Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (original) (raw)

Forecasting economic time series with unconditional time-varying variance

International Journal of Forecasting, 2004

The classical forecasting theory of stationary time series exploits the second-order structure (variance, autocovariance, and spectral density) of an observed process in order to construct some prediction intervals. However, some economic time series show a time-varying unconditional second-order structure. This article focuses on a simple and meaningful model allowing this nonstationary behaviour. We show that this model satisfactorily explains the nonstationary behaviour of several economic data sets, among which are the U.S. stock returns and exchange rates. The question of how to forecast these processes is addressed and evaluated on the data sets.

Realized variance modeling: decoupling forecasting from estimation

accepted on Journal of Financial Econometrics

This paper evaluates the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and functions delivering estimates (estimation criteria). Our empirical findings highlight that: independently of the econometrician's forecasting loss function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the HAR family, for any of the forecasting loss functions considered; the (2,1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-range dependence as well as does the HAR family.

Bayesian Vars: Specification Choices and Forecast Accuracy

Journal of Applied Econometrics, 2013

Bayesian VARs: Specification Choices and Forecast Accuracy* In this paper we discuss how the forecasting performance of Bayesian VARs is affected by a number of specification choices. In the baseline case, we use a Normal-Inverted Wishart (N-IW) prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of h-step ahead forecasts feasible and simple, in particular when using standard and fixed values for the tightness and the lag length. We then assess the role of the optimal choice of the tightness, of the lag length and of both; compare alternative approaches to h-step ahead forecasting (direct, iterated and pseudo-iterated); discuss the treatment of the error variance and of cross-variable shrinkage; and address a set of additional issues, including the size of the VAR, modeling in levels or growth rates, and the extent of forecast bias induced by shrinkage. We obtain a large set of empirical results, but we can summarize them by saying that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.

Economic forecasting: editors’ introduction

Empirical Economics

The 2-day workshop, organized by Robert M. Kunst and Martin Wagner, drew much more attention than originally expected. This reflects the increased-respectively regained-importance of forecasting not only in practical terms but also as a research topic in the underlying scientific disciplines. Much of this growing interest may be also rooted in increased importance of forecasting in fields such as management science, marketing or supply chain management and may well be driven by methodological developments rooted in several disciplines that could be summarized under labels such as big data, machine learning and the like; the workshop itself had a narrower focus on macroeconomic forecasting. Even with the specific focus on macroeconomics, the papers span a wide portfolio of approaches and applications, ranging from statistical theory to data-driven research. As indicated in the beginning, some of the contributions in this special volume are not related to the workshop, as submission of manuscripts was open.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

2017

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The first reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modified information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of fitted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new est...

A ranking of VAR and structural models in forecasting

This paper ranks economic forecasts performances for two structural models against a benchmark of time series models, VAR and ARIMA, according to a set of statistical measures calculated for the main economic aggregates. The period of analysis covers twenty years for annual data (1985-2004) and 28 quarters for quarterly models (1998:1-2004:4). Furthermore, models are tested to see whether predictions contain additional information more than the one showed by a random walk process (Fair-Shiller, 1987). Results show a net supremacy of VAR models over structural models and have significant contribution to information than the one contained in the random walk process.

GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.

ESSAYS IN FORECASTING ABSTRACT OF THE DISSERTATION Essays in Forecasting

2009

This dissertation comprises three essays in macroeconomic forecasting. The first essay discusses model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. Particular emphasis is placed on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error on the class of test statistics with limiting distributions that are functionals of Gaussian processes. Results of an empirical investigation of the marginal predictive content of money for income are also presented. The second essay outlines a number of approaches to the selection of factor proxies (ob-served variables that proxy unobserved estimated factors) using statistics based on large sample datasets. This approach to factor proxy selection is examined via a small Monte Carlo experiment and a set of prediction experiments, where evidence supporting our proposed methodology is presented. The third essay co...