Antonio E. Noriega

A time-series approach to test a change in inflation persistence: the Mexican experience

When a central bank commits credibly to a nonaccommodative monetary policy, observed inflation should be a stationary process. In countries where, for a variety of reasons, the determinants of inflation could lead it to follow a nonstationary process, the adoption of a credible disinflationary programme should therefore induce a fundamental change in the stochastic process governing inflation and, in particular, should diminish its persistence. This article studies the time-series properties of both inflation and core inflation during the 1995–2006 period for the Mexican economy, using recently developed techniques to detect a change in the persistence of economic time series. Consistently with the adoption of an inflation-targeting framework, the results suggest that inflation in Mexico seems to have indeed switched from a nonstationary to a stationary process around the end of year 2000 or the beginning of 2001.

Spurious regression under broken trend stationarity

We study the phenomenon of spurious regression between two random variables, when the generating mechanism of individual series is assumed to follow a stationary process around a trend with (possibly) multiple breaks in the level and slope of trend. We develop the relevant asymptotic theory and show that the phenomenon of spurious regression occurs independently of the structure assumed for the errors. In contrast to previous findings, the presence of a spurious relationship will be less severe when breaks are present in the generating mechanism of individual series. This is true whether the regression model includes a linear trend or not. Simulations confirm our asymptotic results, and reveal that in finite samples, the phenomenon of spurious regression is sensitive to the presence of a linear trend in the regression model, and to the relative location of breaks within the sample.

Stationarity and structural breaks -- evidence from classical and Bayesian approaches

Abstract The purpose of this paper is to analyze and compare the results of applying classical and Bayesian methods to testing for a unit root in time series with a single endogenous structural break. We utilize a data set of macroeconomic time series for the Mexican economy similar to the Nelson–Plosser one. Under both approaches, we make use of innovational outlier models allowing for an unknown break in the trend function. Classical inference relies on bootstrapped critical values, in order to make inference comparable to the finite sample Bayesian one. Results from both approaches are discussed and compared.

Changes in persistence, spurious regressions and the Fisher hypothesis

Abstract Declining inflation persistence has been documented in numerous studies. We show that when time series with changes in persistence are analyzed in a regression framework with other persistent time series like interest rates, spurious regressions are likely to occur. We propose the coefficient of determination R2 as a simple test statistic to distinguish between spurious and genuine regressions in situations where time series possibly exhibit changes in persistence. We extend the analysis towards fractional (co-)integration as well. To this end, we establish the limit theory for the R2 statistic and conduct a Monte Carlo study where we investigate its finite-sample properties. The test performs remarkably well in terms of size and power and is robust to level shifts and multiple changes in persistence. Finally, we apply the test to the Fisher equation for the United States. The newly proposed R2-based test offers robust evidence favourable to the Fisher hypothesis.