Reporte de monitoreo sectorial. Sector acabados de la construcción en México

Gestión del conocimiento

Reporte de monitoreo sectorial. Sector materiales de construcción en Perú

Gestión del conocimiento

Encuentro Sectorial Núcleos Empresariales

Desarrollo empresarial y creación de empresa

Mapa sectorial

Desarrollo empresarial y creación de empresa

Ajuste metodología encuentro sectorial

Desarrollo empresarial y creación de empresa

Calculations on a piece of “Long Point, Head Quarters” stationary with the “Long Point Company, Canada” seal

Calculations on a piece of “Long Point, Head Quarters” stationary with the “Long Point Company, Canada” seal. There are land calculations on this sheet, n.d.

Receipt from M.Y. Keating, Books, Stationary and Newspapers, St. Catharines for books

Receipt from M.Y. Keating, Books, Stationary and Newspapers, St. Catharines for books, Dec. 23, 1887.

Oferta de trabajo un modelo de equilibrio con desempleo análisis sectorial para México (1990) [por] Angélica Sánchez Castañeda

Tesis (Maestría en Economía Industrial) U.A.N.L.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Time Reversibility of Stationary Regular Finite State Markov Chains

We propose an alternate parameterization of stationary regular finite-state Markov chains, and a decomposition of the parameter into time reversible and time irreversible parts. We demonstrate some useful properties of the decomposition, and propose an index for a certain type of time irreversibility. Two empirical examples illustrate the use of the proposed parameter, decomposition and index. One involves observed states; the other, latent states.