Structural Breaks in the Real Exchange Rate and Real Interest Rate Relationship

In this paper we empirically examine the relationship between the real exchange rate and real interest rate differentials using recent econometric methods robust to potential structural breaks. Generally, our study provides evidence of this relationship in the long-run context. More specifically, we first focus on the UK-US relationship, and interestingly find limited evidence of this long-run relationship using traditional methods. But when an approach robust to endogenously determined structural breaks is employed, we find evidence that the real interest rate differential is an important determinant of the real exchange rate. Secondly, in order to investigate the relevance of structural shifts in a more global context, we carry out multiple country analysis. While providing evidence of this long-run relationship, European data suggest that the presence of structural breaks is not very common across countries and is indeed country-specific.

A New Test of the Real Interest Rate Parity Hypothesis: Bounds Approach and Structural Breaks

We test the real interest rate parity hypothesis using data for the G7 countries over the period 1970-2008. Our contribution is two-fold. First, we utilize the ARDL bounds approach of Pesaran et al. (2001) which allows us to overcome uncertainty about the order of integration of real interest rates. Second, we test for structural breaks in the underlying relationship using the multiple structural breaks test of Bai and Perron (1998, 2003). Our results indicate significant parameter instability and suggest that, despite the advances in economic and financial integration, real interest rate parity has not fully recovered from a breakdown in the 1980s.

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.

The Difference, System and ‘Double-D’ GMM Panel Estimators in the Presence of Structural Breaks

The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.

Competition for FDI and profit shifting: On the effects of subsidies and tax breaks

We investigate competition for FDI within a region when a foreign multinational rm can profitably exploit differences in statutory corporate tax rates by shifting taxable pro ts to lower-tax jurisdictions. In such framework we show that targeted tax competition may lead to higher welfare for the region as a whole than lump-sum subsidies when the difference in statutory corporate tax rates and/or their average is high enough. Tax competition is also preferable from an efficiency point of view (overall surplus) by changing the firm's investment decision when pro t shifting motivations induce the rm to locate in the (before tax) least pro table country.

Arch and Structural Breaks in United States Inflation

United States Phillips curves are routinely estimated without accounting for the shifts in mean inflation. As a result we may expect the standard estimates of Phillips curves to be biased and suffer from ARCH. We demonstrate this is indeed the case. We also demonstrate that once the shifts in mean inflation are accounted for the ARCH is largely eliminated in the estimated model and the model defining expected rate of inflation in the New Keynesian model plays no significant role in the dynamics of inflation.

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

This paper uses an infinite hidden Markov model (IIHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and attractive alternative to existing methods. We found a clear structural break during the recent financial crisis. Prior to that, inflation persistence was high and fairly constant.

Equilibrium Exchange Rate Determination and Multiple Structural Changes

The large appreciation and depreciation of the US dollar in the 1980s stimulated an important debate on the usefulness of unit root tests in the presence of structural breaks. In this paper, we propose a simple model to describe the evolution of the real exchange rate. We then propose a more general smooth transition (STR) function than has hitherto been employed, which is able to capture structural changes along the (long-run) equilibrium path, and show that this is consistent with our economic model. Our framework allows for a gradual adjustment between regimes and allows for under- and/or over-valued exchange rate adjustments. Using monthly and quarterly data for up to twenty OECD countries, we apply our methodology to investigate the univariate time series properties of CPI-based real exchange rates with both the U.S. dollar and German mark as the numeraire currencies. The empirical results show that, for more than half of the quarterly series, the evidence in favour of the stationarity of the real exchange rate was clearer in the sub-sample period post-1980.

The Changing Inter-Industry Wage Structure of the Organised Manufacturing Sector in India, 1973-74 to 2003-04

This study examines the inter-industry wage structure of the organised manufacturing sector in India for the period 1973-74 to 2003-04 by estimating the growth of average real wages for production workers by industry. In order to estimate the growth rates, the study adopts a methodological framework that differs from other studies in that the time series properties of the concerned variables are closely considered in order to obtain meaningful estimates of growth that are unbiased and (asymptotically) efficient. Using wage data on 51 manufacturing industries at three digit level of the National Industrial Classification 1998 (India), our estimation procedure obtains estimates of growth of real wages per worker that are deterministic in nature by accounting for any potential structural break(s). Our findings show that the inter-industry wage structure in India has changed a lot in the period 1973-74 to 2003-04 and that it provides some evidence that the inter-industry wage differences have become more pronounced in the post-reforms period. Thus this paper provides new evidence from India on the need to consider the hypothesis that industry affiliation is potentially an important determinant of wages when studying any relationship between reforms and wages.

Growth Rate Estimation in the presence of Unit Roots

This study addresses the issue of the presence of a unit root on the growth rate estimation by the least-squares approach. We argue that when the log of a variable contains a unit root, i.e., it is not stationary then the growth rate estimate from the log-linear trend model is not a valid representation of the actual growth of the series. In fact, under such a situation, we show that the growth of the series is the cumulative impact of a stochastic process. As such the growth estimate from such a model is just a spurious representation of the actual growth of the series, which we refer to as a “pseudo growth rate”. Hence such an estimate should be interpreted with caution. On the other hand, we highlight that the statistical representation of a series as containing a unit root is not easy to separate from an alternative description which represents the series as fundamentally deterministic (no unit root) but containing a structural break. In search of a way around this, our study presents a survey of both the theoretical and empirical literature on unit root tests that takes into account possible structural breaks. We show that when a series is trendstationary with breaks, it is possible to use the log-linear trend model to obtain well defined estimates of growth rates for sub-periods which are valid representations of the actual growth of the series. Finally, to highlight the above issues, we carry out an empirical application whereby we estimate meaningful growth rates of real wages per worker for 51 industries from the organised manufacturing sector in India for the period 1973-2003, which are not only unbiased but also asymptotically efficient. We use these growth rate estimates to highlight the evolving inter-industry wage structure in India.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Regime-Switching Cointegration

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of cointegrating relationships to change when the regime changes. We show how Bayesian model averaging or model selection methods can be used to deal with the high-dimensional model space that results. Our methods are used in an empirical study of the Fisher effect.

Estimating United States Phillips Curves With Expectations Consistent With The Statistical Process Of Inflation

‘Modern’ Phillips curve theories predict inflation is an integrated, or near integrated, process. However, inflation appears bounded above and below in developed economies and so cannot be ‘truly’ integrated and more likely stationary around a shifting mean. If agents believe inflation is integrated as in the ‘modern’ theories then they are making systematic errors concerning the statistical process of inflation. An alternative theory of the Phillips curve is developed that is consistent with the ‘true’ statistical process of inflation. It is demonstrated that United States inflation data is consistent with the alternative theory but not with the existing ‘modern’ theories.

Regime-Switching Cointegration

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of cointegrating relationships to change when the regime changes. We show how Bayesian model averaging or model selection methods can be used to deal with the high-dimensional model space that results. Our methods are used in an empirical study of the Fisher e ffect.