Habit, aggregation and long memory: evidence from television audience data

Many economic outcomes appear to be influenced by habit or commitment, giving rise to persistence. In cases where the decision is binary and persistent, the aggregation of individual time series can result in a fractionally integrated process for the aggregate data. Certain television programmes appear to engender commitment on the part of viewers and the decision to watch or not is clearly binary. We report an empirical analysis of television audience data and show that these series can be modelled as I(d) processes. We also investigate the proposition that temporal aggregation of a fractionally-integrated series leaves the value of d unchanged.

Amplitude and frequency control of a vibratory pile driver

Abstract—This paper describes the digital control of a vibratory pile driver in which the vibration is generated via two tandem pairs of electrically driven, geared, contra-rotating eccentrics. Experimental results are included to show the controller-induced system dynamics for a variety of load condtions, and to highlight the fact that, if the relative phase of the eccentric pairs is not controlled, the natural tendency at high excitation frequency is for the pile driver to operate with a low vibration amplitude. An analytical technique for identifying the system parameters is presented, and analytical performance predictions are compared with experimental results. Analysis of the power flow in the system shows that, although significant power transfer occurs between the two electrical drives, the net power dissipation during pile driving is relatively low

ESTAR model with multiple fixed points. Testing and Estimation

In this paper we propose a globally stationary augmentation of the Exponential Smooth Transition Autoregressive (ESTAR) model that allows for multiple fixed points in the transition function. An F-type test statistic for the null of nonstationarity against such globally stationary nonlinear alternative is developed. The test statistic is based on the standard approximation of the nonlinear function under the null hypothesis by a Taylor series expansion. The model is applied to the U.S real interest rate data for which we find evidence of the new ESTAR process.

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

ESTAR model with multiple fixed points. Testing and Estimation

In this paper we propose a globally stationary augmentation of the Exponential Smooth Transition Autoregressive (ESTAR) model that allows for multiple fixed points in the transition function. An F-type test statistic for the null of nonstationarity against such globally stationary nonlinear alternative is developed. The test statistic is based on the standard approximation of the nonlinear function under the null hypothesis by a Taylor series expansion. The model is applied to the U.S real interest rate data for which we find evidence of the new ESTAR process

Breeding Alfalfa for Semiarid Regions in the Northern Great Plains: History and Additional Genetic Evaluations of Novel Germplasm

Yellow-flowered alfalfa (Medicago sativa subsp. falcata) (also known as sickle medic) has been the cornerstone for breeding alfalfa for dual grazing and hay production in the semiarid regions of the northern Great Plains in the US and Canada. Most, if not all, of the cultivars developed for the northern Great Plains during the 20th century, had parentage tracing back to introductions by Niels Ebbesen Hansen that were obtained from expeditions to Russia, primarily the province of Siberia, on behalf of the United States Department of Agriculture during the early 1900s. The M. falcata genome contains alleles for high levels of drought-tolerance, winter hardiness, and tolerance to grazing, but is generally deficient for commercial seed production traits, such as non-shatter, compared with common alfalfa (M. sativa). A naturalized population, tracing to USDA plant introductions to Perkins County South Dakota by N.E. Hansen in early 1900, and subsequently, facilitated by the determined seed increase and interseeding of a population by a local rancher, Norman ‘Bud’ Smith, has shown highly desirable in situ characteristics for improving rangelands in the northern Great Plains. This includes adequate seed production to build a seed bank in the soil for natural seedling recruitment and population maintenance/expansion and support the production of a commercial seed source. This review documents the seminal events in the development of cultivars to date and describes novel germplasm with potential for new cultivars in the future