Time series analysis and simultaneous equation econometric models

1970

The editors of the Quarterly Ec:onomic Commentary make use of a variety of econometric models, developed in the past by the Institute, as a guide to forecasting. Such models indicate the implications of different assumptions about the course of the economy, the impact on the economy of extrapolated trends, and the consistency of the forecasts of the National Accounts components made in Section 2.2, both with themselves and with the experience embodied in the models. Following normal practice in the use of working models, it was decided to re-run the equations using the latest figures, and where possible to attempt an improvement in the models. Updating the models not only keeps them relevant to current conditions, but also serves as a check on the stability over time of the implied relationships

Modelling Time Series has developed considerably since Yule first considered the notion of spuriousness. The first error correction model was probably estimated by Denis Sargan in the early 60s and this research path was followed by a number of people at the London School of Economics. The long-run is emphasised in papers by Davidson, Hendry and Mizon. The notion that the long-run can be estimated by a regression follows from the Granger representation theorem that gives rise to the notion that non-stationary series are stationary in combination. Cointegration is a filter that in a similar way to differencing data renders series stationary. Engle and Granger provided a means by which the flter can be derived and embeded in a dynamic model. Johansen provided a mechanism by which multiple cointegrating relations might be derived and tested using a sequence of likelihood ratio tests. When stationarity is tested the distribution is non-standard. One problem that arises in this process r...

2011

Article aims of time series econometric model of macroeconomic variable GDP in the US economy. Because that is a nonstationary time series, there are used several statistical tests in order to turn into a stationary series. After applying these tests, the time series became stationary and integrated of order I; thus, we use Box-Jenkins procedure for the determination of ARMA. We estimate by OLS the parameters of various models. Performances chosen ARIMA model (1,1,1) are verified on the basis of classical statistical tests and forecasting.

1945-A nonlinear time series workshop : a toolkit for detecting and identifying nonlinear serial dependence I by Douglas M.Patterson, Richard A.Ashley. p.cm.--(Dynamic modeling and econometrics in economics and finance; v.2) Includes bibliographical references and index. ISBN 978-1-4613-4665-4 ISBN 978-1-4419-8688-7 (eBook)

Journal of Applied Mathematics and Physics, 6, 2635-2649. https://doi.org/10.4236/jamp.2018.612219, 2018

This article aims to provide an analysis for a time series data of gross domestic product (GDP) of the Sudan. An econometric time series model with macroeconomic variables is conducted. Since a non-stationary time series must be made stationary, some statistical tests are followed so that the time series become stationary series. After applying these tests, the time series became stationary and integrated of order I. Box-Jenkins procedure is used to determine ARMA. OLS is used to estimate the models parameters. Performances chosen ARIMA model are verified on the basis of classical statistical tests and forecasting. The model features are interpreted on the basis of standard measures of forecasting performance.

Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development, there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work. The volume can be used as a textbook for a course on applied time series econometrics. The coverage of topics follows recent methodological developments. Unit root and cointegration analysis play a central part. Other topics include structural vector autoregressions, conditional heteroskedasticity, and nonlinear and nonparametric time series models. A crucial component in empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into the existing software packages. Therefore a flexible Java interface has been created that allows readers to replicate the applications and conduct their own analyses.

2010

Abstract Whilst the existence of a unit root implies that current shocks have permanent effects, in the long run, the simultaneous presence of a deterministic trend obliterates that consequence. As such, the long-run level of macroeconomic series depends upon the existence of a deterministic trend. This paper proposes a formal statistical procedure to distinguish between the null hypothesis of unit root and that of unit root with drift.

SSRN Electronic Journal, 2000

Modelling comovements amongst multiple economic variables takes up a relevant part of the literature in time series econometrics. Comovement can be defined as "move together", that is as movement that several series have in common. The pattern of the series could be of different nature, such as trend, cycles, seasonality, being the results of different driving forces. As a results, series that comove share some common features. Common trends, common cycles, common seasonality are terms that are often found in the literature, different in scope but all aimed at modeling common behavior of the series. However, modeling comovements is not only a statistical matter, since in many cases common features are predicted by economic theory, resulting from the optimizing behavior of economic agents.

2016

Econometric modelling with time series: SpeciÞ cation, Estimation, and Testing is a graduate textbook covering a broad range of topics in time series econometrics. The book is unique and valuable in three aspects. First, the book tries to bridge the gap between the purely theoretical view of time series analysis such as the one offered by Hamilton (1994) and applied approach offered by Enders (2015). Indeed, every chapter in approximately 30 pages provides enough theoretical background and rigorous asymptotic theory which is followed by two empirical examples, usually one taken from economics and one from Þ nance. Second, the focus of the book is on maximum likelihood estimation which makes it stand out among other books on time series analysis. Third, authors offer solutions for all examples and exercises from the book in three different software: GAUSS, MATLAB, and R. Most of empirical examples are also available in RATS. In order to follow the content of the book with ease, mathe...

Springer Proceedings in Business and Economics, 2018

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

International Journal of Forecasting, 2004

Applied Time Series Modelling and Forecasting provides a non-technical approach to applied econometric time series models, which involve non-stationary data. This monograph emphasizes the why and how of econometric time series modeling and places less emphasis on the analytical details. Additionally, it extends the topical coverage of Harris (1995) by discussing the econometric analysis of panel tests for unit tests and co-integration as well as for financial time series data. Additionally, the authors present the latest techniques in structural breaks and season unit root testing, testing co-integration with a structural break, seasonal co-integration in multivariate models, and approaches to structural macroeconomic modeling. Chapter 1 provides an overview (or review for some readers) of the basic analytics of time series analysis, which provides the foundation for the remainder of the monograph. Chapter 2 includes details of short-and long-run relationships of the time series, with the first part of the chapter focusing on the examination of long-run relationships between the economic time series. Moreover, the discussion delves into distinguishing between stationary and non-stationary variables. Neglecting to examine such distinctions could result in a spurious regression, which might imply a statistically significant long-run relationship when no causal relationship exists. In fact, the establishment of a long-run relationship naturally leads to the concept of co-integration, which allows analysts to examine if there is a causal relationship between the economic time series. The final part studies the short-run relationships of economic time series. The discussion stresses that the estimation of short-run models can be problematic and argues that the method of differencing the data is not a good solution since this would remove information about the long-run behavior of the time series. Thus, a remedy would be the error correction model (ECM) since the ECM would contain information about the long-and short-run aspects of the economic time series. Chapter 3 presents the details of the presence of a unit root in the time series, and the discussion begins with the Dickey -Fuller (DF) test for a unit root showing that a t-test of the null hypothesis of nonstationarity is not based on the standard t-distribution. Much of the discussion deals with what elements should be included in the testing procedure. That is, whether the inclusion of the trend and the intercept (i.e., deterministic components) or just one of them would result in different results in the testing procedure. The chapter continues by going into the issues of which of the deterministic components should enter the testing process by examining a sequential testing procedure espoused by Perron. The Dickey -Fuller test is modified to examine a more complicated time series known as the augmented Dickey-Fuller, one that entails the addition of lagged dependent variables to the test equation. The discussion continues with the issues of how many lagged terms should be incorporated into the test and related issues such as the power and size properties of the augmented Dickey -Fuller test. The final part of the chapter discusses the empirical issues of seasonal unit roots, including the integration of structural breaks. Chapters 4 and 5 provide a discussion of the estimation of co-integration of single equation models (Chapter 4) and multiple co-integration models (Chapter 5). The most commonly applied method in testing for co-integration, up to the early 1990s, was the twostep estimation procedure of Engle and Granger (1987). This single-equation method for estimation of co-integration is based on the restrictive assumption of a single co-integration relationship, which is estimated via the OLS procedure. However, with the case of more 0169-2070/$ -see front matter D

Econometrica, 1989

1990

111111111111111111111111111111111111111111111 1149931 FOll 584 (*) We would like to express our gratitude to Agustin Maravall and Juan Jose Dolado for their comments on a draft version of this work and to Juan Carlos Delrieu and M. de los Llanos Matea for carrying out the computations of section five. Banco de Espaiia. Servicio de Estudios Documento de Trabajo n.O 9008 ISBN: B4-n93-059-7 Dep6sito legal: M. 30892 -1990 Imprenta del Banco de Espana -3-

Brazilian Review of Econometrics, 1992

A taxa de desemprego do SEADE/DIEESE, Brasil, e apresentada como uma media ponderada dos ultimos tIes meses. Se Xt representa a serie observada em urn certo intervalo de tempo, a serie publicada Yt e construlda, de forma aproximada, como uma media ponderada das liltimas observacy5es, i.e. , Yt = m.-l m-l 2::: WtiXt_i for t = m-1, m + 1, ... com a restric;ao, 2::: Wti = 1 e Wti ;?: 0 para i =O i =o todo i e t. Este problema e urn caso especial de agreg�a.o com justaposic;ao ou do usc de tiltros de media-m6veis em modelos de series temporais. Este artigo estuda os efeitos da utiliz�ao de filtros de medias-moveis em modelos de series temporais, assumindo que a serie original pode ser caracterizada por urn processo AR IMA. Estuda-se tambem 0 efeito deste tipo de agreg�ao em identific�ao, estim�ao, previsao e nos componentes de tendencia e sazonalidade de modelos de series temporais, Msim como, na identific�a.o de pontos de reversao e em rel�Oes dinamicas entre variaveis.