Author: Mark Joseph DwyerPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 172
Book Description
Essays in Nonlinear, Nonstationary Time Series Econometrics
Author: Mark Joseph DwyerPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 172
Book Description
Essays in Nonlinear Time Series Econometrics
Author: Niels HaldrupPublisher: OUP Oxford
ISBN: 0191669547
Category : Business & Economics
Languages : en
Pages : 393
Book Description
This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.
Essays in Nonlinear Time Series Analysis
Author: Jonathan R. MichelPublisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 128
Book Description
This dissertation consists of six papers. Each of these papers are on a different aspect of statistical analysis of nonlinear time series. In the first paper, we study the behavior of a nonstationary time series which has different behavior for "high" and "low" levels. This consists of the introduction of a new nonlinear time series model, a mathematical analysis of the functional limit theorem for this model, a statistical test for behavior similar to this new model, and a proposed technique for robust cointegration in the presence of this new model. The second paper consists of an extension of this idea into volatility modeling. The third paper considers experimental design and sampling of Markov chains. In particular, it focuses on how to feasibly optimally sample a continuous two-state Markov chain. The fourth paper is on integer valued time series. The focus here is on studying the properties of the INGARCH(1,1) model in the nonstationary case. This consists of applying mathematical machinery rarely used in econometrics. Additionally, in this paper extensions towards stationarity tests are considered. The fifth paper studies the dynamic Tobit, a time series model often used when data is censored below. In this paper, weak dependence and mixing properties are shown to hold, which is relevant for studying the statistical properties of estimation for this model. The sixth paper studies the reciprocal of the random walk. This is relevant in time series econometrics as such a process is a possible model for time series with a stochastic diminishing trend.
Non-linear and Non-stationary Time Series Analysis
Author: Maurice Bertram PriestleyPublisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 258
Book Description
Three Essays on Nonlinear Nonstationary Econometrics and Applied Macroeconomics
Author: Youngsoo BaeEssays in Time Series Econometrics
Author: Fei HanPublisher:
ISBN:
Category :
Languages : en
Pages : 296
Book Description
This dissertation consists of three chapters dealing with different topics in time series econometrics including generalized method of moments (GMM) estimation and vector autoregressions (VAR). These econometric models have revolutionized empirical research in macroeconomics. Previous work by Hansen and Singleton (1982) showed that the GMM method can be applied to estimate nonlinear rational expectations models in a simple way that the models need not even be solved. The seminal work of Sims (1980) has demonstrated how VAR models can be used for macroeconomic forecasting and policy analysis. The objective of this dissertation is to provide some new econometric tools for applied research in macroeconomics using time series data. The first chapter develops an asymptotic theory for the GMM estimator in nonlinear econometric models with integrated regressors and instruments. We establish consistency and derive the limiting distribution of the GMM estimator for asymptotically homogeneous regression functions. The estimator is consistent under fairly general conditions, and the convergence rates are determined by the degree of the asymptotic homogeneity of regression functions. Similar to linear regressions, we find that the limiting distribution is generally biased and non-Gaussian, and that instruments themselves cannot eliminate the bias even when they are strictly exogenous. Therefore, GMM yields inefficient estimates and invalid $t$- and chi-square test statistics in general. By implementing the fully modified method developed by Phillips and Hansen (1990), we obtain an efficient GMM estimator which has an unbiased and mixed normal limiting distribution. In the second chapter, we develop a novel shock identification strategy in the context of two-country/block structural vector autoregressive (SVAR) models to identify the transmission of credit shocks. Specifically, we investigate how credit shocks originating in the U.S. or euro area affect domestic economic activity in emerging Asia. Shocks within each block are identified using sign restrictions, whereas shocks across the two blocks are identified using a recursive structure (block Cholesky decomposition). This strategy not only enables us to distinguish the external credit shock from the other structural shocks, but also captures the responses of the domestic country. The main findings include that the transmission of credit shocks across countries through the channel of credit contagion is fast and protracted. The adverse effects of external credit tightening are mitigated by domestic credit policy easing in China, but lead to significant decreases in credit and GDP growth in the other emerging Asian countries. We also find that the external credit shocks play a non-negligible role in driving economic fluctuations in emerging Asia, although the role is smaller in China. In the last chapter, we use a global vector autoregressive (GVAR) model to forecast the principal macroeconomic indicators of the original five ASEAN member countries (i.e. Indonesia, Malaysia, Philippines, Singapore, and Thailand). The GVAR model is a compact model of the world economy designed to explicitly model the economic and financial interdependencies at national and international levels. Our GVAR model covers twenty countries which are grouped into nine countries/regions. After applying vector error correction model (VECM) to estimate parameters in the GVAR, we generate twelve one-quarter-ahead forecasts of real GDP growth, inflation, short-term interest rates, real exchange rates, real equity prices, and world commodity prices over the period 2009Q1-2011Q4, with four out-of-sample forecasts during 2009Q1-2009Q4. Forecast evaluation based on the panel Diebold-Mariano (DM) tests shows that the forecasts of our GVAR model tend to outperform those of country-specific VAR models, especially for short-term interest rates and real equity prices. These results suggest that the interdependencies among countries in the global financial market play an important role in macroeconomic forecasting.
Three Essays on Nonlinear Time Series Econometrics
Author: Zhengfeng GuoPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 86
Book Description
Essays on Nonlinear Transformations of Nonstationary Time Series
Author: Chien-Ho WangPublisher:
ISBN:
Category : Economics
Languages : en
Pages : 204
Book Description
Three Essays on Nonlinear Time-series Econometrics
Author: Novella MaugeriModelling Nonlinear Economic Time Series
Author: Timo TeräsvirtaPublisher: OUP Oxford
ISBN: 9780199587148
Category : Business & Economics
Languages : en
Pages : 592
Book Description
This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models in practice. For thispurpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried outusing numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones.Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.