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This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. By employing a categorization of the underlying durations we reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an observation driven ARMA dynamic based on generalized errors. We illustrate the maximum likelihood estimation of both the model parameters and discrete points of the underlying unspecified baseline survivor function. The dynamic properties of the model as well as the estimation quality are investigated in a Monte Carlo study. It is illustrated that the model is a useful approach to estimate conditional failure probabilities based on (persistent) serially dependent duration data which might be subject to censoring mechanisms. In an empirical study based on financial transaction data we apply the model to estimate conditional asset price change probabilities. An evaluation of the forecasting properties of the model shows that the proposed approach is a promising competitor to well-established ACD type models.

This paper analyzes the equilibrium efficiency in a Ramsey model with habit formation. Uniqueness and saddle-path stability of the steady state is proved analytically. The competitive equilibrium is efficient at the steady state. However, the presence of externalities arising from average past consumption renders the competitive equilibrium inefficient off the steady state because agents do not take (fully) into account the indirect effect that consumption has in utility through its influence on habits. The efficient equilibrium can be decentralized by means of a consumption tax that converges to an arbitrary constant value, or by means of an income tax that converges to zero.

The goal of this paper is to illuminate the capability of the component GARCH model of Ding and Granger (1996) and Engle and Lee (1999) to reproduce the long memory-type behavior of financial volatility. The potential of this model to capture the long memory dynamics observed in measures of financial volatility has been documented recently by Maheu (2005) and Deo et al. (2006), who base their conclusions on simulation techniques and a forecasting exercise, respectively. In this paper, a simple explanation for these observations is provided, which is based on the theoretical autocorrelation function (ACF) of the component GARCH model. We also elucidate why even higher-order GARCH models with Bollerslev's (1986) nonnegativity constraints enforced cannot mimic the long memory effects. The reasoning is supported with several empirical examples, for which we explicitly calculate the theoretical ACF implied by a couple of different fitted models, and find that their structure is just as predicted by our argument. To conveniently conduct these computations, a general simple method for computing the theoretical ACF of GARCH models is suggested, which is easier to use than the formulas developed so far, and particularly so for higher lag-orders. The ability of the component model to approximate long memory is also validated on the basis of a visual comparison between the empirical and the implied theoretical ACFs.

In this paper we present an endogenous growth model with environmental pollution and public capital. As to pollution we assume that it is a by-product of aggregate production and that it negatively affects utility of the household but not production possibilities directly. The paper studies the dynamics of the model and demonstrates that there exists either a unique balanced growth path which is a saddle point or there exist two balanced growth paths with one being locally saddle point stable and one being asymptotically stable.

Diebold and Rudebusch (1991) and Haubrich (1993) argue that, when income follows a fractionally differenced process, the Deaton's excessive smoothness paradox can be resolved. A key to the success of their result relies on a valid test for fractional integration. However, most of the tests in the literature are nested within fractional alternatives. This paper designs a new test for a more general hypothesis that the true data generating process is indeed fractionally integrated. The test is applied to the real disposable income per capita of the U.S. and the real quarterly GDP data of the G7 industrial countries.

This paper develops a change-point model that can endogenously detect a structural shift in a time series of durations. The model is applied to NBER data on U.S. business cycle durations for expansions and contractions. There are two primary results. First, the change-point model endogenously detects a shift in the distribution for the phases of the U.S. business cycle around WWII. The pattern of duration dependence for both contractions and expansions correspond to earlier work, such as Diebold and Rudebusch (1990), Sichel (1991) and Zuehlke(2003), that exogenously split the sample at WWII. The second result is that the change-points for expansions and contractions generally occur earlier than WWII when controlling for various factors, such as the duration of the preceding half-cycle, wars and a trend variable. For expansions, the only significant explanatory variable is a trend, resulting in each successive expansion's hazard rate uniformly shifting down. For contractions, both a trend and the lagged duration of the preceding expansion are found, when estimated separately, to be significant. Controlling for a trend, contractions no longer exhibit positive duration dependence following the estimated change-point.