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.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Identification of Armax Models With Time Dependent Coefficients

2002 ◽  
Vol 124 (3) ◽  
pp. 464-467 ◽  
Author(s):  
R. Ben Mrad ◽  
E. Farag
Keyword(s):  
Moving Average ◽  
Estimation Method ◽  
Initial Guess ◽  
Autoregressive Moving Average ◽  
Hydraulic Actuator ◽  
Prediction Ability ◽  
Signal Prediction ◽  
Highly Nonlinear ◽  
Good Signal ◽  
Stochastic Time

A method that uses input/output measurements is developed for the estimation of the coefficients of stochastic Time-varying AutoRegressive Moving Average with eXogeneous imputs (TARMAX) models. The TARMAX coefficients are expressed as linear combinations of a set of pre-selected functions. The model coefficients estimation method is fully based on linear operations, does not require initial guess values and is suitable for micro-computer implementation. The good performance of the estimation method is verified through numerical examples. A TARMAX model is also used to capture the dynamics of a detailed highly nonlinear model of an automobile hydraulic active suspension system. The TARMAX model is used to relate a desired force provided by a corner processor to the actual force generated by the hydraulic actuator. The TARMAX model is shown to provide good signal prediction ability.

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.

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.

Statistical properties of breath-to-breath variations in ventilation at constant Pet CO2 and Pet O2 in humans

1996 ◽  
Vol 81 (5) ◽  
pp. 2274-2286 ◽  
Author(s):  
Pei-Ji Liang ◽  
Jaideep J. Pandit ◽  
Peter A. Robbins
Keyword(s):  
State Space ◽  
Spectral Power ◽  
Moving Average ◽  
State Space Model ◽  
Statistical Properties ◽  
Autoregressive Moving Average ◽  
Data Sets ◽  
Arma Models ◽  
Space Model ◽  
End Tidal

Liang, Pei-Ji, Jaideep J. Pandit, and Peter A. Robbins. Statistical properties of breath-to-breath variations in ventilation at constant Pet CO2 and Pet O2 in humans. J. Appl. Physiol. 81(5): 2274–2286, 1996.—The purpose of this study was to provide a statistical description of the breath-to-breath variations in ventilation during steady breathing in both rest and during light exercise, with the end-tidal gases controlled by using an end-tidal forcing system. Sixty data sets were studied, only one of which was white (i.e., did not show autocorrelation). Three simple autoregressive moving average (ARMA) models, i.e., AR1, AR2, and AR1MA1, and one simple state-space model were fitted to the data and resulted in white residuals in 15, 31, 46, and 48 out of 60 occasions, respectively. Evolutionary spectral analysis revealed that only 13 data sets had a constant power spectrum, although 50 were uniformly modulated. An autoregressive estimate of variance could be used to “demodulate” the data in most cases, but the results were not significantly affected by fitting the model to the demodulated data. The results indicate that 1) both simple ARMA models and a simple state-space model can describe the autocorrelation present; 2) variations in spectral power were present in the data that cannot be described by these models; and 3) these variations were often due to a uniform modulation and did not significantly affect the coefficients for the models. For these kinds of data, a heteroscedastic form of state-space model provides an attractive theoretical structure for the noise processes.

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.

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