Is a DFM Well Suited for Forecasting Regional House Price Inflation?

This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this study contains 282 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the Mean Absolute Errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001Q1 to 2006Q4, indicate that, in majority of the cases, the DFM outperforms the VARs, both classical and Bayesian, with the latter incorporating both spatial and non-spatial models. Our results, thus, indicate the blessing of dimensionality.Dynamic Factor Model, VAR, BVAR, Forecast Accuracy

Could we have predicted the recent downturn in the South African Housing Market?

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to 2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01 to 2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over the period of 2003:01 to 2008:02.Dynamic Factor Model, BVAR, Forecast Accuracy

Formation of water and methanol in star forming molecular clouds

We study the formation of water and methanol in the dense cloud conditions to find the dependence of its production rate on the binding energies, reaction mechanisms, temperatures, and grain site number. We wish to find the effective grain surface area available for chemical reaction and the effective recombination timescales as functions of grain and gas parameters. We used a Monte Carlo simulation to follow the chemical processes occurring on the grain surface. We find that the formation rate of various molecules is strongly dependent on the binding energies. When the binding energies are high, it is very difficult to produce significant amounts of the molecular species. Instead, the grain is found to be full of atomic species. The production rates are found to depend on the number density in the gas phase. We show that the concept of the effective grain surface area, which we introduced in our earlier work, plays a significant role in grain chemistry. We compute the abundance of water and methanol and show that the results strongly depend on the density and composition in the gas phase, as well as various grain parameters. In the rate equation, it is generally assumed that the recombination efficiencies are independent of the grain parameters, and the surface coverage. Presently, our computed parameter $\alpha$ for each product is found to depend on the accretion rate, the grain parameters and the surface coverage of the grain. We compare our results obtained from the rate equation and the one from the effective rate equation, which includes $\alpha$. At the end we compare our results with the observed abundances.Comment: 12 pages, 16 figures in eps forma

Knowledge regarding diabetes among expectant mothers attending ante natal clinic of a tertiary care institution of Kolkata: a cross sectional study

Background: Prevalence of gestational diabetes (GDM) has increased tremendously in India. Prevention of the condition is possible through health education. However, knowledge regarding diabetes is poor among Indian women. Thus, examining the basic knowledge regarding diabetes among expectant mothers is necessary to identify areas of deficit.Methods: An observational, analytical study of cross-sectional design was undertaken to assess the knowledge regarding diabetes among 173 expectant mothers attending the ante-natal clinic of R. G. Kar Medical College, Kolkata using a pre tested schedule.Results: Mean score of the respondents were less than 3 out of 8 which was a poor score. The overall mean diabetic score was similar for the antenatal mothers irrespective of the number of pregnancy (p=0.3154) but the score was greater than that for the non-pregnant women (p=0.0000). The expectant mothers showed better response compared to the controls when asked whether a person can have diabetes but be unaware of the condition; whether diabetes can harm a person’s body before diagnosis; long term complications of the disease(P0.05). The pregnant women had less reported leisure time physical exercise and first degree relative with diabetes (p<0.05).Conclusions: Mean knowledge score of the expectant mothers was more than the women controls though the overall score was poor. A structured awareness program is urgently needed which would first address diabetes in general and then the specifics of GDM