scholarly journals Parallels and Differences in Earnings Management of the Visegrad Four and the Baltics

2021 ◽  
Vol 13 (3) ◽  
pp. 39-55
Author(s):  
Pavol Durana ◽  
Romualdas Ginevicius ◽  
Mariusz Urbanski ◽  
Ivana Podhorska ◽  
Milos Tumpach
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Earnings Management ◽  
Unit Roots ◽  
Emerging Countries ◽  
Econometric Modeling ◽  
Final Sample ◽  
Positive Perspective ◽  
Visegrad Group ◽  
Enterprise's Competitiveness

Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the enterprise’s competitiveness. Managers purposely manipulate business earnings to achieve the required status of the enterprise. The consequence of these activities is to provide a positive perspective for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. This article aims to identify parallels and differences in earnings management of enterprises in the Visegrad Four and the Baltics in terms of competitiveness for the nineyear period 2010-2018. The research uses a final sample of 4,543 observations from the EBITs of Slovak, Czech, Hungarian and Polish enterprises as well as 1,633 observations from the EBITs of Latvian, Lithuanian and Estonian enterprises. Time-series methods with all necessary assumptions have been run for the analyzed financial dataset. The results of the econometric modeling of unit roots show significant parallels in these groups of countries. The enterprises from the Visegrad group and the Baltics group use the apparatus of earnings management to be competitive. The obtained results confirm the systematic but legal manipulation from the side of management. A quantitative analysis of homogeneity tests using 1,000,000 Monte Carlo simulations indicates significant time differences of manipulation in these emerging countries. The year 2014 signaled a radical “accelerando” in earnings management for the V4, and the year 2016 is highlighted for the Baltics.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

An Enquiry Into Linear Conflict Models

2021 ◽  
Author(s):  
Klaus B. Beckmann ◽  
Lennart Reimer
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Bounded Rationality ◽  
Dynamic Models ◽  
Conflict Analysis ◽  
Monte Carlo Techniques ◽  
Linear Dynamic ◽  
Rich Variety ◽  
Time Series Analyses ◽  
Linear Dynamic Models

This monograph generalises, and extends, the classic dynamic models in conflict analysis (Lanchester 1916, Richardson 1919, Boulding 1962). Restrictions on parameters are relaxed to account for alliances and for peacekeeping. Incrementalist as well as stochastic versions of the model are reviewed. These extensions allow for a rich variety of patterns of dynamic conflict. Using Monte Carlo techniques as well as time series analyses based on GDELT data (for the Ethiopian-Eritreian war, 1998–2000), we also assess the empirical usefulness of the model. It turns out that linear dynamic models capture selected phases of the conflict quite well, offering a potential taxonomy for conflict dynamics. We also discuss a method for introducing a modicum of (bounded) rationality into models from this tradition.

Testing for Unit Roots in Seasonal Time Series

1984 ◽  
Vol 79 (386) ◽  
pp. 355-367 ◽  
Author(s):  
D. A. Dickey ◽  
D. P. Hasza ◽  
W. A. Fuller
Keyword(s):  
Time Series ◽  
Unit Roots ◽  
Seasonal Time Series

scholarly journals Conception of Geomagnetic Data Integrated Space

2019 ◽  
Vol 18 (2) ◽  
pp. 390-415
Author(s):  
Andrei Vorobev ◽  
Gulnara Vorobeva ◽  
Nafisa Yusupova
Keyword(s):  
Time Series ◽  
Relevant Information ◽  
Data Sets ◽  
Spatial Anisotropy ◽  
Automatic Integration ◽  
Final Sample ◽  
Geomagnetic Data ◽  
Consolidation Model ◽  
Discretization Step ◽  
Original Time

. As is known, today the problem of geomagnetic field and its variations parameters monitoring is solved mainly by a network of magnetic observatories and variational stations, but a significant obstacle in the processing and analysis of the data thus obtained, along with their spatial anisotropy, are omissions or reliable inconsistency with the established format. Heterogeneity and anomalousness of the data excludes (significantly complicates) the possibility of their automatic integration and the application of frequency analysis tools to them. Known solutions for the integration of heterogeneous geomagnetic data are mainly based on the consolidation model and only partially solve the problem. The resulting data sets, as a rule, do not meet the requirements for real-time information systems, may include outliers, and omissions in the time series of geomagnetic data are eliminated by excluding missing or anomalous values from the final sample, which can obviously lead to both to the loss of relevant information, violation of the discretization step, and to heterogeneity of the time series. The paper proposes an approach to creating an integrated space of geomagnetic data based on a combination of consolidation and federalization models, including preliminary processing of the original time series with an optionally available procedure for their recovery and verification, focused on the use of cloud computing technologies and hierarchical format and processing speed of large amounts of data and, as a result, providing users with better and more homogeneous data.

scholarly journals PENGARUH INFORMASI LABA TERHADAP REAKSI PASAR DENGAN MANAJEMEN LABA RIIL DAN MANAJEMEN LABA AKRUAL SEBAGAI VARIABEL MODERASI

2019 ◽  
Vol 13 (1) ◽  
pp. 1-23
Author(s):  
Santi Santi ◽  
Kurniawati Kurniawati
Keyword(s):  
Earnings Management ◽  
Market Reaction ◽  
Stock Exchange ◽  
Real Earnings Management ◽  
Manufacturing Companies ◽  
Statistical Tool ◽  
Significance Level ◽  
Moderating Variables ◽  
Earnings Information ◽  
Final Sample

This study aims to investigate the effect of earnings information on market reaction with accrual and real earnings management as the moderating variables. The sample of this study is manufacturing companies listed in the Indonesia Stock Exchange in 2012-2015. Samples are collected by purposive sampling and resulted in 58 companies as the final sample. Data were analyzed using Moderated Regression Analysis (MRA) for testing hypothesis with significance level 5%. The statistical tool used is SPSS 22. The results of this study shown that market reacts positively significant toward earnings management and real earnings management in aggregate weaken the effect of earnings information toward market reaction. Real earnings management through discretionary expenses strengthen the effect of earnings information toward market reaction. Meanwhile, real earnings management through sales manipulation and overproduction, and accrual earnings management do not moderate the effect of earnings information toward market reaction.

scholarly journals Time-Series Momentum: A Monte Carlo Approach

2019 ◽  
Vol 1 (4) ◽  
pp. 103-123
Author(s):  
Clemens Struck ◽  
Enoch Cheng
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Approach
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