The formulation of the National Centers for Environmental Prediction four-dimensional variational dataassimilation (4D-Var) system is described. Results of applying 4D-Var over a one-week assimilation period, with a full set of physical parametrizations, are presented and compared with those of 3D-Var. The linearization has been performed without simplifications and, therefore, the tangent-line...

‘‘Iterated’’ multiperiod-ahead time series forecasts are made using a one-period ahead model, iterated forward for the desired number of periods, whereas ‘‘direct’’ forecasts are made using a horizon-specific estimated model, where the dependent variable is the multiperiod ahead value being forecasted. Which approach is better is an empirical matter: in theory, iterated forecasts are more effic...

We find that covariance matrix forecasts for an international interest rate portfolio generated by a model that incorporates interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption. Simple foreca...

I n this paper, we specify that the GARCH(1,1) model has strong forecasting volatility and its usage under the truncated standard normal distribution (TSND) is more suitable than when it is under the normal and student-t distributions. On the contrary, no comparison was tried between the forecasting performance of volatility of the daily return series using the multi-step ahead forec...

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange...

In this paper we propose a strategy for forecasting the term structure of interest rates which may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Affine Term Structure Models (ATSM) on a vector autoregression (VAR) as prior information rather than imposing them dogmatically. This allows to account for possible model misspecification. We appl...

Weather forecasting is crucial to both the demand and supply sides of electricity markets. Temperature has a great effect on energy demand. Moreover, solar and wind are very promising renewable energy sources. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. We estimate the VAR model with 16 y...

We extend research on fundamental accounting signals (signals) to a quarterly context and investigate the relative efficiency of analysts and whisperers in using signals in generating onequarter-ahead EPS forecasts. We find (1) a subset of signals found to be relevant in predicting one-year-ahead EPS changes are also relevant in predicting one-quarter-ahead EPS changes, (2) neither analysts nor...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on or with methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for ...

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve for...