Mathematics

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8 pages, 287 KiB

Open AccessArticle

Revisiting the Autocorrelation of Long Memory Time Series Models

by Shelton Peiris and Richard Hunt

Mathematics 2023, 11(4), 817; https://doi.org/10.3390/math11040817 - 6 Feb 2023

Cited by 2 | Viewed by 3676

In this article we first revisit some earlier work on fractionally differenced white noise and correct some issues with previously published formulae. We then look at vector processes and derive formula for the Autocorrelation function, which is extended in this work to a [...] Read more.

In this article we first revisit some earlier work on fractionally differenced white noise and correct some issues with previously published formulae. We then look at vector processes and derive formula for the Autocorrelation function, which is extended in this work to a larger range of parameter values than considered elsewhere, and compare this with previously published work. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

22 pages, 2453 KiB

Open AccessArticle

Impact Analysis of the External Shocks on the Prices of Malaysian Crude Palm Oil: Evidence from a Structural Vector Autoregressive Model

by Mohd Syafiq Sabri, Norlin Khalid, Abdul Hafizh Mohd Azam and Tamat Sarmidi

Mathematics 2022, 10(23), 4599; https://doi.org/10.3390/math10234599 - 5 Dec 2022

Cited by 4 | Viewed by 3320

Abstract

Palm oil prices, similar to other edible oils and commodity prices, are highly sensitive to external shocks which have become particularly prominent in the wake of COVID-19 pandemic. The non-stationary nature of the palm oil price complicates the modelling and forecasting of its [...] Read more.

Palm oil prices, similar to other edible oils and commodity prices, are highly sensitive to external shocks which have become particularly prominent in the wake of COVID-19 pandemic. The non-stationary nature of the palm oil price complicates the modelling and forecasting of its behaviour. This study investigates the impact of the external and internal shocks on Malaysian palm oil (MPO) prices using the SVAR methodology. The SVAR model utilised in this study is unique in that it employs the news-based indices called the Infectious Disease Volatility Tracker (IDVT) and the Economic Policy Uncertainty Index (EPUI) as parts of the time series. News-based indices can potentially uncover essential proxies for economic and policy conditions, as well as portend the investment decision-making and in turn the commodity prices. The rationale behind this choice is to capture the impact from perception and news-based indices on the Malaysian palm oil prices. The empirical result from impulse–response function (IRF) shows that the shock in IDVT has a significant positive impact on Malaysian palm oil prices suggesting the MPO is exposed to the external factor. In addition, amongst the external variables tested, IDVT shows the longest lasting and highest positive impact on Malaysian palm oil prices. These results are in accordance with forecast error variance decomposition which indicates that IDVT shock can explain a huge portion of MPO prices especially over a longer period. The model specified in this study is also sufficiently stable and robust. This study contributes to the literature the significance of news-based indices and their capability in influencing public perception on the current macroeconomic condition, hence influencing the decision-making process of economic agents. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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13 pages, 358 KiB

Open AccessArticle

Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

by Juan D. Borrero and Jesus Mariscal

Mathematics 2022, 10(16), 2915; https://doi.org/10.3390/math10162915 - 13 Aug 2022

Cited by 6 | Viewed by 2707

Abstract

Time series forecasting is one of the main venues followed by researchers in all areas. For this reason, we develop a new Kalman filter approach, which we call the alternative Kalman filter. The search conditions associated with the standard deviation of the time [...] Read more.

Time series forecasting is one of the main venues followed by researchers in all areas. For this reason, we develop a new Kalman filter approach, which we call the alternative Kalman filter. The search conditions associated with the standard deviation of the time series determined by the alternative Kalman filter were suggested as a generalization that is supposed to improve the classical Kalman filter. We studied three different time series and found that in all three cases, the alternative Kalman filter is more accurate than the classical Kalman filter. The algorithm could be generalized to time series of a different length and nature. Therefore, the developed approach can be used to predict any time series of data with large variance in the model error that causes convergence problems in the prediction. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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20 pages, 1670 KiB

Open AccessArticle

Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution

by Yiing Fei Tan, Kok Haur Ng, You Beng Koh and Shelton Peiris

Mathematics 2022, 10(10), 1621; https://doi.org/10.3390/math10101621 - 10 May 2022

Cited by 4 | Viewed by 1838

Abstract

This paper proposes a logarithmic version of the two-component ACD (LogCACD) model with no restrictions on the sign of the model parameters while allowing the expected durations to be decomposed into the long- and short-run components to capture the dynamics of these durations. [...] Read more.

This paper proposes a logarithmic version of the two-component ACD (LogCACD) model with no restrictions on the sign of the model parameters while allowing the expected durations to be decomposed into the long- and short-run components to capture the dynamics of these durations. The extended generalised inverse Gaussian (EGIG) distribution is used for the error distribution as its hazard function consists of a roller-coaster shape for certain parameters’ values. An empirical application from the trade durations of International Business Machines stock index has been carried out to investigate this proposed model. Extensive comparisons are carried out to evaluate the modelling and forecasting performances of the proposed model with several benchmark models and different specifications of error distributions. The result reveals that the LogCACD_EGIG(1,1) model gives the best in-sample fit based on the Akaike information criterion and other criteria. Furthermore, the estimated parameters obtained through the maximum likelihood estimation confirm the existence of the roller-coaster-shaped hazard function. The examination of LogCACD_EGIG(1,1) model also provides the best out-of-sample forecasts evaluated based on the mean square forecast error using the Hansen’s model confidence set. Lastly, different levels of time-at-risk forecasts are provided and tested with Kupiec likelihood ratio test. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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14 pages, 2805 KiB

Open AccessArticle

Metamorphoses of Earnings in the Transport Sector of the V4 Region

by Pavol Durana, Katarina Valaskova, Roman Blazek and Jozef Palo

Mathematics 2022, 10(8), 1204; https://doi.org/10.3390/math10081204 - 7 Apr 2022

Cited by 12 | Viewed by 2071

Abstract

The transportation sector is a crucial sector of the sustainability of every national economy. Previous studies highlighted the core significance of transport enterprises in European countries over the past 60 years. The long-term sustainability of enterprises is determined by their ability to gain [...] Read more.

The transportation sector is a crucial sector of the sustainability of every national economy. Previous studies highlighted the core significance of transport enterprises in European countries over the past 60 years. The long-term sustainability of enterprises is determined by their ability to gain earnings. Thus, earnings are the synonym of significance in corporate life. The purpose of this study was to capture the lever year, the trend, and the slope of the development of earnings in the transport sector before the COVID-19 pandemic. Time series of the annual earnings of the enterprises from the close countries of the V4 region were used during a 10-year period. Buishand’s test sets the change-points of the development and indicated the values of specific central lines. The year 2013 was the lever date for the earnings of 830 Slovak and 1042 Hungarian enterprises. The year 2015 was the year of momentum for 757 Polish enterprises. The development of 397 Czech enterprises was mainly influenced by the year 2014. The results of the Mann–Kendall test detected a positive trend in the series of business finance in all countries. In addition, the Sen’s slope was estimated in the transport sector for the analyzed period 2010–2019. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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20 pages, 2272 KiB

Open AccessArticle

Coherence Coefficient for Official Statistics

by Danutė Krapavickaitė

Mathematics 2022, 10(7), 1159; https://doi.org/10.3390/math10071159 - 3 Apr 2022

Cited by 4 | Viewed by 3780

Abstract

One of the quality requirements in official statistics is coherence of statistical information across domains, in time, in national accounts, and internally. However, no measure of its strength is used. The concept of coherence is also met in signal processing, wave physics, and [...] Read more.

One of the quality requirements in official statistics is coherence of statistical information across domains, in time, in national accounts, and internally. However, no measure of its strength is used. The concept of coherence is also met in signal processing, wave physics, and time series. In the current article, the definition of the coherence coefficient for a weakly stationary time series is recalled and discussed. The coherence coefficient is a correlation coefficient between two indicators in time indexed by the same frequency components of their Fourier transforms and shows a degree of synchronicity between the time series for each frequency. The usage of this coefficient is illustrated through the coherence and Granger causality analysis of a collection of numerical economic and social statistical indicators. The coherence coefficient matrix-based non-metric multidimensional scaling for visualization of the time series in the frequency domain is a newly suggested method. The aim of this article is to propose the use of this coherence coefficient and its applications in official statistics. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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19 pages, 3112 KiB

Open AccessArticle

Time-Varying Causalities in Prices and Volatilities between the Cross-Listed Stocks in Chinese Mainland and Hong Kong Stock Markets

by Xunfa Lu, Zhitao Ye, Kin Keung Lai, Hairong Cui and Xiao Lin

Mathematics 2022, 10(4), 571; https://doi.org/10.3390/math10040571 - 12 Feb 2022

Cited by 5 | Viewed by 2572

Abstract

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics [...] Read more.

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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24 pages, 2316 KiB

Open AccessArticle

Factors Influencing Physicians Migration—A Case Study from Romania

by Simona Andreea Apostu, Valentina Vasile, Erika Marin and Elena Bunduchi

Mathematics 2022, 10(3), 505; https://doi.org/10.3390/math10030505 - 5 Feb 2022

Cited by 12 | Viewed by 4387

Abstract

Brain drain is a phenomenon that, over time, has followed an upward trend. It is an important component represented by physicians’ migration. For the country of destination, the migration of physicians offers several advantages, whereas the country of origin loses skilled and sometimes [...] Read more.

Brain drain is a phenomenon that, over time, has followed an upward trend. It is an important component represented by physicians’ migration. For the country of destination, the migration of physicians offers several advantages, whereas the country of origin loses skilled and sometimes highly trained individuals. This process will be reflected both in the efficiency of the health system (severe employment shortage) and in the quality of the health system services. After Romania’s accession to the EU, the migration of doctors intensified, significantly increasing the shortage of physicians. The purpose of this article is to identify the push factors that influence the physicians’ decision to migrate from Romania. For this, a panel regression analysis was applied, highlighting that physicians’ migration is influenced by several factors, such as the number of beds in hospitals, the number of emigrants, unemployment rate, and income. At the same time, we analyzed the extent to which public policy measures addressed to the remuneration of medical staff influenced the propensity towards external mobility of the practicing doctors, already employed and/or graduates. The results confirm that public policies can be a tool for redistributing the labor force allocation on the labor market. Moreover, the results of our analysis highlight that specific measures do not solve the system crises facing the health sector. Systemic, multidimensional changes are needed, adapted to the needs of medical services specific to the geographical area and adequate to the health status of the population. Full article

(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)

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