scholarly journals Estimating accrual-based models in Poland: the time series data approach and the cross-sectional data approach

2021 ◽  
Vol 2021 (1) ◽  
pp. 205-237
Author(s):  
Michał Comporek
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
The Cross

scholarly journals Estimation with Aggregate Shocks

2019 ◽  
Vol 87 (3) ◽  
pp. 1365-1398 ◽  
Author(s):  
Jinyong Hahn ◽  
Guido Kuersteiner ◽  
Maurizio Mazzocco
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Model Parameters ◽  
Test Statistics ◽  
Cross Sectional ◽  
Aggregate Shocks ◽  
Combined Use ◽  
The Cross ◽  
Estimation Of Model Parameters

Abstract Aggregate shocks affect most households’ and firms’ decisions. Using three stylized models, we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents’ decision problem relative to aggregate shocks. Our framework and examples illustrate that the cross-sectional and time-series aspects of the model are often interdependent. Therefore, estimation of model parameters in the presence of aggregate shocks requires the combined use of cross-sectional and time-series data. We provide easy-to-use formulas for test statistics and confidence intervals that account for the interaction between the cross-sectional and time-series variation. Lastly, we perform Monte Carlo simulations that highlight the properties of the proposed method and the risks of not properly accounting for the presence of aggregate shocks.

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

Influence of Inclination Angle on Intermittent Two Phase Flows

2016 ◽  
Author(s):  
Josep Escrig Escrig ◽  
Buddhika Hewakandamby ◽  
Georgios Dimitrakis ◽  
Barry Azzopardi
Keyword(s):  
Time Series ◽  
Void Fraction ◽  
Time Series Data ◽  
Silicone Oil ◽  
Wire Mesh ◽  
Series Data ◽  
Intermittent Flow ◽  
Two Phase ◽  
Cross Sectional ◽  
Inclined Pipes

Intermittent gas and liquid two-phase flow was generated in a 6 m × 67 mm diameter pipe mounted rotatable frame (vertical up to −20°). Air and a 5 mPa s silicone oil at atmospheric pressure were studied. Gas superficial velocities between 0.17 and 2.9 m/s and liquid superficial velocities between 0.023 and 0.47 m/s were employed. These runs were repeated at 7 angles making a total of 420 runs. Cross sectional void fraction time series were measured over 60 seconds for each run using a Wire Mesh Sensor and a twin plane Electrical Capacitance Tomography. The void fraction time series data were analysed in order to extract average void fraction, structure velocities and structure frequencies. Results are presented to illustrate the effect of the angle as well as the phase superficial velocities affect the intermittent flows behaviour. Existing correlations suggested to predict average void fraction and gas structures velocity and frequency in slug flow have been compared with new experimental results for any intermittent flow including: slug, cap bubble and churn. Good agreements have been seen for the gas structure velocity and mean void fraction. On the other hand, no correlation was found to predict the gas structure frequency, especially in vertical and inclined pipes.

scholarly journals The Effect of the Global Financial Crisis on the Level of Accounting Conservatism in Commercial Banks: Evidence from Jordan

2017 ◽  
Vol 12 (2) ◽  
pp. 151 ◽  
Author(s):  
Yusuf Ali Al-Hroot ◽  
Laith Akram Muflih AL-Qudah ◽  
Faris Irsheid Audeh Alkharabsha
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Commercial Banks ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Ordinary Least Squares ◽  
Accounting Conservatism ◽  
Series Data ◽  
Cross Sectional ◽  
The Global Financial Crisis

This paper intends to investigate whether the financial crisis (2008) exerted an impact on the level of accounting conservatism in the case of Jordanian commercial banks before and during the financial crisis. The sample of this study includes 78 observations; these observations are based on the financial statements of all commercial banks in Jordan and may be referred to as cross-sectional data, whereas the period from 2005 to 2011 represents a range of years characterized by time series data. The appropriate regression model to measure the relationship between cross-sectional data and time series data is in this case the pooled data regression (PDR) using the ordinary least squares (OLS) method. The results indicate that the level of accounting conservatism had been steadily increasing over a period of three years from 2005 to 2007. The results also indicate that the level of accounting conservatism was subjected to an increase during crisis period between 2009 and 2011 compared with the level of accounting conservatism for the period 2005-2007 preceding the global financial crisis. The F-test was used in order to test the significant differences between the regression coefficients for the period before and during the global financial crisis. The results indicate a positive impact on the accounting conservatism during the global financial crisis compared with the period before the global financial crisis. The p-value is 0.040 which indicates that there are statistically significant differences between the two periods; these results are consistent with the results in Sampaio (2015).

Time Series Analysis in Pooled Cross-Sections

1986 ◽  
Vol 2 (3) ◽  
pp. 331-349 ◽  
Author(s):  
John J. Beggs
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Sections ◽  
Spectral Methods ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Series Analysis ◽  
Simulation Results ◽  
Spectral Representations

This article proposes the use of spectral methods to pool cross-sectional replications (N) of time series data (T) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.

Applying Circular Statistics to the Analysis of Monitoring Data

2007 ◽  
Vol 23 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Thomas Kubiak ◽  
Cornelia Jonas
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ambulatory Monitoring ◽  
Circular Data ◽  
Circular Statistics ◽  
Series Data ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Wide Range ◽  
Time Series Approach

Abstract. Patterns of psychological variables in time have been of interest to research from the beginning. This is particularly true for ambulatory monitoring research, where large (cross-sectional) time-series datasets are often the matter of investigation. Common methods for identifying cyclic variations include spectral analyses of time-series data or time-domain based strategies, which also allow for modeling cyclic components. Though the prerequisites of these sophisticated procedures, such as interval-scaled time-series variables, are seldom met, their usage is common. In contrast to the time-series approach, methods from a different field of statistics, directional or circular statistics, offer another opportunity for the detection of patterns in time, where fewer prerequisites have to be met. These approaches are commonly used in biology or geostatistics. They offer a wide range of analytical strategies to examine “circular data,” i.e., data where period of measurement is rotationally invariant (e.g., directions on the compass or daily hours ranging from 0 to 24, 24 being the same as 0). In psychology, however, circular statistics are hardly known at all. In the present paper, we intend to give a succinct introduction into the rationale of circular statistics and describe how this approach can be used for the detection of patterns in time, contrasting it with time-series analysis. We report data from a monitoring study, where mood and social interactions were assessed for 4 weeks in order to illustrate the use of circular statistics. Both the results of periodogram analyses and circular statistics-based results are reported. Advantages and possible pitfalls of the circular statistics approach are highlighted concluding that ambulatory assessment research can benefit from strategies borrowed from circular statistics.

Legislative Production in Comparative Perspective: Cross-Sectional Study of 42 Countries and Time-Series Analysis of the Japan Case

2008 ◽  
Vol 9 (1) ◽  
pp. 1-19 ◽  
Author(s):  
KENTARO FUKUMOTO
Keyword(s):  
Time Series ◽  
Marginal Cost ◽  
Time Series Data ◽  
Cross Sectional Study ◽  
Series Data ◽  
Divided Government ◽  
Sectional Study ◽  
Cross Sectional ◽  
Marginal Benefit ◽  
Legislative Production

AbstractLegislative scholars have debated what factors (e.g. divided government) account for the number of important laws a legislative body passes per year. This paper presents a monopoly model for explaining legislative production. It assumes that a legislature adjusts its law production so as to maximize its utility. The model predicts that socio-economic and political changes increase the marginal benefit of law production, whereas low negotiation costs and ample legislative resources decrease the marginal cost of law production. The model is tested in two ways. The first approach compares the legislatures of 42 developed and developing countries. The second analyzes Japanese lawmaking from 1949 to 1990, using an appropriate method for event count time series data. Both empirical investigations support the model's predictions for legislative production.

scholarly journals Time trends in mental health indicators during the initial 16 months of the COVID-19 pandemic in Denmark

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michelle T. Pedersen ◽  
Thea O. Andersen ◽  
Amy Clotworthy ◽  
Andreas K. Jensen ◽  
Katrine Strandberg-Larsen ◽  
...  
Keyword(s):  
Mental Health ◽  
Time Series ◽  
Mental Illness ◽  
Chronic Illness ◽  
Time Trends ◽  
Time Series Data ◽  
Health Indicators ◽  
Series Data ◽  
Cross Sectional ◽  
Mental Health Questionnaire

Abstract Background The COVID-19 pandemic and its associated national lockdowns have been linked to deteriorations in mental health worldwide. A number of studies analysed changes in mental health indicators during the pandemic; however, these studies generally had a small number of timepoints, and focused on the initial months of the pandemic. Furthermore, most studies followed-up the same individuals, resulting in significant loss to follow-up and biased estimates of mental health and its change. Here we report on time trends in key mental health indicators amongst Danish adults over the course of the pandemic (March 2020 - July 2021) focusing on subgroups defined by gender, age, and self-reported previously diagnosed chronic and/or mental illness. Methods We used time-series data collected by Epinion (N=8,261) with 43 timepoints between 20 March 2020 and 22 July 2021. Using a repeated cross-sectional study design, independent sets of individuals were asked to respond to the Copenhagen Corona-Related Mental Health questionnaire at each timepoint, and data was weighted to population proportions. The six mental health indicators examined were loneliness, anxiety, social isolation, quality of life, COVID-19-related worries, and the mental health scale. Gender, age, and the presence of previously diagnosed mental and/or chronic illness were used to stratify the population into subgroups for comparisons. Results Poorer mental health were observed during the strictest phases of the lockdowns, whereas better outcomes occurred during reopening phases. Women, young individuals (<34 yrs), and those with a mental- and/or chronic illness demonstrated poorer mean time-series than others. Those with a pre-existing mental illness further had a less reactive mental health time-series. The greatest differences between women/men and younger/older age groups were observed during the second lockdown. Conclusions People with mental illness have reported disadvantageous but stable levels of mental health indicators during the pandemic thus far, and they seem to be less affected by the factors that result in fluctuating time-series in other subgroups.

scholarly journals Have e-cigarettes renormalised or displaced youth smoking? Results of a segmented regression analysis of repeated cross sectional survey data in England, Scotland and Wales

2019 ◽  
pp. tobaccocontrol-2018-054584 ◽  
Author(s):  
Britt Hallingberg ◽  
Olivia M Maynard ◽  
Linda Bauld ◽  
Rachel Brown ◽  
Linsay Gray ◽  
...  
Keyword(s):  
Time Series ◽  
Young People ◽  
Rapid Growth ◽  
Time Series Data ◽  
Sensitivity Analyses ◽  
Series Data ◽  
Youth Smoking ◽  
Interrupted Time Series Analysis ◽  
Cross Sectional ◽  
Rate Of Decline

ObjectivesTo examine whether during a period of limited e-cigarette regulation and rapid growth in their use, smoking began to become renormalised among young people.DesignInterrupted time-series analysis of repeated cross-sectional time-series data.SettingGreat BritainParticipants248 324 young people aged approximately 13 and 15 years, from three national surveys during the years 1998–2015.InterventionUnregulated growth of e-cigarette use (following the year 2010, until 2015).Outcome measuresPrimary outcomes were prevalence of self-reported ever smoking and regular smoking. Secondary outcomes were attitudes towards smoking. Tertiary outcomes were ever use of cannabis and alcohol.ResultsIn final models, no significant change was detected in the pre-existing trend for ever smoking (OR 1.01, CI 0.99 to 1.03). There was a marginally significant slowing in the rate of decline for regular smoking (OR 1.04, CI 1.00 to 1.08), accompanied by a larger slowing in the rate of decline of cannabis use (OR 1.21, CI 1.18 to 1.25) and alcohol use (OR 1.17, CI 1.14 to 1.19). In all models and subgroup analyses for smoking attitudes, an increased rate of decline was observed after 2010 (OR 0.88, CI 0.86 to 0.90). Models were robust to sensitivity analyses.ConclusionsThere was a marginal slowing in the decline in regular smoking during the period following 2010, when e-cigarettes were emerging but relatively unregulated. However, these patterns were not unique to tobacco use and the decline in the acceptability of smoking behaviour among youth accelerated during this time. These analyses provide little evidence that renormalisation of youth smoking was occurring during a period of rapid growth and limited regulation of e-cigarettes from 2011 to 2015.Trial registration numberResearch registry number: researchregistry4336

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