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Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

Recent studies have examined tax policy issues using labour supply models characterised by a discretised budget set. Microsimulation modelling using a discrete hours approach is probabilistic. This makes analysis of the distribution of income difficult as even for a small sample with a modest range of labour supply points the range of possible labour supply combinations over the sample is extremely large. This paper proposes a method of approximating measures of income distribution and compares the performance of this method to alternative approaches in a microsimulation context. In this approach a pseudo income distribution is constructed, which uses the probability of a particular labour supply value occurring (standardised by the population size) to refer to a particular position in the pseudo income distribution. This approach is compared to using an expected income level for each individual and to a simulated approach, in which labour supply values are drawn from each individual’s hours distribution and summary statistics of the distribution of income are calculated by taking the average over each set of draws. The paper shows that the outcomes of various distributional measures using the pseudo method converge quickly to their true values as the sample size increases. The expected income approach results in a less accurate approximation. To illustrate the method, we simulate the distributional implications of a tax reform using the Melbourne Institute Tax and Transfer Simulator.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

Purpose: The paper analyzes the variability of labor incomes in Argentina from mid-1980s to 2005. The magnitude of income instability and its determinants are evaluated under different macroeconomic contexts. It also analyzes how income fluctuations have influenced income distribution. Finally, the income convergence hypothesis is explored.

Methodology/approach: Different quantitative procedures are employed to measure mobility from dynamic information coming from the regular household survey. Four periods are distinguished that are relatively homogeneous. Dynamic pseudo-panels are also considered.

Findings: The growth in occupational instability registered since the mid-1990s led to a high variability of incomes despite the macroeconomic stability enjoyed throughout the nineties. Moreover, the panorama of growing inequality in the distribution of monthly income (the usual measure employed in Argentina) is also appropriate to describe what happened with the changes in the distribution of more permanent incomes. Finally, long-term income mobility in Argentina is scarce, indicating that the income path does not converge to the general mean.

Research limitations/implications (if applicable): Data refer only to Greater Buenos Aires since microdata are not available for the other areas covered by survey for the entire period under analysis. However, results are reasonably representative of the whole urban areas of the country.

Originality/value of paper: This research identifies the relative importance of labor market and macroeconomic factors in explaining income mobility. Moreover, it is for the first time in Argentina that dynamic information coming from panel data and pseudo-panels are analyzed together.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

Statistical methods of spatial analysis are often successful at either prediction or explanation, but not necessarily both. In a recent paper, Dearmon and Smith (2016) showed that by combining Gaussian Process Regression (GPR) with Bayesian Model Averaging (BMA), a modeling framework could be developed in which both needs are addressed. In particular, the smoothness properties of GPR together with the robustness of BMA allow local spatial analyses of individual variable effects that yield remarkably stable results. However, this GPR-BMA approach is not without its limitations. In particular, the standard (isotropic) covariance kernel of GPR treats all explanatory variables in a symmetric way that limits the analysis of their individual effects. Here we extend this approach by introducing a mixture of kernels (both isotropic and anisotropic) which allow different length scales for each variable. To do so in a computationally efficient manner, we also explore a number of Bayes-factor approximations that avoid the need for costly reversible-jump Monte Carlo methods.

To demonstrate the effectiveness of this Variable Length Scale (VLS) model in terms of both predictions and local marginal analyses, we employ selected simulations to compare VLS with Geographically Weighted Regression (GWR), which is currently the most popular method for such spatial modeling. In addition, we employ the classical Boston Housing data to compare VLS not only with GWR but also with other well-known spatial regression models that have been applied to this same data. Our main results are to show that VLS not only compares favorably with spatial regression at the aggregate level but is also far more accurate than GWR at the local level.