scholarly journals EXACT LOCAL WHITTLE ESTIMATION OF FRACTIONAL INTEGRATION WITH UNKNOWN MEAN AND TIME TREND

2009 ◽  
Vol 26 (2) ◽  
pp. 501-540 ◽  
Author(s):  
Katsumi Shimotsu
Keyword(s):  
Asymptotic Distribution ◽  
Time Trend ◽  
Fractional Integration ◽  
Economic Data ◽  
Whittle Estimation ◽  
Semiparametric Estimator ◽  
Local Whittle Estimator ◽  
The Mean ◽  
Memory Parameter ◽  
Integrated Processes

Recently, Shimotsu and Phillips (2005, Annals of Statistics 33, 1890–1933) developed a new semiparametric estimator, the exact local Whittle (ELW) estimator, of the memory parameter (d) in fractionally integrated processes. The ELW estimator has been shown to be consistent, and it has the same $N(0,{\textstyle{1 \over 4}})$ asymptotic distribution for all values of d, if the optimization covers an interval of width less than 9/2 and the mean of the process is known. With the intent to provide a semiparametric estimator suitable for economic data, we extend the ELW estimator so that it accommodates an unknown mean and a polynomial time trend. We show that the two-step ELW estimator, which is based on a modified ELW objective function using a tapered local Whittle estimator in the first stage, has an $N(0,{\textstyle{1 \over 4}})$ asymptotic distribution for $d \in (- {\textstyle{1 \over 2}},2)$ (or $d \in (- {\textstyle{1 \over 2}},{\textstyle{7 \over 4}})$ when the data have a polynomial trend). Our simulation study illustrates that the two-step ELW estimator inherits the desirable properties of the ELW estimator.

Local Whittle estimation of the long-memory parameter

2020 ◽  
Vol 20 (3) ◽  
pp. 565-583
Author(s):  
Christopher F. Baum ◽  
Stan Hurn ◽  
Kenneth Lindsay
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Fractional Integration ◽  
Whittle Estimation ◽  
Memory Parameter

In this article, we describe and implement the local Whittle and exact local Whittle estimators of the order of fractional integration of a time series.

SEMIPARAMETRIC FUNCTIONAL COEFFICIENT MODELS WITH INTEGRATED COVARIATES

2013 ◽  
Vol 29 (3) ◽  
pp. 659-672 ◽  
Author(s):  
Yiguo Sun ◽  
Zongwu Cai ◽  
Qi Li
Keyword(s):  
Recent Result ◽  
Asymptotic Distribution ◽  
Purchasing Power Parity ◽  
Stationary Processes ◽  
Varying Coefficient Models ◽  
Power Parity ◽  
Empirical Application ◽  
Semiparametric Estimator ◽  
Smooth Coefficients ◽  
Integrated Processes

AbstractCai, Li, and Park (Journal of Econometrics, 2009) and Xiao (Journal of Econometrics, 2009) developed asymptotic theories for estimators of semiparametric varying coefficient models when regressors are integrated processes but the smooth coefficients are functionals of stationary processes. Using a recent result from Phillips (Econometric Theory, 2009), we extend this line of research by allowing for both the regressors and the covariates entering the smooth functionals to be integrated variables. We derive the asymptotic distribution for the proposed semiparametric estimator. An empirical application is presented to examine the purchasing power parity hypothesis between U.S. and Canadian dollars.

scholarly journals Exact local Whittle estimation of fractional integration

2005 ◽  
Vol 33 (4) ◽  
pp. 1890-1933 ◽  
Author(s):  
Katsumi Shimotsu ◽  
Peter C. B. Phillips
Keyword(s):  
Fractional Integration ◽  
Whittle Estimation

TESTING TIME-SERIES STATIONARITY AGAINST AN ALTERNATIVE WHOSE MEAN IS PERIODIC

2001 ◽  
Vol 5 (3) ◽  
pp. 380-412 ◽  
Author(s):  
Melvin A. Hinich ◽  
Phillip Wild
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Real World ◽  
Null Hypothesis ◽  
Data Sets ◽  
Testing Time ◽  
Economic Data ◽  
The Mean ◽  
Important Form ◽  
Real World Problems

We develop a test of the null hypothesis that an observed time series is a realization of a strictly stationary random process. Our test is based on the result that the kth value of the discrete Fourier transform of a sample frame has a zero mean under the null hypothesis. The test that we develop will have considerable power against an important form of nonstationarity hitherto not considered in the mainstream econometric time-series literature, that is, where the mean of a time series is periodic with random variation in its periodic structure. The size and power properties of the test are investigated and its applicability to real-world problems is demonstrated by application to three economic data sets.

ARFIMA Tests for Random Walks in Exchange Rates in Asian, Latin American and African-Middle Eastern Markets

2010 ◽  
Vol 13 (01) ◽  
pp. 1-18 ◽  
Author(s):  
David Karemera ◽  
John Cole
Keyword(s):  
Random Walk ◽  
Exchange Rates ◽  
Random Walks ◽  
Latin American ◽  
Emerging Market ◽  
Middle Eastern ◽  
Fractional Integration ◽  
Market Exchange ◽  
Forecast Performance ◽  
Integrated Processes

This article examines fractional processes as alternatives to random walks in emerging foreign exchange rate markets. Sowell's (1992) joint maximum likelihood is used to estimate the ARFIMA parameters and test for random walks. The results show that, in most cases, the emerging market exchange rates follow fractionally integrated processes. Forecasts of exchange rates based on the fractionally integrated autoregressive moving average models are compared to those from the benchmark random walk models. A Harvey, Leybourne and Newbold (1997) test of equality of forecast performance indicates that the ARFIMA forecasts are more efficient in the multi-step-ahead forecasts than the random walk model forecasts. The presence of fractional integration is seen to be associated with market inefficiency in the exchange markets examined. The evidence suggests that fractional integrated processes are viable alternatives to random walks for describing and forecasting exchange rates in the emerging markets.

scholarly journals TESTING THE ORDER OF FRACTIONAL INTEGRATION OF A TIME SERIES IN THE POSSIBLE PRESENCE OF A TREND BREAK AT AN UNKNOWN POINT

2018 ◽  
Vol 35 (6) ◽  
pp. 1201-1233 ◽  
Author(s):  
Fabrizio Iacone ◽  
Stephen J. Leybourne ◽  
A.M. Robert Taylor
Keyword(s):  
Time Series ◽  
Power Function ◽  
Long Memory ◽  
Fractional Integration ◽  
Monte Carlo Study ◽  
Local Power ◽  
Finite Sample ◽  
Lm Test ◽  
Unknown Point ◽  
Memory Parameter

We develop a test, based on the Lagrange multiplier [LM] testing principle, for the value of the long memory parameter of a univariate time series that is composed of a fractionally integrated shock around a potentially broken deterministic trend. Our proposed test is constructed from data which are de-trended allowing for a trend break whose (unknown) location is estimated by a standard residual sum of squares estimator applied either to the levels or first differences of the data, depending on the value specified for the long memory parameter under the null hypothesis. We demonstrate that the resulting LM-type statistic has a standard limiting null chi-squared distribution with one degree of freedom, and attains the same asymptotic local power function as an infeasible LM test based on the true shocks. Our proposed test therefore attains the same asymptotic local optimality properties as an oracle LM test in both the trend break and no trend break environments. Moreover, this asymptotic local power function does not alter between the break and no break cases and so there is no loss in asymptotic local power from allowing for a trend break at an unknown point in the sample, even in the case where no break is present. We also report the results from a Monte Carlo study into the finite-sample behaviour of our proposed test.

ESTIMATION OF OCCUPATIONAL RADIATION EXPOSURE FOR MEDICAL WORKERS IN RADIOLOGY AND CARDIOLOGY IN THE UNITED ARAB EMIRATES: NINE HOSPITALS EXPERIENCE

2020 ◽  
Vol 189 (4) ◽  
pp. 466-474
Author(s):  
Wiam Elshami ◽  
Mohamed Abuzaid ◽  
Antti Pekkarinen ◽  
Mika Kortesniemi
Keyword(s):  
Radiation Exposure ◽  
Effective Dose ◽  
United Arab Emirates ◽  
Time Trend ◽  
International Standards ◽  
Occupational Radiation Exposure ◽  
High Exposure ◽  
Effective Doses ◽  
The Mean ◽  
Occupational Radiation

Abstract Purpose Occupational radiation exposure for medical workers in radiology and cardiology was analyzed in nine hospitals in the UAE between 2002 and 2016. The purpose of the study was to determine the time trend and the differences in occupational radiation exposure among worker groups and hospitals in the country. Methods Readings of 5700 thermoluminescence dosimeters (TLDs) were obtained from 1011 medical workers and grouped into 5 worker groups (radiographers, diagnostic radiologists, nurses, cardiologists and physicians). Results The mean annual effective dose was from 0.38 to 0.62 mSv per worker. Even though an increase in the collective effective dose has been noticed during the study period, no significant time trend was observed in the mean effective dose. Furthermore, cardiologists received higher mean and maximum effective doses than the other worker groups. Conclusion The annual effective doses were below the limits set by national legislation and international standards, and for the average worker, the likelihood of high exposure is small. However, improvements in radiation protection practices could be implemented to reduce occupational radiation dose to cardiologists, who were the most exposed worker group in this study.

scholarly journals Bayesian geostatistical modelling for mapping schistosomiasis transmission

Parasitology ◽  
2009 ◽  
Vol 136 (13) ◽  
pp. 1695-1705 ◽  
Author(s):  
P. VOUNATSOU ◽  
G. RASO ◽  
M. TANNER ◽  
E. K. N'GORAN ◽  
J. UTZINGER
Keyword(s):  
Negative Binomial ◽  
Infection Intensity ◽  
Spatial Process ◽  
Economic Data ◽  
Excess Zeros ◽  
Risk Profiling ◽  
Morbidity Control ◽  
The Mean ◽  
Binomial Models ◽  
Made In

SUMMARYProgress has been made in mapping and predicting the risk of schistosomiasis using Bayesian geostatistical inference. Applications primarily focused on risk profiling of prevalence rather than infection intensity, although the latter is particularly important for morbidity control. In this review, the underlying assumptions used in a study mapping Schistosoma mansoni infection intensity in East Africa are examined. We argue that the assumption of stationarity needs to be relaxed, and that the negative binomial assumption might result in misleading inference because of a high number of excess zeros (individuals without an infection). We developed a Bayesian geostatistical zero-inflated (ZI) regression model that assumes a non-stationary spatial process. Our model is validated with a high-quality georeferenced database from western Côte d'Ivoire, consisting of demographic, environmental, parasitological and socio-economic data. Nearly 40% of the 3818 participating schoolchildren were infected with S. mansoni, and the mean egg count among infected children was 162 eggs per gram of stool (EPG), ranging between 24 and 6768 EPG. Compared to a negative binomial and ZI Poisson and negative binomial models, the Bayesian non-stationary ZI negative binomial model showed a better fit to the data. We conclude that geostatistical ZI models produce more accurate maps of helminth infection intensity than the spatial negative binomial ones.

Time spent below a random threshold by a Poisson driven sequence of observations

2003 ◽  
Vol 40 (03) ◽  
pp. 807-814 ◽  
Author(s):  
S. N. U. A. Kirmani ◽  
Jacek Wesołowski
Keyword(s):  
Poisson Process ◽  
Asymptotic Distribution ◽  
Random Variables ◽  
Nonhomogeneous Poisson Process ◽  
System Level ◽  
Homogeneous Poisson Process ◽  
Random Threshold ◽  
The Mean ◽  
Current Value ◽  
Independent And Identically Distributed

The mean and the variance of the time S(t) spent by a system below a random threshold until t are obtained when the system level is modelled by the current value of a sequence of independent and identically distributed random variables appearing at the epochs of a nonhomogeneous Poisson process. In the case of the homogeneous Poisson process, the asymptotic distribution of S(t)/t as t → ∞ is derived.

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