2 edition of **Testing of unit root and other nonstationary hypotheses in macroeconomic time series** found in the catalog.

Testing of unit root and other nonstationary hypotheses in macroeconomic time series

L.A Gil-AlanМѓa

- 105 Want to read
- 37 Currently reading

Published
**1996**
by Suntory and Toyota International Centres for Economics and Related Disciplines in London
.

Written in English

**Edition Notes**

Statement | by L.A. Gil-Alaña and P.M. Robinson. |

Series | Econometrics discussion paper -- no.EM/96/317 |

Contributions | Robinson, P.M., Suntory and Toyota International Centres for Economics and Related Disciplines. |

The Physical Object | |
---|---|

Pagination | 38p. ; |

Number of Pages | 38 |

ID Numbers | |

Open Library | OL16577125M |

Moreover, the limit distribution is standard, [an N(0, l)], unlike what happens with other procedures for testing unit (or fractional) roots, where the limit distribution changes with features of the regressors (see, e.g., Schmidt and Phillips ). The results presented across this paper show that the series corresponding to the temperatures. Other Sellers. from $ Other Sellers. See all 2 versions Buy used: $ It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using.

Use the residuals from step 2 to check for unit roots. If f t and p t are to be said to be cointegrated then the residual series must be stationary. We fit the models. and. in order to test the hypothesis H o: a 1 =0, unit root, residual series not stationary, no cointegration H 1: a 1 not 0, no unit root in residual series, original variables. Function ndiffs() in the package forecast is a very convenient way of determining the order of integration of a series. The arguments of this function are x, a time series, alpha, the significacnce level of the test ( by default), test= one of “kpss”, “adf”, or “pp”, which indicates the unit root test to be used; we have only studied the “adf” test.), and max.d= maximum.

J Time Ser Anal – Gil-Alana LA () Testing of unit root cycles in the US real GDP. Working Paper University of Navarra, Faculty of Economics, Pamplona, Spain Gil-Alana LA, Robinson PM () Testing of unit roots and other nonstationary hypotheses in macroeconomic time series. DIFFERENCING AND UNIT ROOT TESTS e d In the Box-Jenkins approach to analyzing time series, a key question is whether to difference th ata, i.e., to replace the raw data {x } by the differenced series {x −x }. Experience indicates that m ttt−1 ost economic time series tend to wander and are not stationary, but that differencing often yields.

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"On the Theory of Testing for Unit Roots in Observed Time Series," Review of Economic Studies, Oxford University Press, vol. 53(3), pages Nelson, Charles R & Kang, Heejoon, " Pitfalls in the Use of Time as an Explanatory Variable in Regression," Journal of Business & Economic Statistics, American Statistical Association, vol.

Testing of unit root and other nonstationary hypotheses in macroeconomic time series Abstract. Recently proposed tests for unit root and other nonstationarity of Robinson (a) are applied to an extended version of the data set used by Nelson and Plosser ().

Macroeconomic time by: L A Gil-Alaña & Peter M Robinson, "Testing of Unit Root and Other Nonstationary Hypotheses in Macroeconomic Time Series - (Now published in 'Journal of Econometrics', 80,pp)," STICERD - Econometrics Paper SeriesSuntory and Toyota International Centres for Economics and Related Disciplines, LSE.

Handle: RePEc:cep. Testing of unit roots and other nonstationary hypotheses in macroeconomic time series, (). Testing the null hypothesis of stationary against the alternative of a unit root, (). The estimation and application of long memory time series models, Author: Luis A.

Gil-Alana. A particular version of the tests of Robinson () for testing stochastic cycles in macroeconomic time series is proposed in this article. The tests have a standard limit distribution and are. The earlyyp g g and pioneering work on testing for a unit root in time series was done by Dickey and Fuller (Dickey and FullerFuller ).

The basic objective of the test is to test the null hypypothesis that φ=1 in: yt= φy t-1 + u t against the one-sided alternative φunit root vs. H 1. • The early and pioneering work on testing for a unit root in time series was done by Dickey and Fuller (Dickey and FullerFuller ).

The basic objective of the test is to test the null hypothesis that φ=1 in: yt = φyt-1 + ut against the one-sided alternative φseries contains a unit root vs. H1: series is.

Efficient Tests of Nonstationary Hypotheses. Journal of the American Statistical Association: Vol. 89, No.pp. In this article we propose a method for testing nonstationary cycles in financial time series data. We use a procedure that permits us to test unit root cycles in raw time series.

The test has several distinguishing features compared with other procedures. In particular, it has a standard null limit distribution and is the most efficient test when directed against the appropriate (fractional. Persistence and sustainability of fishing grounds footprint: Evidence from 89 countries☆.

Author links open overlay panel Sakiru Adebola Solarin a Luis A. Gil-Alana b c Carmen Lafuente d. In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either stationarity, trend stationarity or explosive root depending on the test used.

Stock, J.H. () Confidence intervals of the largest autoregressive root in U.S. macroeconomic time series. Journal of Monetary Econom – Tsay, R.S. & Tiao, G.C. () Asymptotic properties of multivariate nonstationary processes with applications to autoregressions.

presence of unit roots, however, changes the asymptotic behavior of estimators and test statistics, and a diﬀerent set of tools for unit root processes has to be applied.

We continue to illustrate the properties of a unit root time series, and discuss the issue of unit root testing. In practical applications, testing for unit roots is. Testing of unit roots and other nonstationary hypotheses in macroeconomic time series, Journal of Econometr Gil-Alana, L.A.

and P.M. Robinson (). Testing seasonal fractional integration in the UK and Japanese consumption and income, Journal of. Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): (external link). Cavaliere, G. & Taylor, A.M.R. () Testing for unit roots in time series models with non-stationary volatility.

Journal of Econometrics– Cavaliere, G. & Taylor, A.M.R. ( a) Bootstrap unit root tests for time series with nonstationary volatility. existence of a unit root is in doubt, despite the failure of Dickey-Fuller tests (and other unit root tests) to reject the unit root hypothesis. The LM statistic for the stationarity hypothesis Let y, t=1,2, T, be the observed series for which we wish to test stationarity.

Madsen, E. (), Unit Root Inference in Panel Data Models Where the Time-series Dimension is fixed: A comparison of different test, CAM working paper No. – Google Scholar Mark, N.C. and D. Sul (), Cointegration Vector Estimation by Panel DOLS and Long-run Money Demand, Oxford Bulletin of Economics and Statistics, 65, – Testing of unit root and other nonstationary hypotheses in macroeconomic time series.

Article. This article proposes tests for unit root and other forms of nonstationarity that are. Unit Root Tests Introduction Many economic and ﬁnancial time series exhibit trending behavior or non-stationarity in the mean.

Leading examples are asset prices, exchange rates and the levels of macroeconomic aggregates like real GDP. An important econometric task is determining the most appropriate form of the trend in the data. 2. TESTING CYCLES WITH THE ROBINSON™S () TESTS Robinson () proposes tests for unit roots and other forms of nonstationary hypotheses.

He considers the regression model yt =β’zt +xt, t =1,2. (5) where yt is the time series we observe, β is a (kx1) vector of unknown parameters and zt is a (kx1) vector of exogenous regressors.Engle-Granger Test for Cointegration The Engle-Granger cointegration test (, Econometrica) is essentially the unit root test applied to the residual of cointegration regression 1.

The series are cointegrated if the residual has no unit root 2. The series are not cointegrated (and the regression is spurious) if the residual has unit root.In this article, a version of the tests of Robinson (a) is employed, and it permits us to test fractional models independently of the inclusion or not of deterministic trends and of the different types of disturbances.

Moreover, the limit distribution is standard, [an N(0, l)], unlike what happens with other procedures for testing unit (or fractional) roots, where the limit distribution.