不同融资制度下的金融中介发展

在任何一种经济中都需要建立一种金融机构与证券市场能够共同发挥作用的、完善的金融体系。但是,考虑到各种经济制度建立基础和发展历史与传统上的差异,以及金融体系与企业部门之间的关系不同,各国金融体系的架构并不需要保持完全的一致。本文从不同融资制度中金融中介的作用,探讨经济中金融中介的发展。     

The Significance of Credit Rationing on Real Investment Activity – A Test for the Significance of Capital-Market Imperfection in Japan

Does bank credit matter to the real investment finance of firms? If so, to what kind of firms does it matter, and why? Despite the progress of research in this field, there is still little empirical consensus on these problems. This paper contributes to the understanding of capital-market imperfection in Japan by answering these questions. I introduce new methods to test the significance of credit rationing and show that bank credit is indeed a very important determinant of real investment activity of small firms. The availability of long-term loans has particularly significant effects on the investment opportunities of small firms, although large firms with capital of more than 1 billion yen are free from credit rationing. JEL Classification Numbers: C51, E44, G21.     

Two-step estimation of panel data models with censored endogenous variables and selection bias

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.     

How accurate are government forecasts of economic fundamentals? The case of Taiwan

A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate.In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.     

ECONOMETRIC ISSUES IN MACROECONOMIC MODELS WITH GENERATED REGRESSORS

Abstract. The paper critically reviews the literature on the econometric issues raised by the use of generated regressors (GR) in empirical models. The economic rationale for the use of GR is considered, with examples being drawn from several macroeconomic examples, including New Classical Macroeconomic (NCM) models which postulate monetary ncutrality. Various estimation methods are discussed for models which include 'surprise' or 'unexpected' terms and the strengths and weaknesses of each approach are investigated. Drawing upon the work of McAleer and McKenzie (1991b), situations where the typically inefficient two-step estimation (2SE) method will be efficient are highlighted. Problems of model misspecification and measurement errors are also investigated. An empirical section highlights some of the dangers of using uncorrected 2SE estimation results through a careful consideration of many recent attempts to test the NCM monetary neutrality hypothesis.     

Long-run Growth and Intragenerational Transfer Under Capital Market Imperfections

The purpose of this paper is to provide a long-run growth model linking growth to income distribution between lenders and borrowers in an environment where enforcement of loan contracts is imperfect. The equilibrium under costly verification implies a smaller growth rate, relative to the symmetric-information economy. Intra-generational transfer of income is shown to promote growth so long as the redistribution gives rise to an increase in net worth positions of borrowers.
JEL Classification Numbers: G21, O16, O40     

The CUSUM of squares test for the stability of regression models with non-stationary regressors

In this paper, we propose a modified CUSUM of squares test in time series regression models with a non-stationary regressor and show that the limiting distribution of this test is the sup of the absolute value of a Brownian bridge.     

MANY INSTRUMENTS AND/OR REGRESSORS: A FRIENDLY GUIDE

This paper surveys the state of the art in the econometrics of regression models with many instruments or many regressors based on alternative – namely, dimension – asymptotics. We list critical results of dimension asymptotics that lead to better approximations of properties of familiar and alternative estimators and tests when the instruments and/or regressors are numerous. Then, we consider the problem of estimation and inference in the basic linear instrumental variables regression setup with many strong instruments. We describe the failures of conventional estimation and inference, as well as alternative tools that restore consistency and validity. We then add various other features to the basic model such as heteroskedasticity, instrument weakness, etc., in each case providing a review of the existing tools for proper estimation and inference. Subsequently, we consider a related but different problem of estimation and testing in a linear mean regression with many regressors. We also describe various extensions and connections to other settings, such as panel data models, spatial models, time series models, and so on. Finally, we provide practical guidance regarding which tools are most suitable to use in various situations when many instruments and/or regressors turn out to be an issue.     

An empirical likelihood method for spatial regression

Properties of a “blockwise”empirical likelihood for spatial regression with non-stochastic regressors are investigated for spatial data on a lattice. The method enables nonparametric confidence regions for spatial trend parameters to be calibrated, even though non-random regressors introduce non-stationary forms of spatial dependence into the “blockwise” construction. Additionally, the regression results are valid in a general framework allowing for a variety of behavior in regressor variables as well as the underlying spatial error process. The same regression method also applies when the regressors are stochastic.