Tommaso Proietti

Dating the Euro Area Business Cycle

In this Paper we compare alternative approaches for dating the euro area business cycle and analysing its characteristics. First, we extend a commonly used dating procedure to allow for length, size and amplitude restrictions, and to compute the probability of a phase change. Second, we apply the modified algorithm for dating both the classical euro area cycle and the deviation cycle, where the latter is obtained by a variety of methods, including a modified HP filter that reproduces the features of the BK filter but avoids end-point problems, and a production function based approach. Third, we repeat the dating exercise for the main euro area countries, evaluate the degree of synchronization, and compare the results with the UK and the US. Fourth, we construct indices of business cycle diffusion, and assess how widespread are cyclical movements throughout the economy. Finally we repeat the dating exercise using monthly industrial production data, to evaluate whether the higher sampling frequency can compensate the higher variability of the series and produce a more accurate dating.

Short-Run Dynamics in Cointegrated Systems

In this paper the author builds a unifying framework under which the time-domain properties of the permanent-transitory decompositions available in the literature are investigated. Starting from the state space representation of a cointegrated system expressions are derived for the (common) trends and cycles of the Beveridge-Nelson decomposition involving quantities already available from the interim multiplier representation. The cycles result from both movements along the attractor and adjustment dynamics; the latter are shown to be the transitory component of the Gonzalo-Granger decomposition. The two decompositions are equivalent when the number of common cycles and trends add up to the dimension of the system. Algorithms for the extraction of the components are given and the results are illustrated with respect to a trivariate system consisting of US per capita GNP, Private Consumption and Investment. Copyright 1997 by Blackwell Publishing Ltd

Characterising the Business Cycle for Accession Countries

We analyse the evolution of the business cycle in the accession countries, after a careful examination of the seasonal properties of the available series and the required modification of the cycle dating procedures. We then focus on the degree of cyclical concordance within the group of accession countries, which turns out to be in general lower than that between the existing EU countries (the Baltic countries constitute an exception). With respect to the Eurozone, the indications of synchronization are also generally low and lower relative to the position obtaining for countries taking part in previous enlargements (with the exceptions of Poland, Slovenia and Hungary). In the light of the optimal currency area literature, these results cast doubts on the usefulness of adopting the euro in the near future for most accession countries, though other criteria such as the extent of trade and the gains in credibility may point in a different direction.

Forecasting the US unemployment rate

The primary interest is in out-of-sample forecasting of the US monthly unemployment rate. Several linear unobserved components models are fitted and their comparative forecasting accuracy is assessed by means of an extensive rolling-origin procedure using a test period that covers the last two decades. An attempt is made to link forecasting performance to the time domain properties of the models and the evidence is that highly persistent models perform better. Deletion diagnostics and normality tests, along with documenting possible departures from linearity and Gaussianity attributable to business cycle and turning point asymmetries, foster the conclusion that these are mostly concentrated in the pre-forecast period (1948-1980). A search is made for plausible nonlinear extensions capable of accounting for dynamic asymmetries in unemployment rates, leading to the specification of a cyclical trend model with smooth transition in the underlying parameters that improves forecast accuracy at short lead times and at the end of the sample period; as expected, though significant, the gains are not exceptionally large. The generalised impulse response function casts some light on the interpretation of the results. In particular, the main evidence is that persistence is not a stable feature over the business cycle.

Temporal Disaggregation By State Space Methods: Dynamic Regression Methods Revisited

The paper advocates the use of state space methods to deal with the problem of temporal disaggregation by dynamic regression models, which encompass the most popular techniques for the distribution of economic flow variables, such as Chow-Lin, Fernandez and Litterman. The state space methodology offers the generality that is required to address a variety of inferential issues that have not been dealt with previously. The paper contributes to the available literature in three ways: (i) it concentrates on the exact initialization of the different models, showing that this issue is of fundamental importance for the properties of the maximum likelihood estimates and for deriving encompassing autoregressive distributed lag models that nest exactly the traditional disaggregation models; (ii) it points out the role of diagnostics and revisions histories in judging the quality of the disaggregated estimates and (iii) it provides a thorough treatment of the Litterman model, explaining the difficulties commonly encountered in practice when estimating this model.

Trend–Cycle Decompositions with Correlated Components

This paper raises some interpretative issues that arise from univariate trend–cycle decompositions with correlated disturbances. In particular, it discusses whether the interpretation of a negative correlation as providing evidence for the prominence of real, or supply, shocks, can be supported. For this purpose it determines the conditions under which correlated components may originate from the underestimation of the cyclical component in an orthogonal decomposition; from the presence of a growth rate cycle, rather than a deviation cycle; or alternatively, as a consequence of the hysteresis phenomenon. Finally, it considers interpreting correlated components in terms of permanent–transitory decompositions, where the permanent component has richer dynamics than a pure random walk. The consequences for smoothing and signal extraction are discussed: in particular, it is documented that a negative correlation implies that future observations carry most of the information needed to assess cyclical stance. As a result, the components will be subject to underestimation in real time and thus to high revisions. The overall conclusion is that the characterization of economic fluctuations in macroeconomic time series largely remains an open issue.

Comparing seasonal components for structural time series models

Abstract This paper discusses several encompassing representations for linear seasonal models in the structural framework. Their time and frequency domain properties are ascertained in a unifying framework, casting particular attention on the notion of ‘smoothness’ of the seasonal component. The shape of the forecast function is compared with that arising from a number of exponential smoothing algorithms. Finally, we investigate whether the specification of the seasonal model is likely to affect the out-of-sample predictive performance of the basic structural model. We conclude that the latter depends upon the features of the time series under investigation, and in particular on the degree of smoothness of the seasonal pattern.

Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models

This paper aims at testing and modeling business-cycle asymmetries within a structural time-series framework, allowing for smooth transition in the parameters characterizing the cyclical component, namely, the damping factor and the frequency. An LM test of linearity is derived, and illustrations are provided with reference to a set of quarterly U.S. industrial production series for two-digit manufacturing industries.

A Beveridge–Nelson smoother

Abstract This note defines a Beveridge–Nelson smoother, that is a two-sided signal extraction filter for trends. The smoother is shown to be the optimal estimator of the trend when the ARIMA model can be decomposed into an uncorrelated random walk trend and stationary cycle components. The conditions under which such a decomposition is possible are discussed.

Convergence in Italian regional per-capita GDP

This paper addresses the issue of convergence of per-capita GDP of the 20 Italian regions. The paper first focuses on the notion of σ-convergence and proposes a new hierarchical clustering algorithm, grouping regions according to the presence of a monotonically decreasing trend in entropy. Then alternative definitions of long-run convergence are given, based on the notion of cointegration and common trends and the evidence arising from application of stationarity tests to the time series of regional contrasts is examined. The conclusion is that both kind of convergence can be used to characterize the dynamics of regional per-capita GDP.