scholarly journals GDP and life expectancy in Italy and Spain over the long run: A time-series approach

2016 ◽  
Vol 35 ◽  
pp. 813-866 ◽  
Author(s):  
Emanuele Felice ◽  
Josep Pujol Andreu ◽  
Carlo D'Ippoliti
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Long Run ◽  
Time Series Approach

Forecasting Long-Run Coal Price in China: A Shifting Trend Time-Series Approach

2010 ◽  
Vol 14 (3) ◽  
pp. 499-519 ◽  
Author(s):  
Baomin Dong ◽  
Xuefeng Li ◽  
Boqiang Lin
Keyword(s):  
Time Series ◽  
Coal Price ◽  
Long Run ◽  
Time Series Approach

scholarly journals Survival and Years of Good Life in Finland in the very long run

2020 ◽  
Vol 54 ◽  
pp. 1-27
Author(s):  
Claudia Reiter ◽  
Wolfgang Lutz
Keyword(s):  
Quality Of Life ◽  
Time Series ◽  
Life Expectancy ◽  
Good Life ◽  
Critical Levels ◽  
Long Run ◽  
Quality Of Life Indicators ◽  
To Come ◽  
Sullivan Method

In this paper we apply the recently developed wellbeing indicator ‘Years of Good Life’ (YoGL) to Finland, which has the world’s longest annual demographic time series starting in 1722. We combine this with scenarios up to 2100 as developed under the SSP (Shared Socioeconomic Pathways) framework. YoGL is based primarily on the trend in life expectancy but it also considers age-specific proportions of persons above critical levels of quality of life indicators (using the Sullivan method). Since estimating these indicators for historical populations is a major challenge, the paper uses a wide array of sources to come up with a first crude estimation of how quality of life has changed in Finland over the centuries.

scholarly journals Is radiative forcing cointegrated with temperature? A further examination using a structural time series approach

2019 ◽  
Vol 30 (5) ◽  
pp. 1203-1217
Author(s):  
Kelvin Balcombe ◽  
Iain Fraser ◽  
Abhijit Sharma
Keyword(s):  
Time Series ◽  
Radiative Forcing ◽  
Policy Design ◽  
Model Performance ◽  
Content Type ◽  
Structural Time Series ◽  
Long Run ◽  
Time Series Modelling ◽  
Time Series Approach ◽  
Structural Time

Purpose The purpose of this paper is to re-examine the long-run relationship between radiative forcing (including emissions of carbon dioxide, sulphur oxides, methane and solar radiation) and temperatures from a structural time series modelling perspective. The authors assess whether forcing measures are cointegrated with global temperatures using the structural time series approach. Design/methodology/approach A Bayesian approach is used to obtain estimates that represent the uncertainty regarding this relationship. The estimated structural time series model enables alternative model specifications to be consistently compared by evaluating model performance. Findings The results confirm that cointegration between radiative forcing and temperatures is consistent with the data. However, the results find less support for cointegration between forcing and temperature data than found previously. Research limitations/implications Given considerable debate within the literature relating to the “best” way to statistically model this relationship and explain results arising as well as model performance, there is uncertainty regarding our understanding of this relationship and resulting policy design and implementation. There is a need for further modelling and use of more data. Practical implications There is divergence of views as to how best to statistically capture, explain and model this relationship. Researchers should avoid being too strident in their claims about model performance and better appreciate the role of uncertainty. Originality/value The results of this study make a contribution to the literature by employing a theoretically motivated framework in which a number of plausible alternatives are considered in detail, as opposed to simply employing a standard cointegration framework.

scholarly journals Correction to: Measuring the impact of the Afordable Care Act Medicaid expansion on access to primary care using an interrupted time series approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elizabeth A. Brown ◽  
Brandi M. White ◽  
Walter J. Jones ◽  
Mulugeta Gebregziabher ◽  
Kit N. Simpson
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Interrupted Time Series ◽  
Medicaid Expansion ◽  
Time Series Approach ◽  
Access To Primary Care ◽  
The Impact

An amendment to this paper has been published and can be accessed via the original article.

THE IMPACT OF THE HOTEL ROOM TAX: AN INTERRUPTED TIME SERIES APPROACH

1992 ◽  
Vol 45 (4) ◽  
pp. 433-441 ◽  
Author(s):  
CARL BONHAM ◽  
EDWIN FUJII ◽  
ERIC IM ◽  
JAMES MAK
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Hotel Room ◽  
Time Series Approach ◽  
The Impact

scholarly journals Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 51
Author(s):  
Anthony Medford
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Extreme Value Theory ◽  
Autoregressive Model ◽  
Best Practice ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Model Diagnostics ◽  
Temporal Dependence ◽  
Simulation Results

Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best practice life expectancy. This class of model represents a neat and coherent framework for modeling time series extremes. The Gumbel distribution accounts for the extreme nature of best practice life expectancy, while the AR structure accounts for the temporal dependence in the time series. Model diagnostics and simulation results indicate that these models present a viable alternative to Gaussian AR(1) models when dealing with time series of extremes and merit further exploration.

scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

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