Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

https://sisis.rz.htw-berlin.de/inh2007/12357004.pdf

Time series analysis

This article investigates the economic effects of conflict, using the terrorist conflict in the Basque Country as a case study. We find that, after the outbreak of terrorism in the late 1960's, per capita GDP in the Basque Country declined about 10 percentage points relative to a synthetic control region without terrorism. In addition, we use the 1998-1999 truce as a natural experiment. We find that stocks of firms with a significant part of their business in the Basque Country showed a positive relative performance when truce became credible, and a negative relative performance at the end of the cease-fire.

https://www.jstor.org/stable/pdfplus/3132164.pdf

The Economic Costs of Conflict: A Case Study of the Basque Country

Preface. Preface to First Edition. 1. Financial Time Series and Their Characteristics. 2. Linear Time Series Analysis and Its Applications. 3. Conditional Heteroscedastic Models. 4. Nonlinear Models and Their Applications. 5. High-Frequency Data Analysis and Market Microstructure. 6. Continuous-Time Models and Their Applications. 7. Extreme Values, Quantile Estimation, and Value at Risk. 8. Multivariate Time Series Analysis and Its Applications. 9. Principal Component Analysis and Factor Models. 10. Multivariate Volatility Models and Their Applications. 11. State-Space Models and Kalman Filter. 12. Markov Chain Monte Carlo Methods with Applications. Index.

http://inis.jinr.ru/sl/M_Mathematics/MV_Probability/MVspf_Stochastics%20in%20finance/Tsay%20Analysis.pdf

Analysis of Financial Time Series

Food intake is regulated by the hypothalamus, including the melanocortin and neuropeptide Y (NPY) systems in the arcuate nucleus. The NPY Y2 receptor (Y2R), a putative inhibitory presynaptic receptor, is highly expressed on NPY neurons in the arcuate nucleus, which is accessible to peripheral hormones. Peptide YY3-36 (PYY3-36), a Y2R agonist, is released from the gastrointestinal tract postprandially in proportion to the calorie content of a meal. Here we show that peripheral injection of PYY3-36 in rats inhibits food intake and reduces weight gain. PYY3-36 also inhibits food intake in mice but not in Y2r-null mice, which suggests that the anorectic effect requires the Y2R. Peripheral administration of PYY3-36 increases c-Fos immunoreactivity in the arcuate nucleus and decreases hypothalamic Npy messenger RNA. Intra-arcuate injection of PYY3-36 inhibits food intake. PYY3-36 also inhibits electrical activity of NPY nerve terminals, thus activating adjacent pro-opiomelanocortin (POMC) neurons. In humans, infusion of normal postprandial concentrations of PYY3-36 significantly decreases appetite and reduces food intake by 33% over 24 h. Thus, postprandial elevation of PYY3-36 may act through the arcuate nucleus Y2R to inhibit feeding in a gut–hypothalamic pathway.

BRIEF COMMUNICATION ARISING: Gut hormone PYY3-36 physiologically inhibits food intake

This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series.

Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

Time series data are often subject to uncontrolled or unexpected interventions, from which various types of outlying observations are produced. Outliers in time series, depending on their nature, may have a moderate to significant impact on the effectiveness of the Standard methodology for time series analysis with respect to model identification, estimation, and forecasting. In this article we use an iterative outlier detection and adjustment procedure to obtain joint estimates of model parameters and outlier effects. Four types of outliers are considered, and the issues of spurious and masking effects are discussed. The major differences between this procedure and those proposed in earlier literature include (a) the types and effects of outliers are obtained based on less contaminated estimates of model parameters, (b) the outlier effects are estimated simultaneously using multiple regression, and (c) the model parameters and the outlier effects are estimated jointly. The sampling behavior of the test s...

Joint Estimation of Model Parameters and Outlier Effects in Time Series

Outliers in time series can be regarded as being generated by dynamic intervention models at unknown time points. Two special cases, innovational outlier (IO) and additive outlier (AO), are studied in this article. The likelihood ratio criteria for testing the existence of outliers of both types, and the criteria for distinguishing between them are derived. An iterative procedure is proposed for detecting IO and AO in practice and for estimating the time series parameters in autoregressive-integrated-moving-average models in the presence of outliers. The powers of the procedure in detecting outliers are investigated by simulation experiments. The performance of the proposed procedure for estimating the autoregressive coefficient of a simple AR(l) model compares favorably with robust estimation procedures proposed in the literature. Two real examples are presented.

Estimation of time series parameters in the presence of outliers

This new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have been completely rewritten. There is new material on a number of topics, including unit roots, ARCH, and cointegration.\"The Econometric Analysis of Time Series \"focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs. It explores the way in which recent advances in time series analysis have affected the development of a theory of dynamic econometrics, sets out an integrated approach to the problems of estimation and testing based on the method of maximum likelihood, and presents a coherent strategy for model selection.A.C. Harvey is Professor of Econometrics at the London School of Economics.

The Econometric Analysis of Time Series.

Time-series data are often contaminated with outliers due to the influence of unusual and non-repetitive events. Forecast accuracy in such situations is reduced due to (1) a carry-over effect of the outlier on the point forecast and (2) a bias in the estimates of model parameters. Hillmer (1984) and Ledolter (1989) studied the effect of additive outliers on forecasts. It was found that forecast intervals are quite sensitive to additive outliers, but that point forecasts are largely unaffected unless the outlier occurs near the forecast origin. In such a situation the carry-over effect of the outlier can be quite substantial. In this study, we investigate the issues of forecasting when outliers occur near or at the forecast origin. We propose a strategy which first estimates the model parameters and outlier effects using the procedure of Chen and Liu (1993) to reduce the bias in the parameter estimates, and then uses a lower critical value to detect outliers near the forecast origin in the forecasting stage. One aspect of this study is on the carry-over effects of outliers on forecasts. Four types of outliers are considered: innovational outlier, additive outlier, temporary change, and level shift. The effects due to a misidentification of an outlier type are examined. The performance of the outlier detection procedure is studied for cases where outliers are near the end of the series. In such cases, we demonstrate that statistical procedures may not be able to effectively determine the outlier types due to insufficient information. Some strategies are recommended to reduce potential difficulties caused by incorrectly detected outlier types. These findings may serve as a justification for forecasting in conjunction with judgment. Two real examples are employed to illustrate the issues discussed.

Forecasting time series with outliers

The main purpose of this article is to assess the performance of autoregressive integrated moving average (ARIMA) models when occasional level shifts occur in the time series under study. A random level-shift time series model that allows the level of the process to change occasionally is introduced. Between two consecutive changes, the process behaves like the usual autoregressive moving average (ARMA) process. In practice, a series generated from a random level-shift ARMA (RLARMA) model may be misspecified as an ARIMA process. The efficiency of this ARIMA approximation with respect to estimation of current level and forecasting is investigated. The results of examining a special case of an RLARMA model indicate that the ARIMA approximations are inadequate for estimating the current level, but they are robust for forecasting future observations except when there is a very low frequency of level shifts or when the series are highly negatively correlated. A level-shift detection procedure is presented to h...

https://www.jstor.org/stable/info/1391755

Chung Chen

Papers

Joint Estimation of Model Parameters and Outlier Effects in Time Series

Estimation of time series parameters in the presence of outliers

The Econometric Analysis of Time Series.

Forecasting time series with outliers

Random Level-Shift Time Series Models, ARIMA Approximations, and Level-Shift Detection