Accounts Receivable Policy Under Stochastic Inflation

1992 ◽  
Vol 7 (3) ◽  
pp. 291-310
Author(s):  
Carmelo Giaccotto
Keyword(s):  
Inflation Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Real Rate ◽  
Arma Process ◽  
Accounts Receivable ◽  
Credit Period ◽  
The Real ◽  
Cash Discount

This paper investigates the problem of managing accounts receivable under uncertain inflation. In particular, we derive expressions for the cash discount and the length of the credit period under the assumption that the inflation rate can be modeled by a general autoregressive moving-average (ARMA) process. A number of examples illustrate the size of the change required to keep the real rate of interest, implicit in the terms of sale, constant.

ARMA processes have maximal entropy among time series with prescribed autocovariances and impulse responses

1985 ◽  
Vol 17 (04) ◽  
pp. 810-840 ◽  
Author(s):  
Jürgen Franke
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Moving Average ◽  
Autoregressive Process ◽  
Natural Generalization ◽  
Maximal Entropy ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Arma Processes

The maximum-entropy approach to the estimation of the spectral density of a time series has become quite popular during the last decade. It is closely related to the fact that an autoregressive process of order p has maximal entropy among all time series sharing the same autocovariances up to lag p. We give a natural generalization of this result by proving that a mixed autoregressive-moving-average process (ARMA process) of order (p, q) has maximal entropy among all time series sharing the same autocovariances up to lag p and the same impulse response coefficients up to lag q. The latter may be estimated from a finite record of the time series, for example by using a method proposed by Bhansali (1976). By the way, we give a result on the existence of ARMA processes with prescribed autocovariances up to lag p and impulse response coefficients up to lag q.

Representations of continuous-time ARMA processes

2004 ◽  
Vol 41 (A) ◽  
pp. 375-382 ◽  
Author(s):  
Peter J. Brockwell
Keyword(s):  
Continuous Time ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Simple Modification ◽  
Arma Processes ◽  
Kernel Representation ◽  
Autoregressive Moving Average Process

Using the kernel representation of a continuous-time Lévy-driven ARMA (autoregressive moving average) process, we extend the class of nonnegative Lévy-driven Ornstein–Uhlenbeck processes employed by Barndorff-Nielsen and Shephard (2001) to allow for nonmonotone autocovariance functions. We also consider a class of fractionally integrated Lévy-driven continuous-time ARMA processes obtained by a simple modification of the kernel of the continuous-time ARMA process. Asymptotic properties of the kernel and of the autocovariance function are derived.

Representations of continuous-time ARMA processes

2004 ◽  
Vol 41 (A) ◽  
pp. 375-382 ◽  
Author(s):  
Peter J. Brockwell
Keyword(s):  
Continuous Time ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Simple Modification ◽  
Arma Processes ◽  
Kernel Representation ◽  
Autoregressive Moving Average Process

Using the kernel representation of a continuous-time Lévy-driven ARMA (autoregressive moving average) process, we extend the class of nonnegative Lévy-driven Ornstein–Uhlenbeck processes employed by Barndorff-Nielsen and Shephard (2001) to allow for nonmonotone autocovariance functions. We also consider a class of fractionally integrated Lévy-driven continuous-time ARMA processes obtained by a simple modification of the kernel of the continuous-time ARMA process. Asymptotic properties of the kernel and of the autocovariance function are derived.

scholarly journals Multi-targets localization based on ARMA and GA in WSNs

2018 ◽  
Vol 189 ◽  
pp. 04015
Author(s):  
Heng Zhang ◽  
Zhongming Pan
Keyword(s):  
Objective Function ◽  
Moving Average ◽  
Linear Equations ◽  
Optimal Solution ◽  
Arma Model ◽  
Target Localization ◽  
Autoregressive Moving Average ◽  
Arma Process ◽  
Popular Subject ◽  
Noise Sequence

Multi-target localization methods for locating of the movingtarget in interested area monitored by Wireless Sensor Networks (WSNs) are nowadays a popular subject of study. The methods can be classified into two categories: range-free algorithm and range-based algorithm. In this work, we propose a novel multi-target localization method, which belongs to the category of range-based algorithm, by using a genetic algorithm (GA) for searching optimal solution of the objective function of multi-target localization. The objective function is only a group of linear equations with independent variables of acoustic energies calculated at each sensor-node in a WSN. However, application of the method, the accuracy of multi-target localization is sensitive to the SNR of the measured sound signals at each node, thus a denoising strategy should be inserted into the method. It turned out that the measured sound noise, comparing intrinsic sensor noise and environmental noise, may be considered as an Autoregressive Moving Average (ARMA) process. Thus, by building the ARMA model, the noise sequence commingled with the target signals can be predicted. As a consequence, the power of the noises can be subtracted from the measured sound signals for revealing the target signal's power. The results in present work demonstrate the advantage of the proposed method.

ARMA Modeling of Artificial Accelerograms for Algeria

2011 ◽  
Vol 105-107 ◽  
pp. 348-355
Author(s):  
A. Menasri ◽  
M. Brahimi ◽  
R. Frank ◽  
A. Bali
Keyword(s):  
Earthquake Engineering ◽  
Time Window ◽  
Moving Average ◽  
Near Field ◽  
Gaussian White Noise ◽  
Soil Layer ◽  
Frequency Content ◽  
Arma Process ◽  
The Real ◽  
Minimum Number

The main aim of this study is to examine on the real and simulated earthquakes effects. This paper deals with the use of ARMA models in earthquake engineering. The time-varying auto regressive moving average (ARMA) process is used as a simple yet efficient method for simulating earthquake ground motions. This model is capable of reproducing the nonstationary amplitude as well as the frequency content of the earthquake ground accelerations. The moving time-window technique is applied to synthesize the near field earthquakes, Chlef-1, Chlef-2, Chlef-3 and Attaf 1980 recorded on dense soils in Algeria. This model, is based on a low-order, time-invariant ARMA process excited by Gaussian white noise and amplitude modulated using a simple envelope function to account for the non-stationary characteristics. This simple model gives a reasonable fit to the observed ground motion. It is shown that the selected ARMA (2,1) model and the algorithm used for generating the accelerograms are able to preserve the features of the real earthquake records with different frequency content. In this evaluation, the linear and non linear responses of a given soil layer have been adopted. This study suggests the ability to characterize the earthquake by a minimum number of parameters.

ARMA processes have maximal entropy among time series with prescribed autocovariances and impulse responses

1985 ◽  
Vol 17 (4) ◽  
pp. 810-840 ◽  
Author(s):  
Jürgen Franke
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Moving Average ◽  
Autoregressive Process ◽  
Natural Generalization ◽  
Maximal Entropy ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Arma Processes

The maximum-entropy approach to the estimation of the spectral density of a time series has become quite popular during the last decade. It is closely related to the fact that an autoregressive process of order p has maximal entropy among all time series sharing the same autocovariances up to lag p. We give a natural generalization of this result by proving that a mixed autoregressive-moving-average process (ARMA process) of order (p, q) has maximal entropy among all time series sharing the same autocovariances up to lag p and the same impulse response coefficients up to lag q. The latter may be estimated from a finite record of the time series, for example by using a method proposed by Bhansali (1976). By the way, we give a result on the existence of ARMA processes with prescribed autocovariances up to lag p and impulse response coefficients up to lag q.

MODELING POLIO DATA USING THE FIRST ORDER NON-NEGATIVE INTEGER-VALUED AUTOREGRESSIVE, INAR(1), MODEL

2012 ◽  
Vol 09 ◽  
pp. 232-239 ◽  
Author(s):  
TURAJ VAZIFEDAN ◽  
MAHENDRAN SHITAN
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Negative Integer ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Road Accidents ◽  
Arma Process ◽  
Number Of Patients ◽  
Number Of Customers

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

scholarly journals PERAMALAN INFLASI DI INDONESIA MENGGUNAKAN METODE AUTOREGRESSIVE MOVING AVERAGE (ARMA)

2021 ◽  
Vol 4 (2) ◽  
pp. 67-74
Author(s):  
Cheryl Ayu Melyani ◽  
Atsila Nurtsabita ◽  
Ghaitsa Zahira Shafa ◽  
Edy Widodo
Keyword(s):  
Inflation Rate ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Data Line ◽  
Inflation Rates ◽  
Forecasting Inflation ◽  
The Government ◽  
Forecasting Analysis ◽  
Graphic Pattern

A good inflation rate for a country is an inflation rate that has a low and stable value so that able to realize fast and controlled economic growth. Forecasting can be one of the steps that can provide an overview of the value of inflation in Indonesia for the government or related agencies to formulate and maintain inflation stability in Indonesia. In this study, a forecasting analysis was carried out to determine the prediction of inflation in Indonesia in 2021 using the Autoregressive Moving Average (ARMA) method. From the results of the research that has been done, the best model to predict this case is using the ARMA model (3,0,0) because it produces the smallest AIC value of 0.2373 and the smallest RMSE of 7.81. From this model, the results of forecasting inflation rates for the months of May to December 2021 are also obtained with a range of 0.1% to 0.3%. The graphic pattern of the predicted results follows the actual data line pattern, which means that this model is good to use. Abstrak Tingkat inflasi yang baik bagi suatu negara adalah tingkat inflasi yang memiliki nilai yang rendah dan stabil, sehinga mampu mewujudkan pertumbuhan ekonomi yang cepat dan terkendali. Peramalan dapat menjadi salah satu langkah yang dapat memberikan gambaran nilai inflasi di Indonesia bagi pemerintah atau badan yang terkait untuk menyusun dan mempertahankan kestabilan inflasi di Indonesia. Dalam penelitian ini, dilakukan analisis peramalan untuk mengetahui prediksi angka inflasi di Indonesia tahun 2021 menggunakan metode Autoregresif Moving Average (ARMA). Dari hasil penelitian yang telah dilakukan, model terbaik untuk meramalkan kasus ini yaitu menggunakan model ARMA (3,0,0) karena menghasilkan nilai AIC paling kecil yaitu 0.2373 dan RMSE terkecil sebesar 7.81. Dari model tersebut juga didapatkan hasil peramalan angka inflasi untuk bulan Mei hingga Desember 2021 dengan kisaran 0.1% hingga 0.3%. Pola grafik dari hasil prediksi mengikuti pola garis data aktual yang berarti bahwa model ini baik untuk digunakan.

scholarly journals Semiparametric estimation of the canonical permanent‐transitory model of earnings dynamics

2019 ◽  
Vol 10 (4) ◽  
pp. 1495-1536 ◽  
Author(s):  
Yingyao Hu ◽  
Robert Moffitt ◽  
Yuya Sasaki
Keyword(s):  
Moving Average ◽  
State Space Model ◽  
Estimation Method ◽  
Higher Order ◽  
Autoregressive Moving Average ◽  
Earnings Mobility ◽  
Arma Process ◽  
Earnings Dynamics ◽  
Prior Literature ◽  
Transitory Component

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature.

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