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This paper proposes an efficient test designed to have power against alternatives where the error correction term follows a Markov switching dynamics. The adjustment to long run equilibrium is different in different regimes characterised by the hidden state Markov chain process. Using a general nonlinear MS-ECM framework, we propose an optimal test for the null of no cointegration against an alternative of a globally stationary MS cointegration. The Monte Carlo studies demonstrate that our proposed tests display superior powers compared to the linear tests. In an application to price-dividend relationships, our test is able to find cointegration while linear based tests fail to do so.

We analyze the sizes of standard cointegration tests applied to data subject to linear interpolation, discovering evidence of substantial size distortions induced by the interpolation. We propose modifications to these tests to effectively eliminate size distortions from such tests conducted on data interpolated from end-of-period sampled low-frequency series. Our results generally do not support linear interpolation when alternatives such as aggregation or mixed-frequency-modified tests are possible.

Volume 20 of Advances in Econometrics is dedicated to Rob Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics, for their many valuable contributions to the econometrics profession. The Royal Swedish Academy of Sciences cited Rob “for methods of analyzing economic time series with time-varying volatility (ARCH),” while Clive was cited “for methods of analyzing economic time series with common trends (cointegration).” Of course, these citations are meant for public consumption but we specialists in time-series analysis know their contributions go far beyond these brief citations. Consider some of Rob's other contributions to our literature: Aggregation of Time Series, Band Spectrum Regression, Dynamic Factor Models, Exogeneity, Forecasting in the Presence of Cointegration, Seasonal Cointegration, Common Features, ARCH-M, Multivariate GARCH, Analysis of High Frequency Data, and CAViaR. Some of Sir Clive's additional contributions include Spectral Analysis of Economic Time Series, Bilinear Time Series Models, Combination Forecasting, Spurious Regression, Forecasting Transformed Time Series, Causality, Aggregation of Time Series, Long Memory, Extreme Bounds, Multi-Cointegration, and Non-linear Cointegration. No doubt, their Nobel Prizes are richly deserved. And the 48 authors of the two parts of this volume think likewise. They have authored some very fine papers that contribute nicely to the same literature that Rob's and Clive's research helped build.

Volume 20 of Advances in Econometrics is dedicated to Rob Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics, for their many valuable contributions to the econometrics profession. The Royal Swedish Academy of Sciences cited Rob “for methods of analyzing economic time series with time-varying volatility (ARCH)” while Clive was cited “for methods of analyzing economic time series with common trends (cointegration).” Of course, these citations are meant for public consumption but we specialists in time series analysis know their contributions go far beyond these brief citations. Consider some of Rob's other contributions to our literature: Aggregation of Time Series, Band Spectrum Regression, Dynamic Factor Models, Exogeneity, Forecasting in the Presence of Cointegration, Seasonal Cointegration, Common Features, ARCH-M, Multivariate GARCH, Analysis of High Frequency Data, and CAViaR. Some of Sir Clive's additional contributions include Spectral Analysis of Economic Time Series, Bilinear Time Series Models, Combination Forecasting, Spurious Regression, Forecasting Transformed Time Series, Causality, Aggregation of Time Series, Long Memory, Extreme Bounds, Multi-Cointegration, and Non-linear Cointegration. No doubt, their Nobel Prizes are richly deserved. And the 48 authors of the two parts of this volume think likewise. They have authored some very fine papers that contribute nicely to the same literature that Rob's and Clive's research helped build.

The aim of this chapter is to assess the real exchange rate misalignments. A smooth transition autoregressive model (STAR) is used for Tunisian exchange market. This model allows us to see whether these differences are temporary or persistent over the period 1975–2012. We start by defining the exchange rate’s fundamental determinants to provide the equilibrium exchange rate value. Then, we study the observed exchange rate adjustment toward its equilibrium level. Vector autoregressive model and vector error correction model are applied to characterize the joint dynamics of variables in the long run. The results indicate a long-run relationship between variables. In order to consider the nonlinearity for better results, we will move to nonlinear smooth transition model. We found there is a high degree of exchange rate misalignment. We recognized that this difference decreases in the long run and disappears at the end.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

In this chapter the author studies the capital market efficiency hypothesis and checks whether the stock price adjustment dynamics is instantaneous, continuous, and linear or not. In particular, the author proposes to analyze the stock price evolution while taking into account the presence of transaction costs, the coexistence of heterogeneous investors, and the interdependence between stock markets. On the one hand, he provides strong evidence to suggest that the efficiency hypothesis is rejected. On the other hand, he proves that the stock index adjustment is rather discontinuous, asymmetrical, and nonlinear. Using threshold cointegration techniques, he proposes a new nonlinear modeling to reproduce the CAC40 adjustment dynamics that not only replicates the French market adjustment dynamics in the presence of market frictions but also captures the interdependence between the French and American stock markets, highlighting the reaction of French shareholders in relation to the changes in the behaviour of American speculators.