Modeling the spatial dynamics of economic models

The advances that have been characterizing spatial econometrics in recent years are mostly theoretical and have not found an extensive empirical application yet. In this work we aim at supplying a review of the main tools of spatial econometrics and to show an empirical application for one of the most recently introduced estimators. Despite the numerous alternatives that the econometric theory provides for the treatment of spatial (and spatiotemporal) data, empirical analyses are still limited by the lack of availability of the correspondent routines in statistical and econometric software. Spatiotemporal modeling represents one of the most recent developments in spatial econometric theory and the finite sample properties of the estimators that have been proposed are currently being tested in the literature. We provide a comparison between some estimators (a quasi-maximum likelihood, QML, estimator and some GMM-type estimators) for a fixed effects dynamic panel data model under certain conditions, by means of a Monte Carlo simulation analysis. We focus on different settings, which are characterized either by fully stable or quasi-unit root series. We also investigate the extent of the bias that is caused by a non-spatial estimation of a model when the data are characterized by different degrees of spatial dependence. Finally, we provide an empirical application of a QML estimator for a time-space dynamic model which includes a temporal, a spatial and a spatiotemporal lag of the dependent variable. This is done by choosing a relevant and prolific field of analysis, in which spatial econometrics has only found limited space so far, in order to explore the value-added of considering the spatial dimension of the data. In particular, we study the determinants of cropland value in Midwestern U.S.A. in the years 1971-2009, by taking the present value model (PVM) as the theoretical framework of analysis.

Essays in Macroecometrics: methodological aspects and empirical applications

In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

Genetic basis of variation for root traits and response to heat stress in durum wheat

Durum wheat is the second most important wheat species worldwide and the most important crop in several Mediterranean countries including Italy. Durum wheat is primarily grown under rainfed conditions where episodes of drought and heat stress are major factors limiting grain yield. The research presented in this thesis aimed at the identification of traits and genes that underlie root system architecture (RSA) and tolerance to heat stress in durum wheat, in order to eventually contribute to the genetic improvement of this species. In the first two experiments we aimed at the identification of QTLs for root trait architecture at the seedling level by studying a bi-parental population of 176 recombinant inbred lines (from the cross Meridiano x Claudio) and a collection of 183 durum elite accessions. Forty-eight novel QTLs for RSA traits were identified in each of the two experiments, by means of linkage- and association mapping-based QTL analysis, respectively. Important QTLs controlling the angle of root growth in the seedling were identified. In a third experiment, we investigated the phenotypic variation of root anatomical traits by means of microscope-based analysis of root cross sections in 10 elite durum cultivars. The results showed the presence of sizeable genetic variation in aerenchyma-related traits, prompting for additional studies aimed at mapping the QTLs governing such variation and to test the role of aerenchyma in the adaptive response to abiotic stresses. In the fourth experiment, an association mapping experiment for cell membrane stability at the seedling stage (as a proxy trait for heat tolerance) was carried out by means of association mapping. A total of 34 QTLs (including five major ones), were detected. Our study provides information on QTLs for root architecture and heat tolerance which could potentially be considered in durum wheat breeding programs.