Can Savings Help Overcome Income Instability?

Analyzes the extent of instability in monthly income among low-income families, the risk of facing material hardship due to income volatility, and whether modest liquid assets offers protection from such hardship. Considers policy implications

Impact of sample preservation and manipulation on insect gut microbiome profiling : a test case with fruit flies (Diptera, Tephritidae)

High-throughput sequencing (HTS) techniques are of great value for the investigation of microbial communities, and have been extensively used to study the gut microbiome. While most studies focus on the human gut, many others have investigated insects. However, because of the rapid spread of HTS techniques, a lot of variation exists in the protocols for sample preparation. In the present study, we investigated the impact of two widely adopted sample-processing procedures preceding library preparation, i.e., preservation of insect tissue in 70% ethanol (EtOH) and sample dissection. We used the fruit fly Ceratitis capitata (Diptera: Tephritidae) as a model organism and set up two experiments, one comparing the effects of sample manipulation and preservation across life stages and the other across fruit samples from different sources. The results of this study showed no major effects of dissection on the outcome of HTS. However, EtOH preservation did have effects on the recovered gut microbiome, the main effect being a significant reduction of the dominant genus, Providencia, in EtOH-preserved samples. Less abundant bacterial groups were also affected resulting in altered microbial profiles obtained from samples preserved in 70% EtOH. These results have important implications for the planning of future studies and when comparing studies that used different sample preparation protocols

A Nonparametric Approach to Evaluating Inflation-Targeting Regimes

We use a variety of nonparametric test statistics to evaluate the inflation- targeting regimes of Australia, Canada, New Zealand, Sweden and the UK. We argue that a sensible approach of evaluation must rely on a variety of methods, among them parametric and nonparametric econometric methods, for robustness and completeness. Our evaluation strategy is based on examining two possible policy implications of inflation targeting: First, a welfare implication and second, a real variability implication. The welfare implication involves evaluating a utility function, and tested by testing whether (1) the distributions of the levels and the growth rates of private consumption and leisure per capita remained unchanged under inflation targeting, i.e., first-order stochastic dominance; and (2) testing a linear combination of consumption and leisure per capita, where the parameter describing the utility of leisure or the relative preference of leisure is calibrated. Then we introduce nonparametric univariate and multivariate statistical methods to test whether the first and second moments of a variety of real variables, such as the real exchange rate depreciation rate, real GDP per capita growth rate in addition to private consumption per capita and leisure per capita growth rates, remained unchanged under inflation targeting, decreased or increased significantly. There seems to be some evidence of increased welfare under inflation-targeting regimes, but no concrete evidence is found that inflation targeting policy, in general, reduces real variability. Some cross country differences are also found.Nonparametric, First-order stochastic dominance, sudden shift in the distribution, inflation targeting.

Sparse Vector Autoregressive Modeling

The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions and difficult-to-interpret temporal dependence. To overcome such drawbacks, we propose a 2-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero. The first stage selects non-zero AR coefficients based on an estimate of the partial spectral coherence (PSC) together with the use of BIC. The PSC is useful for quantifying the conditional relationship between marginal series in a multivariate process. A refinement second stage is then applied to further reduce the number of parameters. The performance of this 2-stage approach is illustrated with simulation results. The 2-stage approach is also applied to two real data examples: the first is the Google Flu Trends data and the second is a time series of concentration levels of air pollutants.Comment: 39 pages, 7 figure

Breaks, trends and the attribution of climate change: a time-series analysis

Climate change detection and attribution have been the subject of intense research and debate over at least four decades. However, direct attribution of climate change to anthropogenic activities using observed climate and forcing variables through statistical methods has remained elusive, partly caused by difficulties to correctly identify the time-series properties of these variables and by the limited availability of methods to relate nonstationary variables. This paper provides strong evidence concerning the direct attribution of observed climate change to anthropogenic greenhouse gases emissions by first investigating the univariate time-series properties of observed global and hemispheric temperatures and forcing variables and then by proposing statistically adequate multivariate models. The results show that there is a clear anthropogenic fingerprint on both global and hemispheric temperatures. The signal of the well-mixed Greenhouse Gases (GHG) forcing in all temperature series is very clear and accounts for most of their secular movements since the beginning of observations. Both temperature and forcing variables are characterized by piecewise linear trends with abrupt changes in their slopes estimated to occur at different dates. Nevertheless, their long-term movements are so closely related that the observed temperature and forcing trends cancel out. The warming experimented during the last century was mainly due to the increase in GHG which was partially offset by the effect of tropospheric aerosols. Other forcing sources, such as solar, are shown to only contribute to (shorter-term) variations around the GHG forcing trend.Published versio

Why Has U.S. Inflation Become Harder to Forecast?

Forecasts of the rate of price inflation play a central role in the formulation of monetary policy, and forecasting inflation is a key job for economists at the Federal Reserve Board. This paper examines whether this job has become harder and, to the extent that it has, what changes in the inflation process have made it so. The main finding is that the univariate inflation process is well described by an unobserved component trend-cycle model with stochastic volatility or, equivalently, an integrated moving average process with time-varying parameters; this model explains a variety of recent univariate inflation forecasting puzzles. It appears currently to be difficult for multivariate forecasts to improve on forecasts made using this time-varying univariate model.