problem for a space-fractional diffusion equation (SFDE) with a nonlinear source, that is, to determine the initial data from a noisy final data. Very recently, some papers propose new modified regularization solutions to solve this problem. To get a convergence estimate, they required some strongly smooth conditions on the exact solution. In this paper, we shall release the strongly smooth conditions and introduce a stepwise regularization method to solve the backward diffusion problem. A numerical example is presented to illustrate our theoretical result.