Roberto Leon-Gonzalez

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this article proposes several Time Varying Dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving U.S. inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than many standard benchmarks and shrink toward parsimonious specifications. This article has online supplementary materials.

Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space

A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on the space spanned by the cointegrating vectors (as opposed to a particular identified choice for these vectors). In previous work, such priors have been found to greatly complicate computation. In this article, we develop algorithms to carry out efficient posterior simulation in cointegration models. In particular, we develop a collapsed Gibbs sampling algorithm which can be used with just-identifed models and demonstrate that it has very large computational advantages relative to existing approaches. For over-identifed models, we develop a parameter-augmented Gibbs sampling algorithm and demonstrate that it also has attractive computational properties.

Growth, Convergence and Public Investment. A Bayesian Model Averaging Approach

The aim of this study is twofold. First, the determinants of economic growth are studied among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, various types of private, public and human capital in the group of growth factors are included. Also, it is analysed whether Spanish provinces have converged in economic terms in recent decades. The second objective is to obtain cross-section and panel data parameter estimates that are robust to model specification. For this purpose, a Bayesian Model Averaging (BMA) approach is used. Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Dynamic Probabilities of Restrictions in State Space Models: An Application to the Phillips Curve

Using the Savage–Dickey density ratio and an alternative approach that uses more relaxed assumptions, we develop methods to calculate the probability that a restriction holds at a point in time without assuming that the restriction holds at any other points in time. Both approaches use MCMC output only from the unrestricted model to compute the time-varying posterior probabilities for all models of interest. Using U.S. data, we find the probability that the long-run Phillips curve is vertical to be fairly high, but decreases over time. The probability that the NAIRU is not identified fluctuates over time, but increases after 1990.

How do Migration and Remittances Affect Human Capital Investment? The Effects of Relaxing Information and Liquidity Constraints

Abstract This article explores the heterogeneous effects of the migration–remittance process on the educational attainment of Nepalese children. The results suggest that when controlling for remittances, the children of more educated or informed parents suffer from parental absence, while the children of less informed parents gain from migration, implying that the migration experience helps less educated parents estimate the value of and returns to education more precisely. The results also suggest that remittances help severely credit-constrained households enrol their children in school and prevent dropouts. These remittances help households that face less severe liquidity constraints increase their investment in quality education.

Bayesian inference in a cointegrating panel data model

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.

Invariant Inference and Efficient Computation in the Static Factor Model

Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of the variables and computational inefficiency. This paper develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference on the number of factors that does not depend upon the ordering of the variables, and we provide arguments to explain this invariance. Beginning from identified parameters which have nonstandard forms, we use parameter expansions to obtain a specification with standard conditional posteriors. We show significant gains in computational efficiency. Identifying restrictions that are commonly employed result in interpretable factors or loadings and, using our approach, these can be imposed ex-post. This allows us to investigate several alternative identifying schemes without the need to respecify and resample the model. We apply our methods to a simple example using a macroeconomic dataset.

The assimilation of migrant households in the urban areas of a developing country

This paper explores the economic performance of rural-urban migrant households in the recently flourishing urban areas of Nepal. Using nationally representative primary survey data, we find that upon their arrival, these migrant households have 24 percent less income and 13 percent less consumption than their local counterparts but converge to equal levels of income and consumption after 10 and three years, respectively. Our results suggest that a higher level of education accelerates the speed of assimilation. Compared with the Mountain/Hill regions, migrants in the prosperous Tarai region possess a lower level of welfare upon arrival but exhibit more rapid assimilation.

The Quest for Quality Education:International Remittances and Rural-Urban Migration in Nepal

Abstract Despite a large growth in domestic and international migration and remittances in recent decades, there are limited works that systematically identify and establish interactions between internal and international migration. Using primary data from new urban areas of Nepal, we identify households that had migrated from rural to urban areas, explore their migration practices and educational investment behaviors, and analyze the effects of international migration and remittances on investment in education. The results show that, despite their lower income and consumption, migrant households that have members abroad have higher human capital investment measured by the level and budget share of expenditure on children’s education and the time their children spend for studying at home than do urban-native and other types of migrant households. Our findings suggest that searching for better education is one important motivation for migrating to urban areas among rural households having members abroad.

The impact of spousal bereavement on subjective wellbeing: Evidence from the Taiwanese elderly population

&NA; Bereavement is an inevitable event in our life. This paper employs the Taiwanese panel Survey of Health and Living Status of the Elderly (SHLSE) to evaluate the impact of losing a spouse on self‐assessed health and subjective well‐being measured by depression and life satisfaction. Propensity score matching methods are used to generate a hypothetical bereavement date and a weight for the non‐bereaved to create a comparable non‐bereaved cohort and a difference‐in‐differences (DiD) approach is used to estimate the impact of spousal bereavement. The results show that spousal bereavement increases depression scale by 1.81 points but this increment decreases by 0.43 points every year after bereavement. It takes approximate 4 years to restore to the level prior to bereavement. We also examine the demographic and socioeconomic differences in the spousal bereavement impact and find that the spousal bereavement impact is greater on the bereaved in the higher income group in terms of self‐assessed health and depression. Our results only represent a lower boundary of the possible impact of spousal bereavement on self‐assessed health and subjective wellbeing due to data restrictions.