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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation | SpringerLink
"This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies.  Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature.
"Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action."
to:NB  books:noted  downloaded  time_series  statistics  social_science_methodology  to_teach:data_over_space_and_time 
5 weeks ago by cshalizi
Empirical Model Building | Wiley Series in Probability and Statistics
"Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists.
"The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition now features further coverage of momentum based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized and newly added and updated exercises allow readers to test their comprehension of the presented material."
to:NB  books:noted  modeling  simulation  time_series  statistics  downloaded  to_be_shot_after_a_fair_trial 
6 weeks ago by cshalizi
Extremes and Recurrence in Dynamical Systems | Wiley Online Books
"Written by a team of international experts, Extremes and Recurrence in Dynamical Systems presents a unique point of view on the mathematical theory of extremes and on its applications in the natural and social sciences. Featuring an interdisciplinary approach to new concepts in pure and applied mathematical research, the book skillfully combines the areas of statistical mechanics, probability theory, measure theory, dynamical systems, statistical inference, geophysics, and software application. Emphasizing the statistical mechanical point of view, the book introduces robust theoretical embedding for the application of extreme value theory in dynamical systems. Extremes and Recurrence in Dynamical Systems also features:
"• A careful examination of how a dynamical system can serve as a generator of stochastic processes
"• Discussions on the applications of statistical inference in the theoretical and heuristic use of extremes
"• Several examples of analysis of extremes in a physical and geophysical context
"• A final summary of the main results presented along with a guide to future research projects
"• An appendix with software in Matlab® programming language to help readers to develop further understanding of the presented concepts
"Extremes and Recurrence in Dynamical Systems is ideal for academics and practitioners in pure and applied mathematics, probability theory, statistics, chaos, theoretical and applied dynamical systems, statistical mechanics, geophysical fluid dynamics, geosciences and complexity science."
to:NB  books:noted  extreme_values  dynamical_systems  time_series  statistical_inference_for_stochastic_processes  statistics  downloaded  recurrence_times 
6 weeks ago by cshalizi
UCR Matrix Profile Page
"The Matrix Profile (and the algorithms to compute it: ...), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability."
site  software  algorithm  time_series 
6 weeks ago by schahn
A Composite Likelihood Framework for Analyzing Singular DSGE Models | The Review of Economics and Statistics | MIT Press Journals
"This paper builds on the composite likelihood concept of Lindsay (1988) to develop a framework for parameter identification, estimation, inference, and forecasting in dynamic stochastic general equilibrium (DSGE) models allowing for stochastic singularity. The framework consists of four components. First, it provides a necessary and sufficient condition for parameter identification, where the identifying information is provided by the first- and second-order properties of nonsingular submodels. Second, it provides a procedure based on Markov Chain Monte Carlo for parameter estimation. Third, it delivers confidence sets for structural parameters and impulse responses that allow for model misspecification. Fourth, it generates forecasts for all the observed endogenous variables, irrespective of the number of shocks in the model. The framework encompasses the conventional likelihood analysis as a special case when the model is nonsingular. It enables the researcher to start with a basic model and then gradually incorporate more shocks and other features, meanwhile confronting all the models with the data to assess their implications. The methodology is illustrated using both small- and medium-scale DSGE models. These models have numbers of shocks ranging between 1 and 7."
to:NB  state-space_models  economics  time_series  macroeconomics  statistics  likelihood  re:your_favorite_dsge_sucks 
6 weeks ago by cshalizi
Detection and Analysis of Spikes in a Random Sequence | SpringerLink
"Motivated by the more frequent natural and anthropogenic hazards, we revisit the problem of assessing whether an apparent temporal clustering in a sequence of randomly occurring events is a genuine surprise and should call for an examination. We study the problem in both discrete and continuous time formulation. In the discrete formulation, the problem reduces to deriving the probability that p independent people all have birthdays within d days of each other. We provide an analytical expression for a warning limit such that if a subset of p people among n are observed to have birthdays within d days of each other and d is smaller than our warning limit, then it should be treated as a surprising cluster. In the continuous time framework, three different sets of results are given. First, we provide an asymptotic analysis of the problem by embedding it into an extreme value problem for high order spacings of iid samples from the U[0, 1] density. Second, a novel analytical nonasymptotic bound is derived by using certain tools of empirical process theory. Finally, the required probability is approximated by using various bounds and asymptotic results on the supremum of the scanning process of a one dimensional stationary Poisson process. We apply the theories to climate change related datasets, datasets on temperatures, and mass shooting records in the United States. These real data applications of our theoretical methods lead to supporting evidence for climate change and recent spikes in gun violence."

--- Let's just say that I am really curious to see exactly what they have to assume about (e.g.,) mass shootings to reduce it to a birthday problem.
to:NB  stochastic_processes  probability  statistics  time_series  to_teach:data_over_space_and_time  to_be_shot_after_a_fair_trial 
november 2018 by cshalizi

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