In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this paper we argue that this situation is both undesirable and unnecessary. Provided the parameters used in the microsimulation are exogenous, as is often the case in static...

Systematic sampling is a commonly used technique due to its simplicity and ease of implementation. The drawback of this simplicity is that it is not possible to estimate the design variance without bias. There are several ways to circumvent this problem. One method is to suppose that the...

This paper examines the sample proportions estimates in the presence of univariate missing categorical data. A database about smoking habits (2011 National Addiction Survey of Mexico) was used to create simulated yet realistic datasets at rates 5% and 15% of missingness, each for MCAR, MAR and...

Objective: To estimate the treatment effect from participating in an asthma intervention that was part of the National Asthma Control Program.Study Setting: Data on children who participated in asthma case management (N=270) and eligible children who did not participate in case management...

In probability sampling, variance estimation of an estimated mean or total requires developing a mathematical expression that depends on the design used to extract a sample. These formulae can be difficult to build and sometimes involve computation of joint inclusion probabilities of selection,...

The main goal of the article is to investigate forecasting quality of two approaches to modelling main macroeconomic variables without a priori assumptions concerning causality and generate forecasts without additional assumptions regarding regressors. With application of tendency survey data...

The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and...

In this article we present four diversified approaches to forecasting main macroeconomic variables without a priori assumptions concerning causality. We include tendency survey data in both the Bayesian averaging of classical estimates (BACE) and the dynamic factor models (DFM) frameworks. With...

Economic agents are aware to incur in a loss basing their decisions on their own extrapolations instead of sound statistical data, but the loss could be smaller than the one related to waiting for the dissemination of final data. A broad guidance in deciding when statistical offices should...

Parameter shrinkage applied optimally can always reduce error and projection variances from those of maximum likelihood estimation. Many variables that actuaries use are on numerical scales, like age or year, which require parameters at each point. Rather than shrinking these towards zero,...