COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

ADDPLOT: Stata module to add twoway plot objects to an existing twoway graph

addplot adds twoway plot objects to an existing twoway graph. This is useful if you want to add additional objects such as titles or extra data points to a twoway graph after it has been created. Most of what addplot can do, can also be done by rerunning the original graph command including additional options or plot statements. addplot, however, might be useful if you have to modify a graph for which you cannot rerun the original command, for example, because you only have the graph file but not the data that were used to create the graph. Furthermore, addplot can do certain things that would be difficult to achieve in a single graph command (e.g. customizing individual subgraphs within a by-graph). addplot also provides a substitute for some of the functionality of the graph editor.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Training Module for ALS Moderators: Report on conceptual and methodological aspects of the first workshop in Pemba Metuge, Cabo Delgado, Mozambique, April 2005

Report on conceptual and methodological aspects of the first workshop for training moderators and trainers in ALS and sustainable management of natural resources, in Pemba Metuge, Cabo Delgado, Mozambique, April 2005.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

A growth factor-induced, spatially organizing cytoskeletal module enables rapid and persistent fibroblast migration

Directional migration requires robust front/back polarity. We find that fibroblasts treated with platelet-derived growth factor (PDGF) and prepolarized by plating on a fibronectin line substrate exhibit persistent migration for hours. This does not occur in the absence of PDGF or on uniformly coated fibronectin substrates. Persistent migration arises from establishment of two functional modules at cell front and back. At the front, formation of a zone containing podosome-like structures (PLS) dynamically correlates with low RhoA and myosin activity and absence of a contractile lamella. At the back, myosin contractility specifically controls tail retraction with minimal crosstalk to the front module. The PLS zone is maintained in a dynamic steady state that preserves size and position relative to the cell front, allowing for long-term coordination of front and back modules. We propose that front/back uncoupling achieved by the PLS zone is crucial for persistent migration in the absence of directional cues.

LORENZ: Stata module to estimate and display Lorenz curves and concentration curves

Lorenz estimates Lorenz and concentration curves from individual-level data and, optionally, displays the results in a graph. Relative as well as generalized, absolute, unnormalized, or custom-normalized Lorenz or concentration curves are supported, and tools for computing contrasts between different subpopulations or outcome variables are provided. Variance estimation for complex samples is fully supported.

PANELS: Stata module to count panels and apply a command to panel units

panels provides a quick way to count the number of panels (groups) in a dataset and display some basic information about the sizes of the panels. Furthermore, -panels- can be used as a prefix command to other Stata commands to apply them to panel units instead of individual observations. This is useful, for example, if you want to compute frequency distributions or summary statistics for panel characteristics.

Neutrality in the choice of number of firms or level of fixed costs in calibrating an Armington-Krugman-Melitz encompassing module for applied general equilibrium models

This paper shows how an Armington-Krugman-Melitz encompassing module based on Dixon and Rimmer (2012) can be calibrated, and clarifies the choice of initial levels for two kinds of number of firms, or parameter values for two kinds of fixed costs, that enter a Melitz-type specification can be set freely to any preferred value, just as the cases we derive quantities from given value data assuming some of the initial prices to be unity. In consequence, only one kind of additional information, which is on the shape parameter related to productivity, just is required in order to incorporate Melitz-type monopolistic competition and heterogeneous firms into a standard applied general equilibrium model. To be a Krugman-type, nothing is needed. This enables model builders in applied economics to fully enjoy the featured properties of the theoretical models invented by Krugman (1980) and Melitz (2003) in practical policy simulations at low cost.

Behavioral characteristics of applied general equilibrium models with an Armington-Krugman-Melitz encompassing module

This paper explore how simulation results change with different choice of trade specification, and the strength of preference for traded variety by economic agent differs, utilizing two types of three-region, three-sector AGE model that includes the Armington-Krugman-Melitz Encompassing module based on Dixon and Rimmer (2012). Simulation experiments reveal that: (1) the Melitz-type specification does not always enhance effectiveness of a certain policy change more than the one obtained with the Krugman-type, especially when economic agents' preference for traded variety is not so strong; (2) there are likely to be points where the volumes of effects obtained with the Melitz-type exceed the ones with the Krugman-type; and (3) the preference of the producers, those who are in the sectors that exhibit increasing returns to scale, for traded variety might be the engine of explosive effects as suggested by Fujita, et al. (2000).

R&D on crystalline silicon technology: From metallurgical silicon to the PV module

Founded by Antonio Luque in 1979 Personnel: Personnel: 6464 full full-time time staff (19 professors staff (19 professors, 44 PhD PhD researchers 28 PhD students 13 researchers, 28 PhD students, 13 administrative and maintenance staff), 19 “part time” (11 “external PhD students”, 8 master students) Objective: Objective: Contribute to the deployment of Photovoltaic Solar Electricity through R&D& Contribute to the deployment of Photovoltaic Solar Electricity through R&D&i