Multivariate Markov switching with weighted regime determination: giving France more weight than Finland

This article deals with using panel data to infer regime changes that are common to all of the cross section. The methods presented here apply to Markov switching vector autoregressions, dynamic factor models with Markov switching and other multivariate Markov switching models. The key feature we seek to add to these models is to permit cross-sectional units to have different weights in the calculation of regime probabilities. We apply our approach to estimating a business cycle chronology for the 50 U.S. States and the Euro area, and we compare results between country-specific weights and the usual case of equal weights. The model with weighted regime determination suggests that Europe experienced a recession in 2002-03, whereas the usual model with equal weights does not.Business cycles ; France ; Finland

Asymmetric effects of monetary policy in the United States

This paper tests for the presence of asymmetric effects of monetary policy on output. The asymmetries that the authors examine are related to the size and sign of monetary policy shocks and are based on economic theory. Using M1 as the basis for measuring monetary policy shocks, they find evidence in line with previous evidence of larger real effects resulting from positive shocks than from negative shocks—although the authors cannot reject symmetry either. However, using the federal funds rate instead, a measure that is more closely related to the actual conduct of monetary policy, they find that only small negative shocks affect real aggregate activity. The results are interpreted in terms of menu-cost models.Monetary policy ; Macroeconomics

Cross-Sectional Aggregation and Persistence in Conditional Variance

This paper explores the interactions between cross-sectional aggregation and persistence of volatility shocks. We derive the ARMA-GARCH representation that linear aggregates of ARMA processes with GARCH errors admit, and establish conditions under which persistence in volatility of the aggregate series is higher than persistence in the volatility of the individual series. The practical implications of the results are illustrated empirically in the context of an option pricing exercise.ARMA process; Cross-sectional aggregation; GARCH process; Volatility persistence.

Contemporaneous-threshold smooth transition GARCH models

This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH)model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization tosecond conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. (2007) in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. A key feature of the C-STGARCH model is that its transition function depends on all the parameters of the model as well as on the data. The structural properties of the model are investigated, in addition to the finite-sample properties of the maximum likelihood estimator of its parameters. An application to U.S. stock returns illustrates the practical usefulness of the C-STGARCH model

Contemporaneous threshold autoregressive models: estimation, testing and forecasting

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta (1998), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well-suited to rational expectations applications (and pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finite-sample maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen (1992) procedure. Furthermore, we construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate yield is presented and discussed. ; Earlier title: Contemporaneous threshold autoregressive models: estimation, forecasting and rational expectations applicationsRational expectations (Economic theory) ; Forecasting

Real options with priced regime-switching risk

We develop a model of regime-switching risk premia as well as regimedependent factor risk premia to price real options. The model incorporates the observation that the underlying risky income streams of real options are subject to discrete shifts over time as well as random changes. The presence of discrete shifts is due to systematic and unsystematic risk associated with changes in business cycles or in economic policy regimes or events such as takeovers, major changes in business plans. We analyze the impact of regime switching behavior on the valuation of projects and investment opportunities. We find that accounting for Markov switching risk results in a delay in the expected timing of the investment while the regime-specific factor risk premia make the possibility of a regime shift more pronounced

The effects of different parameterizations of Markov-switching in a CIR model of bond pricing

We examine several discrete-time versions of the Cox, Ingersoll and Ross (CIR) model for the term structure, in which the short rate is subject to discrete shifts. Our empirical analysis suggests that careful consideration of which parameters of the short-term interest rate equation that are allowed to be switched is crucial. Ignoring this issue may result in a parameterization that produces no improvement (in terms of bond pricing) relative to the standard CIR model, even when there are clear breaks in the data

Multivariate contemporaneous threshold autoregressive models

In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. The model is a multivariate generalization of the contemporaneous threshold autoregressive model introduced by Dueker et al. (2007). A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. The stability and distributional properties of the proposed model are investigated. The C-MSTAR model is also used to examine the relationship between US stock prices and interest rates.Time-series analysis ; Capital assets pricing model

State-Dependent Threshold STAR Models

In this paper we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a time-varying function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State-dependent logistic STAR and contemporaneous-threshold STAR models are introduced and discussed. These models are also used to investigate the dynamics of U.S. short-term interest rates, where the threshold is allowed to be a function of past output growth and inflation.Nonlinear autoregressive models; Smooth transition; Threshold; Interest rates.

Multivariate Contemporaneous-Threshold Autoregressive Models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.Nonlinear autoregressive model; Smooth transition; Stability; Threshold.