Sastry G Pantula
Biographic Data
| ID | 8920738 |
|---|---|
| NAME | Sastry G Pantula |
| GIVEN NAMES | Sastry G |
| FAMILY NAME | Pantula |
| SIGNATURE | PANTULA S G |
| AFFILIATIONS | North Carolina State University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1991 |
| LATEST PUBLICATION YEAR | 2002 |
| H-INDEX | 0 |
Determining the Order of Differencing in Autoregressive Processes
One way of handling nonstationarity in time series is to compute first differences and fit a model to the differenced series unless the differenced series also looks nonstationary. In that case, second- or higher-order differencing is done. To decide if the current degree of differencing is sufficient, one can look at the autocorrelation function for slow decay. A formal statistical test for the need to difference further is available if one is w…
A Comparison of Unit-Root Test Criteria
During the past 15 years, the ordinary least squares estimator and the corresponding pivotal statistic have been widely used for testing the unit-root hypothesis in autoregressive processes. Recently, several new criteria, based on maximum likelihood estimators and weighted symmetric estimators, have been proposed. In this article, we describe several different test criteria. Results from a Monte Carlo study that compares the power of the differe…
Asymptotic Distributions of Unit-Root Tests When the Process Is Nearly Stationary
Several test criteria are available for testing the hypothesis that the autoregressive polynomial of an autoregressive moving average process has a single unit root. Schwert (1989), using a Monte Carlo study, investigated the performance of some of the available test criteria. He concluded that the actual levels of the test criteria considered in his study are far from the specified levels when the moving average polynomial also has a root close …
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Asymptotic Distributions of Unit-Root Tests When the Process Is Nearly Stationary
Several test criteria are available for testing the hypothesis that the autoregressive polynomial of an autoregressive moving average process has a single unit root. Schwert (1989), using a Monte Carlo study, investigated the performance of some of the available test criteria. He concluded that the actual levels of the test criteria considered in his study are far from the specified levels when the moving average polynomial also has a root close …
A Comparison of Unit-Root Test Criteria
During the past 15 years, the ordinary least squares estimator and the corresponding pivotal statistic have been widely used for testing the unit-root hypothesis in autoregressive processes. Recently, several new criteria, based on maximum likelihood estimators and weighted symmetric estimators, have been proposed. In this article, we describe several different test criteria. Results from a Monte Carlo study that compares the power of the differe…
Determining the Order of Differencing in Autoregressive Processes
One way of handling nonstationarity in time series is to compute first differences and fit a model to the differenced series unless the differenced series also looks nonstationary. In that case, second- or higher-order differencing is done. To decide if the current degree of differencing is sufficient, one can look at the autocorrelation function for slow decay. A formal statistical test for the need to difference further is available if one is w…
Applied Mathematics (3 works) · Autoregressive model (3 works) · Mathematics (3 works) · Monetary Policy and Economic Impact (3 works) · Statistical hypothesis testing (3 works) · Statistics (3 works) · Unit root (3 works) · Econometrics (2 works) · Estimator (2 works) · Financial Risk and Volatility Modeling (2 works)