Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from one to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electr...

F ollowing World War II, the quantity and quality of macroeconomic data expanded dramatically. The most important factor was the regular publication of the National Income and Product Accounts, which contained hundreds of consistently defined and measured statistics that summarized overall economic activity. As the data supply expanded, entrepreneurs realized that a market existed for applying ...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/ √ T . This local embedding makes the problem asymptotically a normal-normal Bayes problem, resulting in closed-form solutions for th...

In this paper we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility model. This was performed through a Monte Carlo study with 1000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The criteria used for prediction are the one step ahead...

We show that network advantages constitute an important intangible asset that goes unrecognized in the financial statements. For a sample of e-commerce firms, we find that network advantages created by Web site traffic have substantial explanatory power for stock prices over and above traditional summary accounting measures such as earnings and book value of equity. Also, network advantages are...

This paper examines the predictive power of credit spreads from the corporate bond market. The high-yield bond spread and investment-grade spread can explain 68 per cent and 42 per cent of output variations one year ahead, while the term spread based on government debts can explain only 12 per cent of them. For output forecasts up to one year ahead, the corporate bond spreads also outperform po...

This paper addresses the incremental effects of two anomalous variables: pricescaled one-year-ahead analysts’ early-in-the-year earnings forecasts from Elgers, Lo, and Pfeiffer (2001) and price-scaled implicit firm values from Frankel and Lee (1998), where implicit firm values are defined in a manner consistent with the Ohlson (1995) residual income model. Because analysts’ forecasts are input ...

This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular, the unconditional mean for each series in the vector autoregression is centered around long-horizon survey forecasts. Heavy shrinkage toward the democratic prior is found to give good real-time predictions of a range of...

The value at risk (VaR) measure often relies on an assumption about the return (or price) distribution of the underlying risky assets. Different distributional assumptions may produce widely different computed VaR values. When estimating VaR using intra-daily equity returns, the question arises as to what assumption should be made about the return distribution. Because of the difficulty of deco...