On robust estimation of the location parameter

(2008). Introduction to Variance Estimation. Journal of the American Statistical Association: Vol. 103, No. 483, pp. 1324-1325.

Introduction to Variance Estimation

Managing soil organic matter (SOM) stocks to address global change challenges requires well-substantiated knowledge of SOM behavior that can be clearly communicated between scientists, management practitioners, and policy makers. However, SOM is incredibly complex and requires separation into multiple components with contrasting behavior in order to study and predict its dynamics. Numerous diverse SOM separation schemes are currently used, making cross-study comparisons difficult and hindering broad-scale generalizations. Here, we recommend separating SOM into particulate (POM) and mineral-associated (MAOM) forms, two SOM components that are fundamentally different in terms of their formation, persistence, and functioning. We provide evidence of their highly contrasting physical and chemical properties, mean residence times in soil, and responses to land use change, plant litter inputs, warming, CO2 enrichment, and N fertilization. Conceptualizing SOM into POM versus MAOM is a feasible, well-supported, and useful framework that will allow scientists to move beyond studies of bulk SOM, but also use a consistent separation scheme across studies. Ultimately, we propose the POM versus MAOM framework as the best way forward to understand and predict broad-scale SOM dynamics in the context of global change challenges and provide necessary recommendations to managers and policy makers.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcb.14859

Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century.

In this paper we analyze the impact of immigrants on the type and quantity of native jobs We use data on fifteen Western European countries during the 1996-2010 period We find that immigrants, by taking manual-routine type of occupations pushed natives towards more “complex” (abstract and communication) jobs Such positive reallocation occurred while the total number of jobs held by natives was unaffected This job upgrade was associated in the short run to a 06% increase in native wages for a doubling of the immigrants’ share These results are robust to the use of two alternative IV strategies based on past settlement of immigrants across European countries measured alternatively with Census or Labor Force data The job upgrade slowed, but did not come to a halt, during the Great Recession We also document the labor market flows behind it: the complexity of jobs offered to new native hires was higher relative to the complexity of lost jobs Finally, we find evidence that such reallocation was significantly larger in countries with more flexible labor laws and that his tendency was particularly strong for less educated workers

/pdf/immigration-jobs-and-employment-protection-evidence-from-2epdeizhf8.pdf

Immigration, Jobs and Employment Protection: Evidence from Europe before and during the Great Recession

We suggest the use of an index of Internet job-search intensity (the Google Index, GI) as the best leading indicator to predict the US monthly unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the unemployment rate for different out-of-sample intervals that start before, during and after the Great Recession. Google-based models also outperform standard ones in most state-level forecasts and in comparison with the Survey of Professional Forecasters. These results survive a falsification test and are also confirmed when employing different keywords. Based on our results for the unemployment rate, we believe that there will be an increasing number of applications using Google query data in other fields of economics.

The Predictive Power of Google Searches in Forecasting Unemployment

Effective land-based solutions to climate change mitigation require actions that maximize soil carbon storage without generating surplus nitrogen. Land management for carbon sequestration is most often informed by bulk soil carbon inventories, without considering the form in which carbon is stored, its capacity, persistency and nitrogen demand. Here, we present coupling of European-wide databases with soil organic matter physical fractionation to determine continental-scale forest and grassland topsoil carbon and nitrogen stocks and their distribution between mineral-associated and particulate organic matter pools. Grasslands and arbuscular mycorrhizal forests store more soil carbon in mineral-associated organic carbon, which is more persistent but has a higher nitrogen demand and saturates. Ectomycorrhizal forests store more carbon in particulate organic matter, which is more vulnerable to disturbance but has a lower nitrogen demand and can potentially accumulate indefinitely. The share of carbon between mineral-associated and particulate organic matter and the ratio between carbon and nitrogen affect soil carbon stocks and mediate the effects of other variables on soil carbon stocks. Understanding the physical distribution of organic matter in pools of mineral-associated versus particulate organic matter can inform land management for nitrogen-efficient carbon sequestration, which should be driven by the inherent soil carbon capacity and nitrogen availability in ecosystems. Land management strategies for enhancing soil carbon sequestration need to be tailored to different soil types, depending on how much organic matter is stored in pools of mineral-associated and particulate organic matter, suggests an analysis of soil organic matter across Europe.

Soil carbon storage informed by particulate and mineral-associated organic matter

The objective of the paper is to adjust for the bias due to unit non-response and measurement error in survey estimates of total household financial wealth. Sample surveys are a useful source of information on household wealth. Yet, survey estimates are affected by non-sampling errors. In particular, in the case of household wealth, unit non-response and measurement error can severely bias the estimates. Using the Italian Survey on Household Income and Wealth (SHIW), we exploit the available auxiliary information in order to assess the magnitude of this bias. We find evidence that for this kind of survey, non-sampling errors are a major issue, possibly more serious than sampling errors. Moreover, in the case of SHIW the potential bias due to measurement error seems to outweigh that induced by non-response.

To misreport or not to report? The measurement of household financial wealth

Data from complex survey designs require special consideration with regard to estimation of finite population parameters and corresponding variance estimation procedures, as a consequence of significant departures from the simple random sampling assumption. In the past decade a number of statistical software packages have been developed to facilitate the analysis of complex survey data. All these statistical software packages are able to treat samples selected from one sampling frame containing all population units. Dual frame surveys are very useful when it is not possible to guarantee a complete coverage of the target population and may result in considerable cost savings over a single frame design with comparable precision. There are several estimators available in the statistical literature but no existing software covers dual frame estimation procedures. This gap is now filled by package Frames2. In this paper we highlight the main features of the package. The package includes the main estimators in dual frame surveys and also provides interval confidence estimation. In practice, the assumption that the sampling frame contains all population units is rarely met. Often, one finds that sampling from a frame which is known to cover approximately all units in the population is quite expensive while other frames (e.g. special lists of units) are available for cheaper sampling methods. However, the latter usually only cover an unknown or only approximately known fraction of the population. A common example of frame undercoverage is provided by telephone surveys. Estimation could be affected by serious bias due to the lack of a telephone in some households and the generalised use of mobile phones, which are sometimes replacing fixed (land) lines entirely. The potential for coverage error as a result of the exponential growth of the cell-phone only population has led to the development of dual-frame surveys. In these designs, a traditional sample from the landline frame is supplemented with an independent sample from a register of cell-phone numbers. The dual frame sampling approach assumes that two frames are available for sampling and that, overall, they cover the entire target population. The most common situation is the one represented in Figure 1 where the two frames, say frame A and frame B, show a certain degree of overlapping, so it is possible to distinguish three disjoint non-empty domains: domain a, containing units belonging to frame A but not to frame B; domain b, containing units belonging to frame B but not to frame A and domain ab, containing units belonging to both frames. As an example, consider a telephone survey where both landline and cell phone lists are available; let A be the landline frame and B the cell phone frame. Then, it is possible to distinguish three types of individuals: landline only units, cell-only units and units with both landline and cell phone, which will compose domain a, b and ab, respectively. Nevertheless, one can face some other situations depending on the relative positions of the frames. For example, Figure 2 shows the case in which frame B is totally included in frame A, that is, frame B is a subset of frame A. Here domain b is empty. We also may find scenarios where the two sampling frames exactly match, as depicted in Figure 3, where ab is the only non-empty domain. Finally, the scenario where domain ab is empty has no interest from a dual frame perspective, since it can be

/pdf/frames2-a-package-for-estimation-in-dual-frame-surveys-4443mqxa05.pdf

Frames2: A Package for Estimation in Dual Frame Surveys

Multiple frame surveys are increasingly used by large statistical agencies and private organizations to reduce frame undercoverage errors and also sampling costs. Estimation for multiple frame surveys has been considered ever since the work of Hartley (1962). In this work we extend the tools of calibration estimation developed so far for single frame surveys to the case of dual frame surveys. Cali- bration allows to handle different types of auxiliary information and can be shown to encompass as a special case the pseudo empirical maximum likelihood approach recently proposed by Rao and Wu (2010).

Calibration estimation in dual frame surveys

Between 2012 and 2015, the PMetro project collected space–time aerosol and gas measurements by using instruments integrated on one of the Minimetro cabins, a public conveyance of the town of Perugia (Italy). In this work, we use PMetro data to study the effect of vehicular traffic and meteorological measurements on the distribution of fine particulate matter by fitting a finite mixture of M-quantile regression models. Using this methodology, it is possible to account for heterogeneity in the data using random effects with a discrete distribution with masses and probabilities directly estimated from the data. This allows us to relax assumptions on the parametric shape of the distribution of the random effects and grants extra-flexibility. In addition, it allows us to investigate the relationship between fine particulate matter concentration and the covariates at different levels of the conditional response distribution. Empirical results show that radon concentration and vehicular traffic have the largest effect on the distribution of fine particulate matter and provide some guidelines for policy makers.

Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality

Maria Giovanna Ranalli

Papers

Soil carbon storage informed by particulate and mineral-associated organic matter

To misreport or not to report? The measurement of household financial wealth

Frames2: A Package for Estimation in Dual Frame Surveys

Calibration estimation in dual frame surveys

Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality