The term structure of inflation expectations

We use information in the term structure of survey-based forecasts of inflation to estimate a factor hidden in the nominal yield curve. We construct a model that accommodates forecasts over multiple horizons from multiple surveys and Treasury real and nominal yields by allowing for differences between risk-neutral, subjective, and objective probability measures. We establish that model-based inflation expectations are driven by inflation, output, and one latent factor. We find that this factor affects inflation expectations at all horizons but has almost no effect on the nominal yields; that is, the latent factor is hidden. We show that this hidden factor is not related to either current and past inflation or the standard set of macro variables studied in the literature. Consistent with the theoretical property of a hidden factor, our model outperforms a standard macro-finance model in its forecasting of inflation and yields.     

Effect of foreign GAAP earnings and Form 20-F reconciliations on revisions of analysts' forecasts

A controversial area of U.S. securities regulations involves the Securities and Exchange Commission's (SEC) financial reporting requirements for foreign firms, specifically, the necessity of providing a quantitative reconciliation to U.S. GAAP (Form 20-F). The results of earnings-returns research to date indicate that the release of foreign GAAP earnings provides important information. However, the results of earnings-returns studies using reconciled information are mixed. Instead of using an earnings-returns methodology adopted in prior research, this study utilizes analysts' revisions as a market indicator of the effect of information released in foreign GAAP earnings and the reconciled information in Form 20-F. Additionally, the study investigates the influence of four firm-specific variables in the firm's information environment—similarities of accounting systems, analyst following, difference between reconciled earnings and foreign GAAP earnings, and dispersion of analysts' expectations—on positive abnormal revision activities of financial analysts at the time of filing Form 20-F.The results indicate that the release of foreign GAAP earnings (at earnings announcement dates) and reconciled information (at the time of filing Form 20-F) contains relevant information as measured by analysts' revisions. Further, variables representing analyst following, change in reconciled earnings, and dispersion of analysts' expectations are significant in explaining the variation observed in positive abnormal revisions.     

Do foreign exchange forecasters apply asymmetric loss functions? Evidence from three major exchange rates

This article investigates which type of loss function is consistent with the hypothesis that major exchange rate forecasts, i.e. the euro, the British pound, and the Japanese yen vis-à-vis the US dollar, are rational. We apply a comprehensive data set, which also allows us to examine different forecast horizons and heterogeneity of forecasters.     

Political forecast cycles

A moral hazard model is used to show why overly optimistic revenue forecasts prior to elections can be optimal: Opportunistic governments can increase spending and appear more competent; ex post deficits emerge in election years, thereby producing political forecast cycles – as also found for US states in the empirical literature. Additionally, we obtain three theoretical results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.     

ALICE: Composite leading indicators for euro area inflation cycles

This paper develops Area-wide Leading Inflation CyclE (ALICE) indicators with the aim to provide early signals for turning points in the euro area headline and core inflation cycles. The headline (core) ALICE leads the reference cycle by three (four) months and the lead times extend to five (nine) months based on longer-leading series. Both ALICE indicators signalled turning points in the inflation cycles ex post and also performed well in a simulated real-time exercise. Moreover, in particular the headline ALICE also appeared to be useful for quantitative forecasting of the direction and level of inflation.     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

Effects of consumer cohorts and age on meat expenditures in the United States

Meat demand is likely influenced by the birth cohort and age of the individual. In this study, we examine the demand for beef, pork, poultry, and other meat in the United States using the 1984–2012 Consumer Expenditure Survey and the almost ideal demand system with the incorporation of age, period, and cohort (APC) effects. We find that the model with APC effects performs better than the models without APC effects. The results indicate that cohorts born in earlier time periods are expected to purchase significantly less poultry compared to cohorts born in later time periods, when they are measured at the same age. Over the life cycle, purchase of poultry is expected to increase with age while the opposite is true for red meat. We also find that the own-price elasticity for beef is highest among the products examined, while the own-price elasticity for other meat is lowest and the inclusion of APC effects increases the absolute value of the own-price elasticities for beef, pork, and poultry, but reduces the own-price elasticity for other meat. Our forecasts indicate that the aggregate poultry purchase will continue to increase until 2022, while the aggregate purchase of red meat will slightly increase until 2017, but will either decrease or stay at same level from year 2017 to 2022.     

Fast and accurate yearly time series forecasting with forecast combinations

It has long been known that combination forecasting strategies produce superior out-of-sample forecasting performances. In the M4 forecasting competition, a very simple forecast combination strategy achieved third place on yearly time series. An analysis of the ensemble model and its component models suggests that the competitive accuracy comes from avoiding poor forecasts, rather than from beating the best individual models. Moreover, the simple ensemble model can be fitted very quickly, can easily scale horizontally with additional CPU cores or a cluster of computers, and can be implemented by users very quickly and easily. This approach might be of particular interest to users who need accurate yearly forecasts without being able to spend significant time, resources, or expertise on tuning models. Users of the R statistical programming language can access this modeling approach using the “forecastHybrid” package.     

Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height

We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall.     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.