Abeysinghe (Economic Letters, 1991, 36, 175-197; Journal of Econometrics, 1994, forthcoming) showed that seasonal dummies in regressions may lead to spurious inference. This paper evaluates the post-sample forecasting performance of a seasonal-dummy ARIMA model with four other models. The results, in general, do not stand in favor of the seasonal-dummy approach. JEL classification: C53

In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework. These models encapsulate exponential smoothing methods in a multivariate setting. The models considered are the local level, local trend and damped trend VISTS models with an additive multivariate seasonal component. W...

We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model. The two non-linear models perform approximately on par and outperform the linear autoregressive model on short forecast horizons of one and two quarters. Furthermore, the MS-AR model is the best performer on longer horizons of thr...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...

This paper brings together two important but hitherto largely unrelated areas of the forecasting literature, density forecasting and forecast combination. It proposes a simple data-driven approach to direct combination of density forecasts using optimal weights. These optimal weights are those weights that minimise the ‘distance’, as measured by the Kullback-Leibler Information Criterion, betwe...

inflation forecasting has been one of the requirements for the implementation of monetary policy in countries which their monetary authorities are pursuing inflation targeting regime. however, owing to central bank independence in one hand and as well as the lagged effects of monetary policies on inflation in the other, the monetary authorities should have the sound perspective about the future...

Forecasting Economic Aggregates by Disaggregates* We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate. We show theoretically that...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming stat...

Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimensio...

forecasting of macroeconomic variables has specific importance in economic topics. indeed, different models are invented to forecast variables to help economic policy makers in adopting appropriate monetary and fiscal policies. in this paper, the performance of integrated model of input-output (io) and neural network is investigated in forecasting final demand and total production and the resul...