scholarly journals UJI LINEARITAS BERDASARKAN ESTIMASI MEAN DAN VARIANSI BERSYARAT UNTUK PROSES RUNTUN WAKTU

2009 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Supriyanto Supriyanto ◽  
Herni Utami
Keyword(s):  
Time Series ◽  
Linear Model ◽  
Test Data ◽  
Akaike Information Criterion ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Test Statistics ◽  
Bootstrap Procedure ◽  
Order Estimation

This study aims to examine the benefits of testing linearity in the case of live test data. Bootstrap procedure is used to form the estimators of the statistics. Hypothetical form is used to follow the linear model. And compare the value of criticism from the distribution of this value with the test statistics that have been calculated based on the observed time series data existing. This procedure starts with a model determines autoregression to the data. By using the Akaike information criterion, order estimation obtained from the autoregression models.

scholarly journals ESTIMATION OF AUTOREGRESSIVE ROOTS NEAR UNITY USING PANEL DATA

2000 ◽  
Vol 16 (6) ◽  
pp. 927-997 ◽  
Author(s):  
Hyungsik R. Moon ◽  
Peter C.B. Phillips
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Time Series Data ◽  
Initial Conditions ◽  
Series Data ◽  
Test Statistics ◽  
Rate Condition ◽  
Panel Models ◽  
Stochastic Trends ◽  
Efficient Extraction

Time series data are often well modeled by using the device of an autoregressive root that is local to unity. Unfortunately, the localizing parameter (c) is not consistently estimable using existing time series econometric techniques and the lack of a consistent estimator complicates inference. This paper develops procedures for the estimation of a common localizing parameter using panel data. Pooling information across individuals in a panel aids the identification and estimation of the localizing parameter and leads to consistent estimation in simple panel models. However, in the important case of models with concomitant deterministic trends, it is shown that pooled panel estimators of the localizing parameter are asymptotically biased. Some techniques are developed to overcome this difficulty, and consistent estimators of c in the region c < 0 are developed for panel models with deterministic and stochastic trends. A limit distribution theory is also established, and test statistics are constructed for exploring interesting hypotheses, such as the equivalence of local to unity parameters across subgroups of the population. The methods are applied to the empirically important problem of the efficient extraction of deterministic trends. They are also shown to deliver consistent estimates of distancing parameters in nonstationary panel models where the initial conditions are in the distant past. In the development of the asymptotic theory this paper makes use of both sequential and joint limit approaches. An important limitation in the operation of the joint asymptotics that is sometimes needed in our development is the rate condition n/T → 0. So the results in the paper are likely to be most relevant in panels where T is large and n is moderately large.

scholarly journals Methods to identify time series abnormalities and predicting issues caused by component failures

2019 ◽  
Vol 290 ◽  
pp. 02002
Author(s):  
Crina Narcisa Deac ◽  
Gicu Calin Deac ◽  
Florina Chiscop ◽  
Cicerone Laurentiu Popa
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Test Data ◽  
Industry 4.0 ◽  
Time Series Data ◽  
Series Data ◽  
Time Interval ◽  
Telemetry Data ◽  
Data Error ◽  
Component Failures

Anomaly detection is a crucial analysis topic in the field of Industry 4.0 data mining as well as knowing what is the probability that a specific machine to go down due to a failure of a component in the next time interval. In this article, we used time series data collected from machines, from both classes - time series data which leads up to the failures of machines as well as data from healthy operational periods of the machine. We used telemetry data, error logs from still operational components, maintenance records comprising historical breakdowns and replacement component to build and compare several different models. The validation of the proposed methods was made by comparing the actual failures in the test data with the predicted component failures over the test data.

Testing Increasing Sensitivity of Enrollment at Private Institutions to Tuition and other Costs

1996 ◽  
Vol 40 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Yu Hsing ◽  
Hui S. Chang
Keyword(s):  
Higher Education ◽  
Time Series ◽  
Functional Form ◽  
Time Series Data ◽  
Series Data ◽  
Standard Errors ◽  
Private Institutions ◽  
Test Statistics ◽  
Sample Period ◽  
Income Elasticities

This paper re-examines the demand for higher education at private institutions and tests if in recent years enrollment has become more sensitive to rising tuition and other related costs. Time series data between FY 1964–65 and FY 1990–91 are used as the sample. Major findings are interesting. The general functional form yields coefficients with smaller standard errors and larger value of the test statistics. The logarithmic form can be rejected at the 5% level. Tuition elasticities rose from −0.261 to −0.557 and income elasticities also increased from 0.493 to 1.093 during the sample period. Thus, enrollment has become more sensitive to changes in tuition and other costs. However, part of the loss of enrollment due to tuition increases can be recovered by rising income elasticities.

scholarly journals DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL

2020 ◽  
Vol 17 (36) ◽  
pp. 1186-1198
Author(s):  
Mustofa USMAN ◽  
N INDRYANI ◽  
WARSONO A. ◽  
AMANTO WAMILIANA
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Impulse Response ◽  
Time Series Data ◽  
Granger Causality Test ◽  
Share Price ◽  
Series Data ◽  
P Value ◽  
Test Statistics ◽  
Causality Test

The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.

Sign in / Sign up

Export Citation Format

Share Document