Bubbles in House Prices and their Impact on Consumption: Evidence for the US

This paper provides evidence that some aggregate and regional U.S. real house price indices exhibited a bubble in the last few years according to the Phillips et al. (2007) unit root test. We subsequently investigate whether house price acceleration (deceleration) had a signi.cant impact on consumption in an error correction mechanism implied by a wide class of optimizing models. Our results support the argument that real house prices have their major effect on consumption only during the bubble period

Are analysts' loss functions asymmetric?

Recent research by Gu and Wu (2003) and Basu and Markov (2004) suggests that the well-known optimism bias in analysts? earnings forecasts is attributable to analysts minimizing symmetric, linear loss functions when the distribution of forecast errors is skewed. An alternative explanation for forecast bias is that analysts have asymmetric loss functions. We test this alternative explanation. Theory predicts that if loss functions are asymmetric then forecast error bias depends on forecast error variance, but not necessarily on forecast error skewness. Our results confirm that the ex ante forecast error variance is a significant determinant of forecast error and that, after controlling for variance, the sign of the coefficient on forecast error skewness is opposite to that found in prior research. Our results are consistent with financial analysts having asymmetric loss functions. Further analysis reveals that forecast bias varies systematically across style portfolios formed on book-to-price and market capitalization. These firm characteristics capture systematic variation in forecast error variance and skewness. Within style portfolios, forecast error variance continues to play a dominant role in explaining forecast error.

Are analysts? loss functions asymmetric?

Recent research by Gu and Wu (2003) and Basu and Markov (2004) suggests that the well-known optimism bias in analysts? earnings forecasts is attributable to analysts minimizing symmetric, linear loss functions when the distribution of forecast errors is skewed. An alternative explanation for forecast bias is that analysts have asymmetric loss functions. We test this alternative explanation. Theory predicts that if loss functions are asymmetric then forecast error bias depends on forecast error variance, but not necessarily on forecast error skewness. Our results confirm that the ex ante forecast error variance is a significant determinant of forecast error and that, after controlling for variance, the sign of the coefficient on forecast error skewness is opposite to that found in prior research. Our results are consistent with financial analysts having asymmetric loss functions. Further analysis reveals that forecast bias varies systematically across style portfolios formed on book-to-price and market capitalization. These firm characteristics capture systematic variation in forecast error variance and skewness. Within style portfolios, forecast error variance continues to play a dominant role in explaining forecast error.

An examination of the precipitation delivery mechanisms for Dolleman Island, eastern Antarctic Peninsula

Copyright @ 2004 Wiley-BlackwellThe variability of size and source of significant precipitation events were studied at an Antarctic ice core drilling site: Dolleman Island (DI), located on the eastern coast of the Antarctic Peninsula. Significant precipitation events that occur at DI were temporally located in the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis data set, ERA-40. The annual and summer precipitation totals from ERA-40 at DI both show significant increases over the reanalysis period. Three-dimensional backwards air parcel trajectories were then run for 5 d using the ECMWF ERA-15 wind fields. Cluster analyses were performed on two sets of these backwards trajectories: all days in the range 1979–1992 (the climatological time-scale) and a subset of days when a significant precipitation event occurred. The principal air mass sources and delivery mechanisms were found to be the Weddell Sea via lee cyclogenesis, the South Atlantic when there was a weak circumpolar trough (CPT) and the South Pacific when the CPT was deep. The occurrence of precipitation bearing air masses arriving via a strong CPT was found to have a significant correlation with the southern annular mode (SAM); however, the arrival of air masses from the same region over the climatological time-scale showed no such correlation. Despite the dominance in both groups of back trajectories of the westerly circulation around Antarctica, some other key patterns were identified. Most notably there was a higher frequency of lee cyclogenesis events in the significant precipitation trajectories compared to the climatological time-scale. There was also a tendency for precipitation trajectories to come from more northerly latitudes, mostly from 50–70°S. The El Niño Southern Oscillation (ENSO) was found to have a strong influence on the mechanism by which the precipitation was delivered; the frequency of occurrence of precipitation from the east (west) of DI increased during El Niño (La Niña) events

Updated world map of the Köppen-Geiger climate classification

International audienceAlthough now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Köppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Köppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Köppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Köppen-Geiger climate map is freely available electronically in the Supplementary Material Section

Planck Observations of M33

We have performed a comprehensive investigation of the global integrated flux density of M33 from radio to ultraviolet wavelengths, finding that the data between $\sim$100 GHz and 3 THz are accurately described by a single modified blackbody curve with a dust temperature of $T_\mathrm{dust}$ = 21.67$\pm$0.30 K and an effective dust emissivity index of $\beta_\mathrm{eff}$ = 1.35$\pm$0.10, with no indication of an excess of emission at millimeter/sub-millimeter wavelengths. However, sub-dividing M33 into three radial annuli, we found that the global emission curve is highly degenerate with the constituent curves representing the sub-regions of M33. We also found gradients in $T_\mathrm{dust}$ and $\beta_\mathrm{eff}$ across the disk of M33, with both quantities decreasing with increasing radius. Comparing the M33 dust emissivity with that of other Local Group members, we find that M33 resembles the Magellanic Clouds rather than the larger galaxies, i.e., the Milky Way and M31. In the Local Group sample, we find a clear correlation between global dust emissivity and metallicity, with dust emissivity increasing with metallicity. A major aspect of this analysis is the investigation into the impact of fluctuations in the Cosmic Microwave Background (CMB) on the integrated flux density spectrum of M33. We found that failing to account for these CMB fluctuations would result in a significant over-estimate of $T_\mathrm{dust}$ by $\sim$5 K and an under-estimate of $\beta_\mathrm{eff}$ by $\sim$0.4.Comment: Accepted for publication in MNRA