This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence” found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the ...

UNLABELLED The objective of this paper is to verify the hypotheses presented in the literature on the causal relationship between inflation and its uncertainty, for the newest EU countries. To ensure the robustness of the results, in the study four models for inflation uncertainty are estimated in parallel: ARCH (1), GARCH (1,1), EGARCH (1,1,1) and PARCH (1,1,1). The Granger method is used to t...

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for di¤erent predictors to a¤ect di¤erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future in‡a...

This paper extends the distributional theory for the problem of testing for structural change in the linear model when the timing of the change is unknown, and proposes a simple method of obtaining approximate critical values for the mean-Wald test. The results apply for a very wide range of regressor types, including integrated and trending regressors, and regressors that exhibit their own str...

In this paper we investigate whether macroeconomic uncertainty could distort banks’ allocation of loanable funds. To provide a road– map for our empirical investigation, we present a simple framework which demonstrates that lower uncertainty about the return from lending should lead to a more unequal distribution of lending across banks as managers take advantage of more precise knowledge of di...

OLS estimation of an impulse-indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a tdistribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitti...

Regional inequality is severe in China since regional development is uneven due to various initial conditions and government policies. We employ unit root tests allowing for structural breaks to alternative inequality measures from 1952 to 2000. Empirical results indicate that (1) the regional inequality is trend stationary with structural breaks rather than follow a random walk. Thus, ignoring...

This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various authors including Christoffersen and Diebold (2000), Diebold, Gunther and Tay (1998), Diebold, Hahn and Tay (1999), White (2000), Bai (2003...

Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. This paper provides a formal method of testing for nonstationary long memory against the alternative of particular forms of nonlinerarity. The nonlinear models we consider are ESTAR and SETAR models. We provide analysis on the asymptotic properties of the tests and carry out a detail...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...