Hedging effectiveness of fertilizer swaps

The fertilizer swaps market is a potential tool to protect against fertilizer price risk. The swaps evaluated here are cash settled using The Fertilizer Index. Hedge ratios and hedging effectiveness are calculated for urea and DAP diammonium phosphate (DAP)) swaps. Urea and DAP swaps perform poorly as a hedging tool over a one-week horizon. As the hedging horizon increases, the hedging effectiveness of swaps improves. The swaps are more effective in mitigating risk across ocean freight routes than across inland routes. The limited hedging effectiveness is due to high spatial basis risk in fertilizer markets.     

Do equity index industry groups improve forecasts of inflation and production? A US analysis

This study develops a new financial market indicator, which may be a useful addition to analysing real activity in the US. By taking the ratio of the price return of equity industry groups of the S&P 500 over a benchmark industry group, in this case taken to be the Utilities industry group, an indicator is created which represents the price return performance specific to each individual industry. We then perform recursive pseudo out-of-sample bivariate forecasts of future changes in the Industrial Production Index (IPI) and the Consumer Price Index (CPI) at 3-month, 6-month and 12-month horizons using each of the indicators and compare results against an AR forecast. The results of the bivariate forecasts using a number of the indicators produce better forecasts of changes in the IPI and are also significant for causality, both for the full sample period and when tested recursively. Bivariate forecasts of changes to the CPI, however, do not improve upon the AR forecasts.     

How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US,French and UK inflation forecasts

This paper proposes an empirical investigation of the impact of oil price forecast errors on inflation forecast errors for three different sets of recent forecast data: the median of SPF inflation forecasts for the United States and the Central Bank inflation forecasts for France and the United Kingdom. Mainly two salient points emerge from our results. First, there is a significant contribution of oil price forecast errors to the explanation of inflation forecast errors, whatever the country or the period considered. Second, the pass-through of oil price forecast errors to inflation forecast errors is typically multiplied by around 2 when the oil price volatility is large.     

The diversity of forecasts from macroeconomic models of the US economy

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated US recessions. We generate forecasts from six different models of the US economy and compare them to professional forecasts from the Federal Reserve??s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates.     

Oil price forecasting under asymmetric loss

Based on the approach developed by Elliott et al. (2005), we found that the loss function of a sample of oil price forecasters is asymmetric in the forecast error. Our findings indicate that the loss oil price forecasters incurred when their forecasts exceeded the price of oil tended to be larger than the loss they incurred when their forecast fell short of the price of oil. Accounting for the asymmetry of the loss function does not necessarily make forecasts look rational.     

When do the USDA forecasters make mistakes?

This study analysed forecasts for all US corn, soya bean and wheat categories published within the World Agricultural Supply and Demand Estimates (WASDE ) reports over the 1987/88 through 2009/10 marketing years in an attempt to identify patterns and better understand when the USDA forecasters make mistakes. Two general sources of errors were investigated: behavioural and macroeconomic factors. The first objective was to examine how these factors affect the size of the forecast error and the second concentrated on the direction of the error due to these effects. Our findings suggest that the largest increase in the size of USDA forecast errors was associated with structural changes in commodity markets that took place in the mid-2000s. Corn, soya bean and wheat forecast errors also grew during the periods of economic growth and with changes in exchange rates, while inflation and changes in oil price had a much smaller impact. With respect to behavioural sources, we identified patterns consistent with leniency and pessimism across different categories. Predictability of forecast errors based on the information available at the time the forecasts are made provides evidence of inefficiency and suggests that these forecasts may be improved using the findings of this study.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

A Nonlinear Approach for Modeling and Forecasting US Business Cycles

The purpose of this paper is to provide a complete evaluation of four regime-switching models by checking their performance in detecting US business cycle turning points, in replicating US business cycle features and in forecasting US GDP growth rate. Both individual and combined forecasts are considered. Results indicate that while the Markov-switching model succeeded in replicating all the NBER peak and trough dates without an extra-cycle detection, it seems to be outperformed by the Bounce-back model in term of the delay time to a correct alarm. Concerning business cycle features characterization, none of the competing models dominates over all the features. The performance of the Markov-switching and bounce back models in detecting turning points was not translated into an improved business cycle feature characterization since they are outperformed by the Floor and Ceiling model. The forecast performance of the considered models varies across regimes and across forecast horizons. That is, the model performing best in an expansion period is not necessarily the same in a recession period and similarly for the forecast horizons. Finally, combining such individual forecasts generally leads to increased forecast accuracy especially for h=1.     

Empirical confidence intervals for USDA commodity price forecasts

Conventional procedures for calculating confidence limits of forecasts generated by statistical models provide little guidance for forecasts based on a combination or a consensus process rather than formal models, as is the case with US Department of Agriculture (USDA) forecasts. This study applied and compared several procedures for calculating empirical confidence intervals for USDA forecasts of corn, soybean and wheat prices over the 1980/81 through 2006/07 marketing years. Alternative procedures were compared based on out-of-sample performance over 1995/96 through 2006/07. The results of this study demonstrate that kernel density, quantile distribution and best fitting parametric distribution (logistic) methods provided confidence intervals calibrated at the 80% level prior to harvest and 90% level after harvest. The kernel density-based method appears most accurate both before and after harvest with the final value falling inside the forecast interval 77% of the time before harvest and 92% after harvest, followed by quantile regression (73% and 91% before and after harvest, respectively) logistic distribution (73% and 90% before and after harvest, respectively) and histogram (66% and 84% before and after harvest, respectively). Overall, this study demonstrates that empirical approaches may be used to construct more accurate confidence intervals for USDA corn, soybean and wheat price forecasts.     

A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.     

Commodity prices and related equity prices

This paper shows that commodity‐sensitive stock price indices have strong power in predicting nominal and real commodity prices at short horizons (one‐month‐ahead predictions) using both in‐ and out‐of‐sample tests. The forecasts based on commodity‐sensitive stock price indices are able to significantly outperform naïve no‐change forecasts. For example, the one‐month‐ahead forecasts for nominal commodity prices reduce the mean squared prediction error by between 1.5% (for natural gas prices) and 20% (for copper prices). Moreover, the one‐month‐ahead directional forecast is found to perform significantly better than a 50:50 coin toss. As stock prices are not subject to revision, the proposed variable, which reflects timely and readily available market information, can potentially be a valuable predictor and thereby help to improve the accuracy of commodity price forecasts.     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

Testing causality among five fertilizer prices

This article identifies the causal relationships among the prices of five fertilizers using both asymptotic Wald test and resampling (bootstrap) techniques. Monthly data for urea, muriate of potash, rock phosphate, diammonium phosphate and triple super phosphate between 2008 (March) and 2016 (March) are used for the analysis. Results show that the urea price Granger causes all other fertilizer prices. The results of the study are significant in understanding the price dynamics and identify the reference fertilizer price that is useful for the fertilizer industry, farmers and government.     

Managing price risks using and local polynomial kernel forecasts

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 to forecast prices and to generate ex ante hedge ratios. The selective multiproduct hedge based on the LPK price and hedge ratio forecasts is in general found to be better than continuous hedging, no hedging and alternative forecasting procedures. Selective multivariate hedging using the LPK is found to improve hog producer's expected returns. The findings indicate that combining hedging with forecasts, especially when using the LPK procedure, can improve price risk management.     

Has US inflation really become harder to forecast?

Since the mid-1980s, Phillips curve forecasts of US inflation have been inferior to those of a conventional causal autoregression. However, little change in forecast accuracy is detected against the benchmark of a noncausal autoregression, more accurately characterizing US inflation dynamics.     

A Sequential Rationality Test of USDA Preliminary Price Estimates for Selected Program Crops: Rice, Soybeans, and Wheat

Despite recurrent evaluations on USDA price forecasts, the performance of USDA price estimates has not previously been examined in publication. To fill the void in research to this important public information, a sequential forecast evaluation procedure is applied to selected USDA price estimates: Rice, soybeans, and wheat. The evaluation procedure reveals that the USDA price estimates are short-run unbiased; however, they are not long-run rational. In addition, short-run optimality and efficiency tests suggest that USDA price estimates need to be properly scaled and fully reflect information embodied in past prices and their estimates — a possible venue to improve the predictability of USDA price estimates for the crops.     

FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.