Timo Teräsvirta

Bio: Timo Teräsvirta is an academic researcher from Aarhus University. The author has contributed to research in topics: Autoregressive model & Autoregressive conditional heteroskedasticity. The author has an hindex of 62, co-authored 224 publications receiving 20403 citations. Previous affiliations of Timo Teräsvirta include Queensland University of Technology & The RiverBank.

Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

Abstract: 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.

Testing linearity against smooth transition autoregressive models

Abstract: SUMMARY We study a general univariate smooth transition autoregressive, STAR, model. It contains as a special case the self-exciting threshold autoregressive, SETAR, model. We present three tests for testing linearity against STAR models and discuss their properties. The power of the tests in small samples is investigated by simulation when the alternative is the logistic STAR model. One of the tests is identical to Tsay's (1986) test statistic and is recommended only in a special case. Of the two remaining tests with wider applicability, one seems superior to the other in small samples. It is also more powerful than the CUSUM test recently proposed for testing linearity against SETAR models.

Modelling nonlinear economic relationships

Abstract: Basic concepts general models and tools for analysis nonlinear models in economic theory particular nonlinear multivariate models long memory models linearity testing building nonlinear models forecasting, aggression and non-symmetry applications strategies for nonlinear modelling.

Smooth transition autoregressive models — a survey of recent developments

Abstract: This paper surveys recent developments related to the smooth transition autoregressive (STAR) time series model and several of its variants. We put emphasis on new methods for testing for STAR nonlinearity, model evaluation, and forecasting. Several useful extensions of the basic STAR model, which concern multiple regimes, time-varying non-linear properties, and models for vector time series, are also reviewed.

Time Series Analysis

Abstract: Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, such as GNP and its components, price indices, sales, and stock returns are observed over time. In addition to being interested in the contemporaneous relationships among such variables, we are often concerned with relationships between their current and past values, that is, relationships over time.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions.

Abstract: During the last fifteen years, Akaike's entropy-based Information Criterion (AIC) has had a fundamental impact in statistical model evaluation problems. This paper studies the general theory of the AIC procedure and provides its analytical extensions in two ways without violating Akaike's main principles. These extensions make AIC asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the “true” models. These selection criteria are called CAIC and CAICF. Asymptotic properties of AIC and its extensions are investigated, and empirical performances of these criteria are studied in choosing the correct degree of a polynomial model in two different Monte Carlo experiments under different conditions.