Reconsidering the evidence: Are Eurozone business cycles converging

This paper, using 40 years of monthly industrial production data, examines the relationship between the business cycles of the 12 Eurozone countries. Since estimates of the business cycle have been found to be sensitive to how the cycle is measured, a range of alternative measures are considered. We focus on both parametric and nonparametric univariate measures of the ‘classical’ and ‘growth’ cycles. We then investigate whether Eurozone business cycles have converged. This is based on an analysis of the distribution of bivariate correlation coefficients between the 12 countries’ business cycles. This extends previous work that has tested for convergence, in a similar manner by focusing on correlation, but has not considered the entire distribution, instead focusing on the mean correlation coefficient or particular bivariate correlation coefficients. Although empirical inference about individual Eurozone business cycles is found to be sensitive to the measure of the business cycle considered, our measure of convergence between the Eurozone business cycles exhibits common features across the alternative measures of the business cycle. Interestingly, we find that there have been periods of convergence, identified by the distribution tending to unity, and periods of divergence. Although further data are required to corroborate the story, there is evidence to suggest that the Euro-zone has entered a period of convergence after the clear period of divergence in the early 1990s in the aftermath of German unification and at the time of the currency crises in Europe. This is encouraging for the successful operation of a common monetary policy in the Eurozone. --

Least squares estimation in nonlinear cohort panels with learning from experience

We discuss techniques of estimation and inference for nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator employed in the seminal paper by Malmendier and Nagel (2016). Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a panel of survey expectations demonstrates the usefulness of the theory developed

The modified conditional sum-of-squares estimator for fractionally integrated models

In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA ($p_1$,$d$,$p_2$) model. We derive expressions for the estimator's bias and show that the leading term can be easily removed by a simple modification of the CSS objective function. We call this new estimator the modified conditional sum-of-squares (MCSS) estimator. We show theoretically and by means of Monte Carlo simulations that its performance relative to that of the CSS estimator is markedly improved even for small sample sizes. Finally, we revisit three classical short datasets that have in the past been described by ARFIMA($p_1$,$d$,$p_2$) models with constant term, namely the post-second World War real GNP data, the extended Nelson-Plosser data, and the Nile data

Estimating Structural Parameters in Regression Models with Adaptive Learning

This paper investigates the asymptotic properties of the ordinary least squares (OLS) estimator of structural parameters in a stylised macroeconomic model in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous variable. In particular, when the learning recursion is subject to so-called decreasing gain sequences the model does not satisfy, in general, any of the sufficient conditions for consistent estimability available in the literature. The paper demonstrates that, for appropriate parameter sets, the OLS estimator nevertheless remains strongly consistent and asymptotically normally distributed

Co-breaking : representation, estimation and testing

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Least squares estimation in nonlinear cohort panels with learning from experience

We discuss techniques of estimation and inference for nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator employed in the seminal paper by Malmendier and Nagel (2016). Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a panel of survey expectations demonstrates the usefulness of the theory developed