The limit distribution of the estimates in cointegrated regression models with multiple structural changes

We study estimation and inference in cointegrated regression models with multiple structural changes allowing both stationary and integrated regressors. Both pure and partial structural change models are analyzed. We derive the consistency, rate of convergence and the limit distribution of the estimated break fractions. Our technical conditions are considerably less restrictive than those in Bai et al. [Bai, J., Lumsdaine, R.L., Stock, J.H., 1998. Testing for and dating breaks in multivariate time series. Review of Economic Studies 65, 395–432] who considered the single break case in a multi-equations system, and permit a wide class of practically relevant models. Our analysis is, however, restricted to a single equation framework. We show that if the coefficients of the integrated regressors are allowed to change, the estimated break fractions are asymptotically dependent so that confidence intervals need to be constructed jointly. If, however, only the intercept and/or the coefficients of the stationary regressors are allowed to change, the estimates of the break dates are asymptotically independent as in the stationary case analyzed by Bai and Perron [Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66, 47–78]. We also show that our results remain valid, under very weak conditions, when the potential endogeneity of the non-stationary regressors is accounted for via an increasing sequence of leads and lags of their first-differences as additional regressors. Simulation evidence is presented to assess the adequacy of the asymptotic approximations in finite samples.     

Estimating restricted structural change models

This paper considers issues related to multiple structural changes, occurring at unknown dates, in the linear regression model when restrictions are imposed on the parameters. This includes, for example, the important special case where different nonadjacent regimes are the same. The estimates are constructed as global minimizers of the restricted sum of squared residuals and we provide an extension of the algorithm discussed in Bai and Perron [2003b, Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22] to efficiently compute them. We show that the estimates of the break dates have the same asymptotic properties with or without the restrictions imposed; that is, in large samples, there is no efficiency gain from imposing valid restrictions as far as the estimates of the break dates are concerned. Of course, efficiency gains occur for the other parameters of the model. Simulations show that in small samples, all parameters are more efficiently estimated using the restrictions. We also consider tests of the null hypothesis of no structural change. These are also more powerful when the restrictions are imposed. A Gauss code for all the procedures discussed in this paper is available from the authors.     

Conditional regression analysis

This article deals with the question of whether the inclusion of multiplicative terms to model conditional effects in multiple regression is legitimate. The major arguments in the controversy relating to this subject are reviewed. The main conclusion is that most of the objections against multiplicative terms are based on misinterpretations of the coefficients of conditional models. For the often-ignored possible numerical problems in the estimation of these models, due to multicollinearity, an indirect estimation technique is proposed. The potentials of conditional regression analysis are demonstrated on a concrete example.     

Computation and analysis of multiple structural change models

In a recent paper, Bai and Perron ( 1998 ) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least‐squares operations of order O(T²) for any number of breaks. Our method can be applied to both pure and partial structural change models. Second, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program. Copyright © 2002 John Wiley & Sons, Ltd.     

Unstable Diffusion Indexes: With an Application to Bond Risk Premia

This paper studies the empirically relevant problem of estimation and inference in diffusion index forecasting models with structural instability. Factor model and factor augmented regression both experience a structural change with different unknown break dates. In the factor model, we estimate factors and loadings by principal components. We consider least squares estimation of the factor augmented regression and propose a break test. The empirical application uncovers instabilities in the linkages between bond risk premia and macroeconomic factors.     

The Limiting Distribution of Post-sample Stability Tests for GMM Estimation when the Potential Break Date is Unknown

Recent papers have proposed a split-sample prediction method to test for structural stability in GMM estimation when the potential break date is treated as known. In this note we derive the limiting distribution of the supremum of this test over all possible break dates, thus allowing for endogenous determination of the potential break date.     

GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

We propose a class of observation‐driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time‐varying parameters in a wide class of nonlinear models. The GAS model encompasses other well‐known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time‐varying mean. In addition, our approach can lead to new formulations of observation‐driven models. We illustrate our framework by introducing new model specifications for time‐varying copula functions and for multivariate point processes with time‐varying parameters. We study the models in detail and provide simulation and empirical evidence. Copyright © 2012 John Wiley & Sons, Ltd.     

Prescribed conditional interaction structure models with application to the analysis of mobility tables

The present paper considers some new models for the analysis of multidimensional contigency tables. Although the theoretical background used here appeared already in Haberman (1974), prescribed conditional interaction (PCIN) models were introduced by Rudas (1987) and their mathematical properties were worked out by Leimer and Rudas (1988). These models are defined by prescribing the values of certain conditional interactions in the contingency table. Conditional interaction is defined here as the logarithm of an appropriately defined conditional odds ratio. This conditional odds ratio is a conditional version of a generalization of the well known odds ratio of a 2×2 table and that of the three factor interaction term of a 2×2×2 table and applies to any number of dimensions and any number of categories of the variables. The well known log-linear (LL) models are special PCIN models. Estimated frequencies under PCIN models and tests of fit can be computed using existing statistical software (e.g. BMDP). The paper describes the class of PCIN models and compares it to the class of association models of Goodman (1981). As LL models are widely used in the analysis of social mobility tables, application of more general PCIN models is illustrated.     

Extreme returns and intensity of trading

Asymmetric information models of market microstructure claim that variables such as trading intensity are proxies for latent information on the value of financial assets. We consider the interval‐valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from this density, we have some random draws (trades) and the lowest and highest returns are the realized extreme observations of the latent process over the sample of draws. In this context, we propose a semiparametric model of extreme returns that exploits the results provided by extreme value theory. If properly centered and standardized extremes have well‐defined limiting distributions, the conditional mean of extreme returns is a nonlinear function of the conditional moments of the latent process and of the conditional intensity of the process that governs the number of draws. We implement a two‐step estimation procedure. First, we estimate parametrically the regressors that will enter into the nonlinear function, and in a second step we estimate nonparametrically the conditional mean of extreme returns as a function of the generated regressors. Unlike current models for ITS, the proposed semiparametric model is robust to misspecification of the conditional density of the latent process. We fit several nonlinear and linear models to the 5‐minute and 1‐minute low/high returns to seven major banks and technology stocks, and find that the nonlinear specification is superior to the current linear models and that the conditional volatility of the latent process and the conditional intensity of the trading process are major drivers of the dynamics of extreme returns.     

Influence analysis for Poisson inverse Gaussian regression models based on the EM algorithm

For Poisson inverse Gaussian regression models, it is very complicated to obtain the influence measures based on the traditional method, because the associated likelihood function involves intractable expressions, such as the modified Bessel function. In this paper, the EM algorithm is employed as a basis to derive diagnostic measures for the models by treating them as a mixed Poisson regression with the weights from the inverse Gaussian distributions. Several diagnostic measures are obtained in both case-deletion model and local influence analysis, based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. Two numerical examples are given to illustrate the results.     

Consistent model specification tests

In this paper we propose two consistent tests for functional form of nonlinear regression models without employing specified alternative models. The null hypothesis is that the regression function equals the conditional expectation function, which is tested against the alternative hypothesis that the null is false. These tests are based on a Fourier transform characterization of conditional expectations.     

Subgame-perfect equilibria in stochastic timing games

We develop a notion of subgames and the related notion of subgame-perfect equilibrium – possibly in mixed strategies – for stochastic timing games. To capture all situations that can arise in continuous-time models, it is necessary to consider stopping times as the starting dates of subgames. We generalize Fudenberg and Tirole’s (Rev. Econom. Stud. 52, 383–401, 1985) mixed-strategy extensions to make them applicable to stochastic timing games and thereby provide a sound basis for subgame-perfect equilibria of preemption games. Sufficient conditions for equilibrium existence are presented, and examples illustrate their application as well as the fact that intuitive arguments can break down in the presence of stochastic processes with jumps.     

Using OLS to Estimate and Test for Structural Changes in Models with Endogenous Regressors

We consider the problem of estimating and testing for multiple breaks in a single‐equation framework with regressors that are endogenous, i.e. correlated with the errors. We show that even in the presence of endogenous regressors it is still preferable, in most cases, to simply estimate the break dates and test for structural change using the usual ordinary least squares (OLS) framework. Except for some knife‐edge cases, it delivers estimates of the break dates with higher precision and tests with higher power compared to those obtained using an instrumental variable (IV) method. Also, the OLS method avoids potential weak identification problems caused by weak instruments. To illustrate the relevance of our theoretical results, we consider the stability of the New Keynesian hybrid Phillips curve. IV‐based methods only provide weak evidence of instability. On the other hand, OLS‐based ones strongly indicate a change in 1991:Q1 and that after this date the model loses all explanatory power. Copyright © 2013 John Wiley & Sons, Ltd.     

Structural break tests and the Greek sovereign debt crisis: revisited

Amidst the lack of consensus from previous academic studies, this paper contributes to existing literature by further examining the commencement date of the Sovereign Debt Crisis for the Greek economy. The contribution of this paper purports that the contentious issue of the start of the Greek crisis was taking place much earlier than reported by previous research. Empirical results from this paper challenge earlier studies that may have underestimated the impact of the degree of persistence in the volatility of bond returns. This analysis uses monthly 10-year Greek government bond data and three independent structural break model tests which allow for the detection of possible endogenous break dates to capture the beginning of the crisis. Each model provides empirically plausible and robust frameworks for examining the volatility of bond returns in an evolving time series behaviour. Ultimate results from a series of autoregressive EGARCH estimations, with and without dummy variables for break dates are compared. The dummy variables are incorporated within the coefficients of the mean and variance equations to validate the structural breaks in each series. Overall results show a significant presence of nonconsistent parameters capturing a structural break in the time series sample. The detection of this break, November 2009, represents a major regime change triggered by the start of the debt crisis for the Greek economy. Crucially, research implications of such excess volatilities in sovereign bond markets have poignant implications for regulators, investors and portfolio risk managers alike.     

Copula-based multivariate GARCH model with uncorrelated dependent errors

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our new C-MGARCH model nests a conventional MGARCH model as a special case. The aim of this paper is to model MGARCH for non-normal multivariate distributions using copulas. We model the conditional correlation (by MGARCH) and the remaining dependence (by a copula) separately and simultaneously. We apply this idea to three MGARCH models, namely, the dynamic conditional correlation (DCC) model of Engle [Engle, R.F., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20, 339–350], the varying correlation (VC) model of Tse and Tsui [Tse, Y.K., Tsui, A.K., 2002. A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20, 351–362], and the BEKK model of Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150]. Empirical analysis with three foreign exchange rates indicates that the C-MGARCH models outperform DCC, VC, and BEKK in terms of in-sample model selection and out-of-sample multivariate density forecast, and in terms of these criteria the choice of copula functions is more important than the choice of the volatility models.     

Testing for Panel Cointegration with Multiple Structural Breaks*

This paper proposes a Lagrange multiplier (LM) test for the null hypothesis of cointegration that allows for the possibility of multiple structural breaks in both the level and trend of a cointegrated panel regression. The test is general enough to allow for endogenous regressors, serial correlation and an unknown number of breaks that may be located at different dates for different individuals. We derive the limiting distribution of the test and conduct a small Monte Carlo study to investigate its finite sample properties. In our empirical application to the solvency of the current account, we find evidence of cointegration between saving and investment once a level break is accommodated.     

Two-stage rank estimation of quantile index models

This paper estimates a class of models which satisfy a monotonicity condition on the conditional quantile function of the response variable. This class includes as a special case the monotonic transformation model with the error term satisfying a conditional quantile restriction, thus allowing for very general forms of conditional heteroscedasticity. A two-stage approach is adopted to estimate the relevant parameters. In the first stage the conditional quantile function is estimated nonparametrically by the local polynomial estimator discussed in Chaudhuri (Journal of Multivariate Analysis 39 (1991a) 246–269; Annals of Statistics 19 (1991b) 760–777) and Cavanagh (1996, Preprint). In the second stage, the monotonicity of the quantile function is exploited to estimate the parameters of interest by maximizing a rank-based objective function. The proposed estimator is shown to have desirable asymptotic properties and can then also be used for dimensionality reduction or to estimate the unknown structural function in the context of a transformation model.     

An efficient Bayesian approach to multiple structural change in multivariate time series

This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Owing to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters. Two empirical applications show the improvements the model has over benchmarks. In a macro application with seven variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.     

Unemployment persistence: does the size of the shock matter?

One of the stylized facts of unemployment is that shifts in its mean rate between decades and half-decades account for most of its variance. In this paper, we use a statistical analysis based on Markov switching regression models to identify the dates of infrequent changes in the mean of the unemployment rate series of fifteen countries. We find that in most countries, unemployment persistence is much reduced once the (infrequently) changing mean rate, induced by large shocks to unemployment, has been removed. We conclude that the observed persistence in unemployment appears to be consistent with multiple equilibria models and models with an endogeneous natural rate. © 1998 John Wiley & Sons, Ltd.     

Testing the Null of Cointegration with Structural Breaks*

We propose a Lagrange Multiplier‐type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break, in both the deterministic and the cointegration vectors. Our proposal focuses on the presence of endogenous regressors. The test complements the usual non‐cointegration tests so as to obtain stronger evidence of cointegration. We consider the cases of known and unknown dates of the break. In the latter case, we show that minimizing the Sum of Squared Residuals results in a super‐consistent estimator of the break fraction. Finally, the behaviour of the tests is studied through Monte Carlo experiments.