Cointegration Causality And Forecasting

A collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with and/or studied with both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

One of the major issue for policy makers is handling with continues increase in the level of unemployment in Pakistan. Thus forecasting unemployment rate is imperative to policy makers. This study aims to explore the best forecasting model among ARIMA, ARFIMA and exponential smoothing for forecasting unemployment. Secondly this study analyzed unemployment using time series techniques, measured long & short run relationship with population growth, labor force participation rate and crop production, and also investigated the causality between unemployment and other variables. Time series data ranging from 1965 to 2014 is collected from Pakistan Economic Survey for analysis. This study evaluate the forecasting performance of three models by using the forecast accuracy criterion such mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil's U statistics. Double Exponential Smoothing model is chosen as a best forecasted model for unemployment rate on the basis of forecast accuracy criterion. Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test is used for checking stationarity in the variables. At level the variables were non stationary and become stationary at first difference. The results of Johnson cointegration and Vector Error Correction model (VECM) indicated that there exists long & short run cointegration relationship between unemployment rate and other variables. Granger Causality test shows bi-directional causality running from crop production toward population growth.

Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.

This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.

This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.

We consider the forecasting of cointegrated variables, and we show that at long horizonsquot; nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariatequot; forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. quot; Our results highlight a potentially important deficiency of standard forecast accuracyquot; measures they fail to value the maintenance of cointegrating relationships amongquot; variables and we suggest alternatives that explicitly do so.

This paper applies linear and nonlinear Granger causality tests to examine the dynamic relation between London Metal Exchange (LME) cash prices and three possible predictors. The analysis uses matched quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. We also examine the effects of cointegration on both linear and nonlinear Granger causality tests. When cointegration is not modeled, we find evidence of both linear and nonlinear causality between cash prices and analyzed predictor variables. However, after controlling for cointegration, we no longer find evidence of significant nonlinear causality. Our results contribute to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. We also illustrate the importance of interest rate and inventory as well as futures price in forecasting cash prices. Our failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply.