Chiara Osbat

Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data

We show how the use of panel data methods such as those proposed in single equations by Kao (1999) and Pedroni (1999) or in systems by Larsson and Lyhagen (1999) to investigate economic hypotheses such as purchasing power parity or the term structure of interest rates may be affected by the existence of cross-unit cointegrating relations. The existing literature assumes that such relations, that tie the units of the panel together, are not present. Using empirical examples from a panel of OECD countries we show that this assumption is very likely to be violated. Simulations of the properties of panel cointegration tests in the presence of cross-unit relations are then presented to demonstrate the serious cost of assuming away such relations. Some fixes are proposed as a way of dealing with these more general scenarios.

Testing for PPP: Should we use panel methods?

A common finding in the empirical literature on the validity of purchasing power parity (PPP) is that it holds when tested for in panel data, but not in univariate (i.e. country specific) analysis. The usual explanation for this mis-match is that panel tests for unit roots and cointegration are more powerful than their univariate counterparts. In this paper we suggest an alternative ex-planation for the mismatch. More generally, we warn against the use of panel methods for testing for unit roots in macroeconomic time series. Existing panel methods assume that cross-unit cointegrating or long-run relationships, that tie the units of the panel together, are not present. However, using empirical examples on PPP for a panel of OECD countries, we show that this assumption is very likely to be violated. Simulations of the properties of panel unit root tests in the presence of long-run cross-unit relationships are then presented to demonstrate the serious cost of assuming away such relationships. The empirical size of the tests is substantially higher than the nominal level, so that the null hypothesis of a unit root is rejected very often, even if correct.

Determinants of the Euro Real Effective Exchange Rate: A BEER/PEER Approach

This paper presents an empirical analysis of the medium-term determinants of the euro effective exchange rate. The empirical analysis builds on synthetic quarterly data from 1975 to 1998, and derives a Behavioural Equilibrium Exchange Rate (BEER) and a Permanent Equilibrium Exchange Rate (PEER). Four different model specifications are retained, due to the difficulties encountered in specifying an encompassing model. Results indicate that differentials in real interest rates and productivity, and (in some specifications) the relative fiscal stance and the real price of oil, have a significant influence on the euro effective exchange rate. Assessing the existence and the extent of the over- or undervaluation of the exchange rate is not straightforward, since these different specifications often lead to contrasting findings. However, all four models point unambiguously to the undervaluation of the euro in 2000, although the extent of this undervaluation largely depends on the specification chosen.

Productivity and the Euro-Dollar exchange rate

This article analyses the impact of productivity developments in the United States and the euro area on the euro-dollar exchange rate. The article presents a new measure of relative average labour, productivity (ALP) which does not suffer from the biases implicit in readily available relative ALP data. Importantly, the patterns of these series differ widely. Employing the Johansen cointegration framework, four models are estimated using four different productivity proxies. Our results indicate that the extent to which productivity can explain the euro depreciation varies with the productivity proxy used: readily available measures explain most, our new, preferred measure least. In all models, however, productivity can explain only a fraction of the actual euro depreciation experienced in 1999–2000. JEL no. F31, C32, O47

Low inflation in the euro area: Causes and consequences

Euro area inflation remained unexpectedly weak between 2012 and 2016. This was surprising as the region’s ongoing economic recovery was expected to stimulate inflation. Persistently low inflation begs a great many questions: What are the underlying factors? Are the economic models used to predict inflation fit for purpose? Do falling inflation expectations point to a real “de-anchoring” from the inflation target? How can monetary policy combat weak inflation and its related risks? Should monetary policy be supported by other policy domains? The Eurosystem has introduced a whole range of unconventional measures, including an asset purchase programme, with the aim of getting inflation back to its target of below, but close to 2%. To gain a full and comprehensive understanding of the complex issues underlying low inflation, it also set up the Low Inflation Task Force (LIFT) with a brief to investigate the above questions. The article discusses its main findings (see Ciccarelli and Osbat, 2017), which in short suggest that: Inflation in the euro area has been held back chiefly by cyclical forces. More specifically, domestic shocks caused inflation to fall in the 2012-14 period, following which foreign shocks gained importance, e.g. the prolonged decline in oil prices. Traditional models – and more specifically the Phillips curve, which captures the relationship between domestic economic activity and inflation – continue to be relevant for understanding and predicting inflation dynamics. There was a real threat of inflation expectations becoming de-anchored, and the asset purchase programme proved justified. De-anchoring may result from declining confidence in both the effectiveness of monetary policy and the inflation target. The asset purchase programme has proved successful as it has supported inflation, fostering its return to target in good time, and it has also contributed to re-anchoring inflation expectations. Monetary policy can benefit from positive interactions with measures in other policy domains – e.g. structural reforms and budgetary policies – and particularly so in an environment where policy rates are close to their lower bound.