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Essays On Empirical Time Series Modeling With Causality And Structural Change
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Book Synopsis Essays on Empirical Time Series Modeling with Causality and Structural Change by : Jin Woong Kim
Download or read book Essays on Empirical Time Series Modeling with Causality and Structural Change written by Jin Woong Kim and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, three related issues of building empirical time series models for financial markets are investigated with respect to contemporaneous causality, dynamics, and structural change. In the first essay, nation-wide industry information transmission among stock returns of ten sectors in the U.S. economy is examined through the Directed Acyclical Graph (DAG) for contemporaneous causality and Bernanke decomposition for dynamics. The evidence shows that the information technology sector is the most root cause sector. Test results show that DAG from ex ante forecast innovations is consistent with the DAG from ex post fit innovations. This supports innovation accounting based on DAGs using ex post innovations. In the second essay, the contemporaneous/dynamic behaviors of real estate and stock returns are investigated. Selected macroeconomic variables are included in the model to explain recent movements of both returns. During 1971-2004, there was a single structural break in October 1980. A distinct difference in contemporaneous causal structure before and after the break is found. DAG results show that REITs take the role of a causal parent after the break. Innovation accounting shows significantly positive responses of real estate returns due to an initial shock in default risk but insignificant responses of stock returns. Also, a shock in short run interest rates affects real estate returns negatively with significance but does not affect stock returns. In the third essay, a structural change in the volatility of five Asian and U.S. stockmarkets is examined during the post-liberalization period (1990-2005) in the Asian financial markets, using the Sup LM test. Four Asian financial markets (Hong Kong,Japan, Korea, and Singapore) experienced structural changes. However, test results do not support the existence of structural change in volatility for Thailand and U.S. Also, results show that the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) persistent coefficient increases, but the Autoregressive Conditional heteroskedasticity (ARCH) impact coefficient, implying short run adjustment, decreases in Asian markets. In conclusion, when the econometric model is set up, it is necessary to consider contemporaneous causality and possible structural breaks (changes). The dissertation emphasizes causal inference and structural consistency in econometric modeling. It highlights their importance in discovering contemporaneous/dynamic causal relationships among variables. These characteristics will likely be helpful in generating accurate forecasts.
Author :Judea Pearl Publisher :Createspace Independent Publishing Platform ISBN 13 :9781507894293 Total Pages :0 pages Book Rating :4.8/5 (942 download)
Book Synopsis An Introduction to Causal Inference by : Judea Pearl
Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Download or read book The Book of Why written by Judea Pearl and published by Basic Books. This book was released on 2018-05-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Book Synopsis Research Papers in Statistical Inference for Time Series and Related Models by : Yan Liu
Download or read book Research Papers in Statistical Inference for Time Series and Related Models written by Yan Liu and published by Springer Nature. This book was released on 2023-05-31 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Book Synopsis Essays on Time Series and Causality Analysis in Financial Markets by : Tatevik Zohrabyan
Download or read book Essays on Time Series and Causality Analysis in Financial Markets written by Tatevik Zohrabyan and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial market and its various components are currently in turmoil. Many large corporations are devising new ways to overcome the current market instability. Consequently, any study fostering the understanding of financial markets and the dependencies of various market components would greatly benefit both the practitioners and academicians. To understand different parts of the financial market, this dissertation employs time series methods to model causality and structure and degree of dependence. The relationship of housing market prices for nine U.S. census divisions is studied in the first essay. The results show that housing market is very interrelated. The New England and West North Central census divisions strongly lead house prices of the rest of the country. Further evidence suggests that house prices of most census divisions are mainly influenced by house price changes of other regions. The interdependence of oil prices and stock market indices across countries is examined in the second essay. The general dependence structure and degree is estimated using copula functions. The findings show weak dependence between stock market indices and oil prices for most countries except for the large oil producing nations which show high dependence. The dependence structure for most oil consuming (producing) countries is asymmetric implying that stock market index and oil price returns tend to move together more during the market downturn (upturn) than a market boom (downturn). In the third essay, the relationship among stock returns of ten U.S. sectors is studied. Copula models are used to explore the non-linear, general association among the series. The evidence shows that sectors are strongly related to each other. Energy sector is relatively weakly connected with the other sectors. The strongest dependence is between the Industrials and Consumer Discretionary sectors. The high dependence suggests small (if any) gains from industry diversification in U.S. In conclusion, the correct formulation of relationships among variables of interest is crucial. This is one of the fundamental issues in portfolio analysis. Hence, a thorough examination of time series models that are used to understand interactions of financial markets can be helpful for devising more accurate investment strategies.
Book Synopsis Exploratory Causal Analysis with Time Series Data by : James M. McCracken
Download or read book Exploratory Causal Analysis with Time Series Data written by James M. McCracken and published by Springer Nature. This book was released on 2022-06-01 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Book Synopsis Essays on Testing for Nonstationarities and Structural Change in Time Series Models by : Timothy J. Vogelsang
Download or read book Essays on Testing for Nonstationarities and Structural Change in Time Series Models written by Timothy J. Vogelsang and published by . This book was released on 1993 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causality written by Carlo Berzuini and published by John Wiley & Sons. This book was released on 2012-06-04 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays in Econometrics by : Clive W. J. Granger
Download or read book Essays in Econometrics written by Clive W. J. Granger and published by Cambridge University Press. This book was released on 2001-07-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: These are econometrician Clive W. J. Granger's major essays in causality, integration, cointegration, and long memory.
Book Synopsis Essays on Time Series Analysis by : Yanlin Shi
Download or read book Essays on Time Series Analysis written by Yanlin Shi and published by . This book was released on 2014 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is a collection of essays on modelling volatility with time series techniques. The first essay addresses the question of modelling structural breaks in the Fractionally Integrated Generalised Autoregressive Conditional Heteroskedasticity (FIGARCH) model. By detecting structural change points via the Markov Regime-Switching (MRS) framework, a two-stage Three-State FIGARCH (3S-FIGARCH) model is proposed. Compared with various existing FIGARCH family models, our empirical results suggest that the 3S-FIGARCH model is preferred in all cases and can potentially provide a more reliable estimate of the long-memory parameter. The second essay examines the confusion between long memory and regime switching in volatility via a set of Monte Carlo simulations. A theoretical proof is provided to show that this confusion is caused by the effects of the smoothing probability from the data-generating process (DGP) of the MRS-GARCH model. To control for these effects, the MRS-FIGARCH model is proposed. By conducting a set of Monte Carlo simulations, we show that the MRS-FIGARCH model can effectively distinguish between the pure FIGARCH and pure MRS-GARCH DGPs. Further, an empirical application suggests that the MRS-FIGARCH can be a widely useful tool for volatility modelling. The third essay empirically studies the relation between public information arrivals and intraday stock return volatility. Motivated by the Mixture of Distribution Hypothesis (MDH) and the study of Veronesi (1999), we fit hourly Standard & Poor's (S&P) 100 stock return data with the MRS-GARCH model to investigate the effect of the quantity and quality of news on stock return volatility in the calm (low volatility) and turbulent (high volatility) states. The effect of news on the persistence and magnitude of volatility depends on the quality of news and the state of stock return volatility. In addition, this effect varies across sectors and firm sizes. The fourth essay analyses the effects of news on the so-called 'idiosyncratic volatility puzzle'. By empirically modelling the stock return data from the Center for Research in Security Prices (CRSP) database from 2000 to 2011, we demonstrate that both the quantity and quality of news can significantly explain the effect of idiosyncratic volatility on excess returns. Specifically, when news effects are appropriately controlled, the average magnitude of this effect can be reduced by roughly 50 per cent.
Book Synopsis Essays in Honor of Cheng Hsiao by : Dek Terrell
Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Book Synopsis Essays in Time Series Econometrics by : Neslihan Sakarya
Download or read book Essays in Time Series Econometrics written by Neslihan Sakarya and published by . This book was released on 2017 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of four research papers. Three of these papers are based on the analysis the Hodrick-Prescott (HP) filter which is a commonly used technique to extract the trend from a time series in macroeconomics, while the last paper introduces a monitoring procedure to detect a change from spurious regression to cointegration. In the first paper, we derive a new representation of the transformation of the data that is implied by the HP filter. This representation allows us to carry out a rigorous analysis of the properties of the HP filter without using the ARMA based approximation that has been used in the previous literature. In the second paper, we introduce a new property of the HP filter that has not been discovered before. When the trend is extracted from the original time series, the remaining series is called the cyclical component. The new property suggests that the cyclical component is approximately the trend in the fourth difference of the original series. We formalize this approximation by correcting it for the begin and end points of the sample. This property allows us to analyze the properties of the cyclical component when the original series has a linear trend break or is integrated of order up to 4. The third paper approaches the HP filter from frequency domain approach, unlike the first two papers. Since the results of the previous literature are based on the spectral properties of a procedure that is only an approximation to the HP filter, in this paper, we formalize the conjectures that are provided in the literature. The last paper introduces a monitoring procedure to detect a structural break that changes the relation between two integrated time series. It is well-known that two integrated series are highly correlated, while the causality between these series is not obvious. Cointegration is a term that describes the existence of causality between two integrated series. The null hypothesis of the monitoring procedure is that the regression is spurious throughout the sample, whereas under the alternative hypothesis, there is a change from spurious regression to cointegration at an unknown breakpoint. We derive the limiting distribution of the detector under both the null and (fixed and local) alternative hypotheses. The monitoring procedure is consistent, as long as the structural break is bounded away from the end of the sample. Simulation results suggest that the procedure has excellent size properties, as well as good power properties. Finally, the monitoring procedure is applied to the relationship between US nominal wages and prices; a relation found not to exhibit cointegration in the seminal Engle and Granger (1987) paper. Our monitoring procedure indicates a change towards cointegration around the beginning of the new millennium.
Book Synopsis Techniques of Event History Modeling by : Hans-Peter Blossfeld
Download or read book Techniques of Event History Modeling written by Hans-Peter Blossfeld and published by Lawrence Erlbaum Assoc Incorporated. This book was released on 1995 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a companion and update to Event History Analysis and substantially extends the practical application of event history analysis. It also adds several important new models and concepts which have been developed in an extremely active research area since the late 1980s. It provides a comprehensive introductory account of event history modeling techniques and their usefulness for causal analysis in the social sciences. By giving many concrete application examples, it demonstrates that event history models allow a natural time-related representation of causal arguments in empirical studies. In contrast to structural equation analysis, which is based upon the observation of states and on "time-less" models, event history analysis employs the time-path of changes in states and relates changes in (qualitative and metric) causal variables in the past to changes in discrete outcomes in the future. Since effects follow their causes in time, this necessarily implies a temporal interval which may be very short or very long, but can never be zero or infinity. This book demonstrates that event history modeling is a major step forward in causal analysis because it is the most appropriate of all currently available methodologies to uncover such (normally unknown) lags between causes and their effects and reveals the various temporal shapes of the unfolding effect. This is because the transition rate can be used to represent the quantity of the causal effect at any point in time. A particular strength of this book lies in the description of a new approach to interdependent dynamical systems. It is shown that a causal approach to interdependent systems is easily possible with the help of the transition rate concept, and that the systems view is not a substitute for a proper causal approach in the social sciences. This book also proposes that the social sciences should give up their traditional deterministic approach in empirical analyses in favor of a probabilistic one. It is argued that randomness should not only be seen as a technical term (arising because of limited empirical observation), but must be understood as a theoretical category; it is the propensity of social agents to change their behavior in the future under certain conditions that have taken place in the past and present. This means that the aim of statistical (and substantive) models must be to capture common elements in the behavior of people, or patterns of action that recur in many cases. In event history models, the causal effect to be explained is therefore the probability of a time-related change. Finally, the book is critical with regard to the widely applied models with unobserved heterogeneity since there is, in general, no way to make reliable assumptions about what has not been observed. Thus, in using such models, most empirical researchers try to draw sharp conclusions, even when these can only be generated by imposing much stronger assumptions than can be plausibly defended. This book introduces the reader to the computer program TDA (Transition Data Analysis). Designed by Götz Rohwer, TDA estimates the kinds of models most frequently used with longitudinal data, in particular, event history data. The guiding principle in constructing TDA was the desire to make a broad range of event history analysis techniques as simple and convenient to apply as possible. TDA is now widely used in many research and university centers which analyze longitudinal data in Europe and the USA. It can be run on DOS-based personal computers and UNIX workstations. Included with this book is a disk with an executable version of the TDA program package for DOS-based machines, a file with the data used in the examples throughout the book, and a series of files containing the TDA set-ups for the examples. Thus, the reader is offered the unique opportunity to easily run and modify all the application examples on the computer. The authors have emphasize
Book Synopsis Three Essays on Price Dynamics and Causations Among Energy Markets and Macroeconomic Information by : Sung Wook Hong
Download or read book Three Essays on Price Dynamics and Causations Among Energy Markets and Macroeconomic Information written by Sung Wook Hong and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148354
Book Synopsis Essays on Structural Change in Economic Time Series by : Robert Sollis
Download or read book Essays on Structural Change in Economic Time Series written by Robert Sollis and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Elements of Causal Inference by : Jonas Peters
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
