Forecasting real housing price growth in the Eighth District states

The authors consider forecasting real housing price growth for the individual states of the Federal Reserve's Eighth District. They first analyze the forecasting ability of a large number of potential predictors of state real housing price growth using an autoregressive distributed lag (ARDL) model framework. A number of variables, including the state housing price-to-income ratio, state unemployment rate, and national inflation rate, appear to provide information that is useful for forecasting real housing price growth in many Eighth District states. Given that it is typically difficult to determine a priori the particular variable or small set of variables that are the most relevant for forecasting real housing price growth for a given state and time period, the authors also consider various methods for combining the individual ARDL model forecasts. They find that combination forecasts are quite helpful in generating accurate forecasts of real housing price growth in the individual Eighth District states.Housing - Prices ; Federal Reserve District, 8th

Local Polynomial Kernel Forecasts and Management of Price Risks using Futures Markets

This study contributes to understanding price risk management through hedging strategies in a forecasting context. A relatively new forecasting method, nonparametric local polynomial kernel (LPK), is used and applied to the hog sector. The selective multiproduct hedge based on the LPK price and hedge ratio forecasts is, in general, found to be better than continuous hedge and alternative forecasting procedures in terms of reduction of variance of unhedged return. The findings indicate that combining hedging with forecasts, especially when using the LPK technique, can potentially improve price risk management.Marketing,

Forecasting Housing Prices under Different Submarket Assumptions

This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategies are more accurate and efficient than models that are spatially aggregated, or with submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction in an urban setting and surrounding areas in that forecasting models based on expert knowledge of market structure or public school quality and simple model combining techniques may outperform the models using more sophisticated statistical techniques.Clustering, Forecasting, Hedonic price, Housing Submarket, Demand and Price Analysis, C53, R21,

Forecasting Weekly Electricity Prices at Nord Pool

This paper analyses the forecasting power of weekly futures prices at Nord Pool. The forecasting power of futures prices is compared to an ARIMAX model of the spot price. The time series model contains lagged external variables such as: temperature, precipitation, reservoir levels and the basis (futures price less the spot price); and generally reflects the typical seasonal patterns in weekly spot prices. Results show that the time series model forecasts significantly beat futures prices when using the Diebold and Mariano (1995) test. Furthermore, the average forecasting error of futures prices reveals that they are significantly above the settlement spot price at the ‘delivery week’ and their size increases as the time to maturity increases. Those agents taking positions in weekly futures contracts at Nord Pool might find the estimated ARIMAX model useful for improving their expectation formation process for the underlying spot price.Electricity Markets, Power Derivatives and Forecasting Electricity Prices

Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis

Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date. This approach is based on the premise that demand for crude oil derives from the demand for refined products such as gasoline or heating oil. Oil industry analysts such as Philip Verleger and financial analysts widely believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. Our objective is to evaluate this proposition. We derive from first principles a number of alternative forecasting model specifications involving product spreads and compare these models to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot spreads that allows the marginal product market to change over time. We document MSPE reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons

Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts

This study uses quantile regressions to estimate historical forecast error distributions for WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence limits for the forecasts based on the empirical distributions. Quantile regressions with fit errors expressed as a function of forecast lead time are consistent with theoretical forecast variance expressions while avoiding assumptions of normality and optimality. Based on out-of-sample accuracy tests over 1995/96–2006/07, quantile regression methods produced intervals consistent with the target confidence level. Overall, this study demonstrates that empirical approaches may be used to construct accurate confidence intervals for WASDE corn, soybean, and wheat price forecasts.commodity, evaluating forecasts, government forecasting, judgmental forecasting, prediction intervals, price forecasting, Crop Production/Industries, Demand and Price Analysis,

Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models

Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA modelsComment: IEEE PES 2017 General Meeting, Chicago, I

Exchange Rates Predictability in Developing Countries

The main objective of this study is to re-investigates the exchange rates predictability puzzle using monetary model. It is hypothesised that the performance of exchange rate predictability is better off in countries with monetary instability. We employ bootstrap technique as proposed by Kilian (1999) to alleviate statistical inference intricacies inherit in the long horizon forecasting for three different monetary models (flexible price, sticky price and relative price) for selected developing economies. The empirical result shows the superiority of sticky price model along with the evidence of exchange rate predictability for high inflation economies.Foreign exchange; international finance; forecasting;

Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model

The purpose of this paper is to compare the forecasting power of DFM and LBVAR models as they are used to forecast house price growth rates for 42 metropolitan areas in the United States. The forecasting performances of these two large-scale models are compared based on the Theil U-statistic.Housing market, DFM, LBVAR, dynamic PCA, Demand and Price Analysis,