Ho-Chuan (River) Huang

Bayesian analysis of the SUR Tobit model

This paper develops a practical sampling scheme for Bayesian analysis of correlated censored data using the seemingly unrelated Tobit regressions model. Posterior inference is performed via the Gibbs sampler with data augmentation algorithm. In particular, the relevant full conditional distributions needed in the use of Gibbs sampler are derived. The method is then applied to a real data set on the determination of the payments of cash and stock dividends.

Estimation of the SUR Tobit model via the MCECM algorithm

Abstract A novel approach is proposed to estimate the seemingly unrelated regressions model in which the dependent variables might be censored. Our method via the Monte Carlo version of the EM algorithm can be used to retrieve the latent values which greatly simplify the computation of the E-step and a sequence of conditional maximizations is performed to implement the M-step. Its practicality is illustrated by estimating a bivariate SUR Tobit model to determine the payments of cash and stock dividends.

Nonlinearity between Inequality and Growth

The existing literature shows that income inequality plays an important role in growth process, and such a relationship is better characterized as nonlinearity. The paper revisits the issue by employing the threshold regressions with instrumental variables approach. Using the initial level of economic development as a threshold variable, we find strong evidence in support of a nonlinear income threshold in the relationship. In particular, the data show that an increase in inequality would hinder growth in low-income countries but accelerate growth in high-income ones. The results therefore suggest that redistributive policy that alleviates inequality can foster economic growth in low-income countries, while policymakers confront a tradeoff between inequality and growth in high-income countries.

Time-varying discrete monetary policy reaction functions

A novel dynamic ordered probit model with time-varying parameters is proposed to estimate a monetary policy reaction function with narrative-based monetary indicators. The estimation and inference are carried out using the Bayesian simulation-based approach. Empirically, these are the following findings. First, there is strong evidence in support that the Central Bank in Taiwan responds counter-cyclically to inflation but weaker, if any, evidence to economic growth. Secondly, the persistence and consistence in policy-making of the monetary authority is confirmed by the significance of the positive autoregressive coefficient. Although not all, the estimates of the TVP-DOP model provide, at least, partial support of time-varying parameters. Finally, the results indicate that studies of the discrete monetary policy reaction functions without explicitly considering the possible dynamics inherent in the time series data and time-variations in model parameters may be inappropriate, if not incorrect.

A flexible nonlinear inference to Okun's relationship

The study implements the flexible nonlinear inference approach of Hamilton (2001) to assess the validity of Okuns law. Using US quarterly data from 1948:1 to 2004:2, overwhelming evidence is found of nonlinearity between cyclical components of unemployment and output. However, the relationship is still negative and provides strong support of (nonlinear) Okuns law.

Kuznets Hypothesis In A Panel Of States

This paper uses a new comprehensive cross-state panel for the United States over the 1945-2004 period to reassess the relationship between income inequality and economic development. By employing the pooled mean group estimator of Pesaran, Shin, and Smith (1999), it detects a long-run cointegrating association between inequality and development (as well as its squared term). Moreover, their relationship is better characterized by a U shape rather than the inverted-U profile asserted by Kuznets (1955). The evidence is robust to a variety of sensitivity tests.

The Effect of Growth Volatility on Income Inequality

This paper assesses the long-run effect of growth volatility on income inequality using a comprehensive panel of annual U.S. state-level data during the 1945 to 2004 period. Using the pooled mean group (PMG) estimator, we find overwhelming evidence supporting the hypothesis that larger growth volatility positively and significantly associates with higher income inequality. In addition, our key finding is robust to alternative lag structures, conditioning variables, inequality measures, volatility indicators, and time periods.

Okun's law in panels of countries and states

This article contributes to the empirical literature of Okuns law in three respects. First, in contrast to the limited data used in the existing studies, we employ two extensive (across countries and across states, i.e. within a country) panel data sets to investigate the validity of Okuns law. Second, the use of the Pooled Mean Group (PMG) estimator permits us not to pre-filter the data as often done in the current literature, and can take into account the possibility of cointegration between unemployment and output. Third, in addition to the short-run relationship or cyclical components between unemployment and output, we also estimate the long-run linkage between these two important variables. Empirical results show that unemployment and output are long-run cointegrated, irrespective of using country- or state-level data. Moreover, the unemployment-output linkages are found to be negative and highly significant both in the short- and long-run. Our results not only confirm the validity of Okuns law (in the short-run) but also point out that a similar tradeoff exists in the long run.

Does financial development volatility affect industrial growth volatility

This paper investigates whether volatility of financial development plays a role in determining industrial growth volatility. Three key findings emerge. First, overwhelming evidence supports the view that more volatile financial development raises the industrial volatility in sectors that rely more on external liquidity. Second, the harmful effect of financial volatility on industrial volatility mainly works through the increase in fluctuations of the growth of real value added per firm and the number of firms, with the former effect more prominent. Third, both the volatilities of the banking sector and the stock market positively associate with higher industrial growth volatility, which contrasts sharply with the finding in the existing literature that financial structure generally does not matter.

Tests of regime-switching CAPM under price limits

Abstract This paper incorporates two specific features in the test of capital asset pricing model (CAPM). The first, or empirical, one is to allow the systematic risk β to come from two different regimes to capture the instability found in the previous studies. The second, or institutional, one is to consider the censoring effect caused by the implementation of price limit regulation. Under price limit regulation, the observed returns need not be equal to the equilibrium ones if the closing prices reach limits. The caused econometric problems seem to be very challenging but can be resolved by the Gibbs sampler with data augmentation algorithm. A simple illustrative example is provided by using the data from the Taiwan Stock Exchange where the price limit system is adopted. The general results suggest that β s are unstable over time and the data may be consistent with CAPM in one regime but inconsistent in the other regime.