Francesca Di Iorio

Testing for Breaks in Cointegrated Panels - with an Application to the Feldstein-Horioka Puzzle

Stability tests for cointegrating coefficients are known to have very low power with small to medium sample sizes. In this paper we propose to solve this problem by extending the tests to dependent cointegrated panels through the stationary bootstrap. Simulation evidence shows that the proposed panel tests improve considerably on asymptotic tests applied to individual series. As an empirical illustration we examined investment and saving for a panel of 14 European countries over the 1960-2002 period. While the individual stability tests, contrary to expectations and graphical evidence, in almost all cases do not reject the null of stability, the bootstrap panel tests lead to the more plausible conclusion that the long-run relationship between these two variables is likely to have undergone a break.

Change point analysis of imprecise time series

Abstract In this paper we describe how to conduct a change-point analysis when dealing with time series imprecisely or vaguely observed, i.e. time ordered observations whose values are not known exactly, such as interval or ordinal time series (imprecise time series). In order to treat such time series, we propose to employ a fuzzy approach i.e. data are parameterized in the form of fuzzy variables. Then, to detect the number and location of change points we employ a deviation measure for fuzzy variables in the framework of Atheoretical Regression Trees (ART). We present simulation results pertaining to the behavior of the proposed approach as well as two empirical applications to real imprecise time series.

A Note on the Estimation of Long-Run Relationships in Panel Equations with Cross-Section Linkages

We address the issue of estimation and inference in dependent non-stationary panels of small cross-section dimensions. The main conclusion is that the best results are obtained applying bootstrap inference to single-equation estimators, such as FM-OLS and DOLS. SUR estimators perform badly, or are even unfeasible, when the time dimension is not very large compared to the cross-section dimension.

Latitudinal variability of the dynamic linkage between temperature and atmospheric carbon dioxide concentrations

In this paper, a novel data-driven approach is used to investigate the presence of spatial differences in the dynamic linkage between temperature and atmospheric carbon dioxide concentrations. This linkage seems to be latitude dependent. The main findings of the study are as follows. In the latitude belts surrounding the equator (0°− 24° N and 0°− 24° S), the link seems very similar. On the opposite, the patterns of the temperature CO2 link in the Arctic is very distant from those concerning the equatorial regions and other latitude bands in the South Hemisphere. This big distance is consistent with the so-called Arctic amplification phenomenon. Further, it is important to underline that this observational data-based analysis provides an independent statistical confirmation of the results from global circulation modelling.

Can you do the wrong thing and still be right? Hypothesis testing in I(2) and near-I(2) cointegrated VARs

ABSTRACT In this paper, we investigate the small-sample performance of LR tests on long-run coefficients in the I(2) model; we focus on a comparison between I(2) and near-I(2) data, i.e. I(1) data with a second root very close to unity, and report the results of some Monte Carlo experiments. With near-I(2) data, the finite-sample properties of the tests are (i) similar to those found with genuine I(2) data, (ii) systematically superior to those of the analogous tests constructed in the I(1) model, even if the latter is, in principle, correctly specified and the former is not. Therefore, there seems to be strong support to the idea that, in practice, modelling near-I(2) data using the I(2) model may be a good idea, despite the inherent misspecification.

Theoretical Regression Trees: A Tool for Multiple Structural-Change Models Analysis

The analysis of structural-change models is nowadays a popular subject of research both in econometric and statistical literature. The most challenging task is to identify multiple breaks occurring at unknown dates. In case of multiple shifts in mean Cappelli and Reale (Provasi, C. (eds.) S.Co. 2005: Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, pp. 479–484. Cleup, Padova, 2005) have proposed a method called ART that employs regression trees to estimate the number and location of breaks. In this paper we focus on regime changes due to breaks in the coefficients of a parametric model and we propose an extension of ART that addresses this topic in the general framework of the linear model with multiple structural changes. The proposed approach considers in the tree growing phase the residuals of parametric models fitted to contiguous subseries obtained by splitting the original series whereas tree pruning together with model selection criteria provides the number of breaks. We present simulation results well as two empirical applications pertaining to the behavior of the proposed approach.

Maximum Likelihood Estimation of Input Demand Models with Fixed Costs of Adjustment

Traditional models of input demand rely upon convex and symmetric adjustment costs. However, the fortune of this highly restrictive approach is due more to analytical convenience than to empirical relevance. In this note we examine the model under more realistic hypothesis of fixed costs, show that it can be cast in the form of a Double Censored Random Effect Tobit Model, derive its likelihood function, and finally evaluate the performance of the ML estimators through a Monte Carlo experiment. The performances, although strongly dependent on the degree of censoring, appear to be promising.