Vasco J. Gabriel

The Consumption-Wealth Ratio Under Asymmetric Adjustment ⁄

This paper argues that nonlinear adjustment may provide a better explanation of fluctuations in the consumption-wealth ratio. The nonlinearity is captured by a Markov-switching vector error-correction model that allows the dynamics of the relationship to differ across regimes. Estimation of the system suggests that these states are related to the behaviour of financial markets. In fact, estimation of the system suggests that short-term deviations in the consumption-wealth ratio will forecast either asset returns or consumption growth; the first when changes in wealth are transitory, the second when changes in wealth are permanent. Our approach uncovers a richer and more complex dynamic in the consumption-wealth ratio than previous results in the literature, whilst being in accordance with theoretical predictions of a simple model of consumption under uncertainty.

A simple method of testing for cointegration subject to multiple regime changes

Abstract In this paper we propose a simple method of testing for cointegration in models that allow for multiple shifts in the long-run relationship. The procedure consists of carrying out conventional residual-based tests with standardized residuals from an appropriate Markov switching model. Our Monte Carlo results show that standard tests work well, even though their asymptotic validity can be questioned because they are not based on least-squares residuals. An empirical application to the present-value model of stock prices is also discussed.

An Efficient test of Fiscal Sustainability

We suggest using the efficient test with prespecified cointegration vectors of Horvath and Watson (1995) for the ‘strong’ fiscal sustainability hypothesis. Unlike this procedure, conventional methodologies tend to penalize the sustainability hypothesis.

Instability in cointegration regressions: a brief review with an application to money demand in Portugal

This study addresses some modelling questions related to the possibility of structural change in models with nonstationary variables. Focusing on cointegration issues, some methodological aspects are discussed, attempting to integrate coherently the several steps of the modelling strategy. These range from unit root to cointegration testing and to testing for instability in the cointegration vector. An empirical example with Portuguese data tries to illustrate the usefulness of this approach, where a simple money demand function is estimated using an error-correction model (ECM). If a break is explicitly allowed in the cointegration vector the forecasting performance of the ECM improves.

An Estimated DSGE Open Economy Model of the Indian Economy with Financial Frictions

We develop an open economy DSGE model of the Indian economy and estimate it by Bayesian Maximum Likelihood methods. We build up in stages to a model with a number of features important for emerging economies in general and the Indian economy in particular: a large proportion of credit-constrained consumers, a financial accelerator facing domestic firms seeking to finance their investment, “liability dollarization” and incomplete exchange rate pass-through. Our estimation results support the inclusion of financial frictions in an otherwise standard small open economy model. The simulation properties of the estimated model are examined under a generalized inflation targeting Taylor-type interest rate rule with forward- and backward-looking components.

Is there really a gap between aggregate productivity and technology

The important contribution by Basu and Fernald (2002) shows that, in practice, there is a statistically significant gap between aggregate productivity and technology that can be attributed to inefficient product and labour markets. This is important, as it implies that the Solow residual is an imperfect index for aggregate technology change. We take a related approach and find that when we control for capacity utilization, time varying markup and account for externalities between industries, by employing a superior system estimator, the gap between the aggregate productivity and technology is shown to narrow considerably.

Cointegration and the joint confirmation hypothesis

Recent papers by Charemza and Syczewska (1998) and Carrion, Sanso and Ortuno (2001) focused on the joint use of unit root and stationarity tests. In this paper, the discussion is extended to the case of cointegration. Critical values for testing the joint confirmation hypothesis of no cointegration are computed and a small Monte Carlo experiment evaluates the relative performance of this procedure.

Time-varying cointegration, identification, and cointegration spaces

Abstract We derive the conditions under which time-varying cointegration leads to cointegration spaces that may be time-invariant or, in contrast, time-varying. The model of interest is a vector error correction model with arbitrary time-varying cointegration parameters. We clarify the role of identification and normalization restrictions and show that structural breaks in error-correction models may actually correspond to stable long-run economic relationships, as opposed to a single-equation setup, in which an identification restriction is imposed. Moreover, we show that, in a time-varying cointegrating relationship with a given number of variables and cointegration rank, there is a minimum number of orthogonal Fourier functions that most likely guarantees time-varying cointegrating spaces.

Tests for the Null Hypothesis of Cointegration: a Monte Carlo Comparison

Abstract The aim of this paper is to compare the relative performance of several tests for the null hypothesis of cointegration, in terms of size and power in finite samples. This is carried out using Monte Carlo simulations for a range of plausible data‐generating processes. We also analyze the impact on size and power of choosing different procedures to estimate the long run variance of the errors. We found that the parametrically adjusted test of McCabe et al. (1997) is the most well‐balanced test, displaying good power and relatively few size distortions.

Linear instrumental variables model averaging estimation

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favorably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education.