Marco Bee

Agricultural survey methods

List of Contributors. Introduction. 1 The present state of agricultural statistics in developed countries: situation and challenges. 1.1 Introduction. 1.2 Current state and political and methodological context. 1.3 Governance and horizontal issues. 1.4 Development in the demand for agricultural statistics. 1.5 Conclusions. Acknowledgements. Reference. Part I Census, Frames, Registers and Administrative Data. 2 Using administrative registers for agricultural statistics. 2.1 Introduction. 2.2 Registers, register systems and methodological issues. 2.3 Using registers for agricultural statistics. 2.4 Creating a farm register: the population. 2.5 Creating a farm register: the statistical units. 2.6 Creating a farm register: the variables. 2.7 Conclusions. References. 3 Alternative sampling frames and administrative data. What is the best data source for agricultural statistics? 3.1 Introduction. 3.2 Administrative data. 3.3 Administrative data versus sample surveys. 3.4 Direct tabulation of administrative data. 3.5 Errors in administrative registers. 3.6 Errors in administrative data. 3.7 Alternatives to direct tabulation. 3.8 Calibration and small-area estimators. 3.9 Combined use of different frames. 3.10 Area frames. 3.11 Conclusions. Acknowledgements. References. 4 Statistical aspects of a census. 4.1 Introduction. 4.2 Frame. 4.3 Sampling. 4.4 Non-sampling error. 4.5 Post-collection processing. 4.6 Weighting. 4.7 Modelling. 4.8 Disclosure avoidance. 4.9 Dissemination. 4.10 Conclusions. References. 5 Using administrative data for census coverage. 5.1 Introduction. 5.2 Statistics Canada s agriculture statistics programme. 5.3 1996 Census. 5.4 Strategy to add farms to the farm register. 5.5 2001 Census. 5.6 2006 Census. 5.7 Towards the 2011 Census. 5.8 Conclusions. Acknowledgements. References. Part II Sample Design, Weighting and Estimation. 6 Area sampling for small-scale economic units. 6.1 Introduction. 6.2 Similarities and differences from household survey design. 6.3 Description of the basic design. 6.4 Evaluation criterion: the effect of weights on sampling precision. 6.5 Constructing and using strata of concentration . 6.6 Numerical illustrations and more flexible models. 6.7 Conclusions. Acknowledgements. References. 7 On the use of auxiliary variables in agricultural survey design. 7.1 Introduction. 7.2 Stratification. 7.3 Probability proportional to size sampling. 7.4 Balanced sampling. 7.5 Calibration weighting. 7.6 Combining ex ante and ex post auxiliary information: a simulated approach. 7.7 Conclusions. References. 8 Estimation with inadequate frames. 8.1 Introduction. 8.2 Estimation procedure. References. 9 Small-area estimation with applications to agriculture. 9.1 Introduction. 9.2 Design issues. 9.3 Synthetic and composite estimates. 9.4 Area-level models. 9.5 Unit-level models. 9.6 Conclusions. References. Part III GIS and Remote Sensing. 10 The European land use and cover area-frame statistical survey. 10.1 Introduction. 10.2 Integrating agricultural and environmental information with LUCAS. 10.3 LUCAS 2001 2003: Target region, sample design and results. 10.4 The transect survey in LUCAS 2001 2003. 10.5 LUCAS 2006: a two-phase sampling plan of unclustered points. 10.6 Stratified systematic sampling with a common pattern of replicates. 10.7 Ground work and check survey. 10.8 Variance estimation and some results in LUCAS 2006. 10.9 Relative efficiency of the LUCAS 2006 sampling plan. 10.10 Expected accuracy of area estimates with the LUCAS 2006 scheme. 10.11 Non-sampling errors in LUCAS 2006. 10.12 Conclusions. Acknowledgements. References. 11 Area frame design for agricultural surveys. 11.1 Introduction. 11.2 Pre-construction analysis. 11.3 Land-use stratification. 11.4 Sub-stratification. 11.5 Replicated sampling. 11.6 Sample allocation. 11.7 Selection probabilities. 11.8 Sample selection. 11.9 Sample rotation. 11.10 Sample estimation. 11.11 Conclusions. 12 Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. 12.1 Introduction. 12.2 Satellites and sensors. 12.3 Accuracy, objectivity and cost-efficiency. 12.4 Main approaches to using EO for crop area estimation. 12.5 Bias and subjectivity in pixel counting. 12.6 Simple correction of bias with a confusion matrix. 12.7 Calibration and regression estimators. 12.8 Examples of crop area estimation with remote sensing in large regions. 12.9 The GEOSS best practices document on EO for crop area estimation. 12.10 Sub-pixel analysis. 12.11 Accuracy assessment of classified images and land cover maps. 12.12 General data and methods for yield estimation. 12.13 Forecasting yields. 12.14 Satellite images and vegetation indices for yield monitoring. 12.15 Examples of crop yield estimation/forecasting with remote sensing. References. 13 Estimation of land cover parameters when some covariates are missing. 13.1 Introduction. 13.2 The AGRIT survey. 13.3 Imputation of the missing auxiliary variables. 13.4 Analysis of the 2006 AGRIT data. 13.5 Conclusions. References. Part IV Data Editing and Quality Assurance. 14 A generalized edit and analysis system for agricultural data. 14.1 Introduction. 14.2 System development. 14.3 Analysis. 14.4 Development status. 14.5 Conclusions. References. 15 Statistical data editing for agricultural surveys. 15.1 Introduction. 15.2 Edit rules. 15.3 The role of automatic editing in the editing process. 15.4 Selective editing. 15.5 An overview of automatic editing. 15.6 Automatic editing of systematic errors. 15.7 The Fellegi Holt paradigm. 15.8 Algorithms for automatic localization of random errors. 15.9 Conclusions. References. 16 Quality in agricultural statistics. 16.1 Introduction. 16.2 Changing concepts of quality. 16.3 Assuring quality. 16.4 Conclusions. References. 17 Statistics Canada s Quality Assurance Framework applied to agricultural statistics. 17.1 Introduction. 17.2 Evolution of agriculture industry structure and user needs. 17.3 Agriculture statistics: a centralized approach. 17.4 Quality Assurance Framework. 17.5 Managing quality. 17.6 Quality management assessment. 17.7 Conclusions. Acknowledgements. References. Part V Data Dissemination and Survey Data Analysis. 18 The data warehouse: a modern system for managing data. 18.1 Introduction. 18.2 The data situation in the NASS. 18.3 What is a data warehouse? 18.4 How does it work? 18.5 What we learned. 18.6 What is in store for the future? 18.7 Conclusions. 19 Data access and dissemination: some experiments during the First National Agricultural Census in China. 19.1 Introduction. 19.2 Data access and dissemination. 19.3 General characteristics of SDA. 19.4 A sample session using SDA. 19.5 Conclusions. References. 20 Analysis of economic data collected in farm surveys. 20.1 Introduction. 20.2 Requirements of sample surveys for economic analysis. 20.3 Typical contents of a farm economic survey. 20.4 Issues in statistical analysis of farm survey data. 20.5 Issues in economic modelling using farm survey data. 20.6 Case studies. References. 21 Measuring household resilience to food insecurity: application to Palestinian households. 21.1 Introduction. 21.2 The concept of resilience and its relation to household food security. 21.3 From concept to measurement. 21.4 Empirical strategy. 21.5 Testing resilience measurement. 21.6 Conclusions. References. 22 Spatial prediction of agricultural crop yield. 22.1 Introduction. 22.2 The proposed approach. 22.3 Case study: the province of Foggia. 22.4 Conclusions. References. Author Index. Subject Index.

Dental arch changes following rapid maxillary expansion.

The purpose of this research was to evaluate changes in upper arch dimension and form following rapid maxillary expansion (RME) using a modified Haas appliance in the primary dentition. The sample comprised 49 children [17 males, 32 females, mean age 7 years 5 months, standard deviation (SD) 1 year 1 month] with a crossbite or maxillary crowding. Twenty patients had a normal SN-GoGn angle (7 males, 13 females, mean 33.25 degrees, SD 2.10), three were low angle (1 male, 2 females, mean 27.67 degrees, SD 2.31), and 22 were high angle (8 males, 14 females, mean 39.95 degrees, SD 3.15). The vertical dimensions of four patients could not be measured, due to the unavailability of radiographs. Expansion was undertaken to either correct a crossbite or treat maxillary crowding. The upper dental casts were analysed using a computerized system: before treatment (T1), at appliance removal (T2), and 2 years 4 months after appliance removal (T3). Using bootstrap statistical analysis applied to distance ratio values [Euclidean distance matrix analysis (EDMA)], it was found that 48 patients showed a change in arch form. In 40.82 per cent (n = 20, group A), the arch form changed from T1 to T2, T1 to T3, and T2 to T3. In 32.65 per cent (n = 16, group B), it varied from T1 to T2 but relapsed at T3 to the form of T1. For 24.5 per cent (n = 12, group C), it changed from T1 to T2 but maintained the same form at T3. The favourable characteristics for obtaining expansion, identified by logistic regression analysis, were being male, of an immature stage of dental development (lateral incisor not fully erupted) and the presence of a lateral crossbite. Intercanine and intermolar widths, arch length, and the distance between the interincisive point and the line joining the canines (depth of the intercanine arch) at the different time points were analysed using a two-tailed t-test (P < 0.05). For the whole group, the increase in intercanine and intermolar width and in the depth of the intercanine arch was significant. Comparison between groups A, B, and C was undertaken using an analysis of variance, but there was no significant difference between the groups. This modified type of Haas appliance was able to increase the transverse dimension of the maxillary dental arch in the mixed dentition. The most appropriate timing for treatment appears to be before the eruption of the permanent lateral incisors.

Copula-Based Multivariate Models with Applications to Risk Management and Insurance

The purpose of this paper consists in analysing the relevance of dependence concepts in finance, insurance and risk management, exploring how these concepts can be implemented in a statistical model via copula functions and pointing out some difficulties related to this methodology. In particular, we first review the statistical models currently used in the actuarial and financial fields when dealing with loss data; then we show, by means of two risk management applications, that copula-based models are very flexible but sometimes difficult to set up and to estimate; finally we study, by means of a simulation experiment, the properties of the maximum likelihood estimators of the Gaussian and Gumbel copula.

Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models

Continuous-time stochastic volatility models are becoming increasingly popular in finance because of their flexibility in accommodating most stylized facts of financial time series. However, their estimation is difficult because the likelihood function does not have a closed-form expression. A characteristic function-based estimation method for non-Gaussian Ornstein-Uhlenbeck-based stochastic volatility models is proposed. Explicit expressions of the characteristic functions for various cases of interest are derived. The asymptotic properties of the estimators are analyzed and their small-sample performance is evaluated by means of a simulation experiment. Finally, two real-data applications show that the superposition of two Ornstein-Uhlenbeck processes gives a good approximation to the dependence structure of the process.

Firms’ Bankruptcy and Turnover in a Macroeconomy

The so-called “rational expectations revolution” that has completely reshaped economic theory and general equilibrium theory in the last two decades has, incidentally, brought earlier ideas on the crucial importance of agents’ knowledge, information and beliefs to the forefront forcing modem followers of those ideas to reconsider them far more deeply, systematically and rigorously (Arrow (1986), Hahn (1977, 1981)). It soon turned out that when agents act upon beliefs and engage in out-of-equilibrium learning, heterogeneity (of beliefs) and self-referentiality (of market outcomes)1 may determine large sets of multiple equilibria, and of dynamic paths of the economy, which collapse onto the unique rational-expectations (RE) competitive general equilibrium only under a number of restrictive conditions2.

Approximate maximum likelihood estimation of the autologistic model

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models because it does not require evaluation of the normalizing constant, which is often computationally intractable. An AMLE-based algorithm for parameter estimation of the autologistic model is proposed. The impact of the numerical choice of the input parameters of the algorithm is studied by means of extensive simulation experiments, and the outcomes are compared to existing approaches. AMLE is much more precise, in terms of Mean-Square-Error, with respect to Maximum pseudo-likelihood, and comparable to ML-type methods. Although the computing time is non-negligible, the implementation is straightforward and the convergence conditions are weak in most practically relevant cases.

Modelling credit default swap spreads by means of normal mixtures and copulas

This paper develops a multivariate statistical model for the analysis of credit default swap spreads. Given the large excess kurtosis of the univariate marginal distributions, it is proposed to model them by means of a mixture of distributions. However, the multivariate extension of this methodology is numerically difficult, so that copulas are used to capture the structure of dependence of the data. It is shown how to estimate the parameters of the marginal distributions via the EM algorithm; then the parameters of the copula are estimated and standard errors computed through the nonparametric bootstrap. An application to credit default swap spreads of some European reference entities and extensive simulation results confirm the effectiveness of the method.

A Maximum Entropy Approach to Loss Distribution Analysis

In this paper we propose an approach to the estimation and simulation of loss distributions based on Maximum Entropy (ME), a non-parametric technique that maximizes the Shannon entropy of the data under moment constraints. Special cases of the ME density correspond to standard distributions; therefore, this methodology is very general as it nests most classical parametric approaches. Sampling the ME distribution is essential in many contexts, such as loss models constructed via compound distributions. Given the difficulties in carrying out exact simulation,we propose an innovative algorithm, obtained by means of an extension of Adaptive Importance Sampling (AIS), for the approximate simulation of the ME distribution. Several numerical experiments confirm that the AIS-based simulation technique works well, and an application to insurance data gives further insights in the usefulness of the method for modelling, estimating and simulating loss distributions.

A simple approach to the estimation of Tukey's gh distribution

ABSTRACT The Tukeys gh distribution is widely used in situations where skewness and elongation are important features of the data. As the distribution is defined through a quantile transformation of the normal, the likelihood function cannot be written in closed form and exact maximum likelihood estimation is unfeasible. In this paper we exploit a novel approach based on a frequentist reinterpretation of Approximate Bayesian Computation for approximating the maximum likelihood estimates of the gh distribution. This method is appealing because it only requires the ability to sample the distribution. We discuss the choice of the input parameters by means of simulation experiments and provide evidence of superior performance in terms of Root-Mean-Square-Error with respect to the standard quantile estimator. Finally, we give an application to operational risk measurement.

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.