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In this article we design an econometric test for monotone comparative statics (MCS) often found in models with multiple equilibria. Our test exploits the observable implications of the MCS prediction: that the extreme (high and low) conditiona l quantiles of the dependent variable increase monotonically with the explanatory variable. The main contribution of the article is to derive a likelihood-ratio test, which to the best of our knowledge is the first econometric test of MCS proposed in the literature. The test is an asymptotic “chi-bar squared” test for order restrictions on intermediate conditional quantiles. The key features of our approach are: (1) we do not need to estimate the underlying nonparametric model relating the dependent and explanatory variables to the latent disturbances; (2) we make few assumptions on the cardinality, location, or probabilities over equilibria. In particular, one can implement our test without assuming an equilibrium selection rule.

Throughout the 1990s, the supply of new condominiums in Tokyo significantly increased while prices persistently fell. This article investigates whether the market power of condominium developers is a factor in explaining the outcome in this market and whether there is a relationship between production cost trend and the degree of market power that the developers were able to exercise. In order to respond to these questions, we construct and structurally estimate a dynamic durable goods oligopoly model of the condominium market – one incorporating time-variant costs and a secondary market – using a nested GMM procedure. We find that the data provide no evidence that firms in the primary market have substantial market power in this industry. Moreover, the counterfactual experiment provides evidence that inflationary and deflationary expectations on production cost trends have asymmetric effects to the market power of condominium producers. The increase in their markup when cost inflation is anticipated is significantly higher than the decrease in the markup when the same magnitude of cost deflation is anticipated.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

This article analyzes transitions into and out of social assistance (SA) in Canada. We estimate dynamic probit models, controlling for endogenous initial conditions and unobserved heterogeneity, using longitudinal data extracted from the Survey of Labour and Income Dynamics (SLID) for the years 1993–2010. The data indicate that there are substantial provincial differences in SA participation with higher participation rates in the eastern part of the country. However, since the mid-1990s, participation rates have fallen substantially in all provinces with only a modest increase at the end of the observation period. Results from the probit models suggest that there is a significant time dependency in social assistance, even after controlling for endogenous initial conditions and unobserved heterogeneity. The extent of this state dependence varies across provinces.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.

In this chapter, the authors ask two questions: (i) Is the conduct of monetary policy stable across time and similar across major economies? and (ii) Do policy decisions of major central banks have international spillover effects? To address these questions, the authors build on recent semi-parametric advances in time-varying parameter models that allow us to increase the vector autoregressive () dimension and to jointly model three advanced economies (USA, UK and the Euro Area). The main reduced-form finding of this chapter is an increased connectedness between and within countries during the recent financial crisis. In order to study policy spillovers, we jointly identify three economy-specific monetary policy shocks using a combination of sign and magnitude restrictions. The authors find that monetary policy shocks were larger in magnitude and more persistent in the early 1980s than in subsequent periods. The authors also uncover positive spillover effects of policy between countries in the 1980s and diminished, and sometimes negative ‘beggar-thy-neighbour’ effects in the second half of the sample. Moreover, during the 1980s, the authors find evidence for policy coordination between the Federal Reserve, the Bank of England and the European Central Bank.