scholarly journals UK overseas visitors: Seasonality and persistence

2018 ◽  
Vol 25 (5) ◽  
pp. 827-831
Author(s):  
Guglielmo Maria Caporale ◽  
Luis Alberiko Gil-Alana
Keyword(s):  
Long Memory ◽  
Fractional Integration ◽  
Integration Framework ◽  
Quarterly Data

This article analyses seasonality and persistence in the number of UK overseas visitors applying a fractional integration framework to (monthly and quarterly) data from 1986 to 2017. The results indicate that long memory is present in the series and the degree of persistence is higher for seasonally adjusted data, with shocks having transitory but long-lasting effects.

scholarly journals Particulate matter 10 (PM10): persistence and trends in eight European capitals

2021 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Luis A. Gil-Alana ◽  
Nieves Carmona-González
Keyword(s):  
Particulate Matter ◽  
Long Memory ◽  
Air Pollutants ◽  
Fractional Integration ◽  
Mean Reversion ◽  
Statistical Properties ◽  
Standard Approach ◽  
Integration Framework

AbstractThis paper examines the statistical properties of daily PM10 in eight European capitals (Amsterdam, Berlin, Brussels, Helsinki, London, Luxembourg, Madrid and Paris) over the period 2014–2020 by applying a fractional integration framework; this is more general than the standard approach based on the classical dichotomy between I(0) stationary and I(1) non-stationary series used in most other studies on air pollutants. All series are found to be characterised by long memory and fractional integration, with orders of integration in the range (0, 1), which implies that mean reversion occurs and shocks do not have permanent effects. Persistence is the highest in the case of Brussels, Amsterdam and London. The presence of negative trends in Brussels, Paris and Berlin indicates some degree of success in reducing pollution in these capitals.

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.

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.

scholarly journals Modelling International Monthly Tourist in Spain*

2020 ◽  
Vol 29 (3) ◽  
pp. 723-736
Author(s):  
Juncal Cuñado ◽  
Luis Alberiko Gil-Alana ◽  
Fernando Perez De Gracia
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Fractional Integration ◽  
Structural Break ◽  
A Value ◽  
Integration Techniques ◽  
The Mean

This article investigates the degree of persistence in the international monthly tourist time series in Spain using long memory (fractional integration) techniques. Our findings can be summarized as follows. The two standard hypotheses of integer degrees of differentiation, i.e., the I(0) and the I(1) behaviour, are clearly rejected. The series is found to be I(d) with a value of d in the interval (0.421, 0.780) thus implying long memory behaviour and mean reverting behaviour. However, if a structural break is considered, it takes place at May 2007, and then, the two subsamples present orders of integration which are above 1 and thus rejecting the mean reverting hypothesis.

scholarly journals Analysing the relationship between CO2 emissions and GDP in China: a fractional integration and cointegration approach

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Gloria Claudio-Quiroga ◽  
Luis A. Gil-Alana
Keyword(s):  
Economic Growth ◽  
Long Memory ◽  
Fractional Integration ◽  
Environmental Policies ◽  
Dynamic Processes ◽  
Standard Methods ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Cointegration Tests ◽  
The Relationship

AbstractThis paper examines the relationship between the logarithms of carbon dioxide (CO2) emissions and real Gross Domestic Product (GDP) in China by applying fractional integration and cointegration methods. These are more general than the standard methods based on the dichotomy between stationary and non-stationary series, allow for a much wider variety of dynamic processes, and provide information about the persistence and long-memory properties of the series and thus on whether or not the effects of shocks are long-lived. The univariate results indicate that the two series are highly persistent, their orders of integration being around 2, whilst the cointegration tests (using both standard and fractional techniques) imply that there exists a long-run equilibrium relationship between the two variables in first differences, i.e. their growth rates are linked together in the long run. This suggests the need for environmental policies aimed at reducing emissions during periods of economic growth.

scholarly journals Modeling Long Range Dependence in Wheat Food Price Returns

2019 ◽  
Vol 11 (9) ◽  
pp. 46
Author(s):  
Naveen Musunuru
Keyword(s):  
Long Range ◽  
Long Memory ◽  
Fractional Integration ◽  
Food Price ◽  
Long Range Dependence ◽  
Price Shocks ◽  
Fractional Difference ◽  
Long Time ◽  
Econometric Tests ◽  
Figarch Model

The present paper focuses on analyzing the volatility dynamics of wheat commodity based on the presence of long memory. The paper utilizes several econometric tests to identify the presence and magnitude of the fractional difference parameter. Fractional GARCH models, namely FIGARCH and FIEGARCH, are employed to examine the long memory property. Twenty years of wheat daily price data were used to study the long-range dependence. The results reveal that fractional integration is found in the daily wheat price return series. Overall, the FIGARCH model seems a better fit, in describing the time-varying volatility of the commodity adequately, compared to the FIEGARCH model. Food price shocks are likely to persist for a long time for wheat, resulting in higher market risk for producers and increased purchasing costs for consumers.

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