Laurent L. Pauwels

What Prompts the People's Bank of China to Change its Monetary Policy Stance? Evidence from a Discrete Choice Model

In the present paper, we model the policy stance of the Peoples Bank of China (PBC) as a latent variable, and the discrete changes in the reserve requirement ratio, policy interest rates, and the scale of open market operations are taken as signals of movement of this latent variable. We run a discrete choice regression that relates these observed indicators of policy stance to major trends of macroeconomic and financial developments, which are represented by common factors extracted from a large number of variables. The predicted value of the estimated model can then be interpreted as the implicit policy stance of the PBC. In a second step, we estimate how much of the variation in the PBCs implicit stance can be explained by measures of its policy objectives on inflation, growth and financial stability. We find that deviations of CPI inflation from an implicit target and deviations of broad money growth from the announced targets, but not output gaps, figure significantly in the PBCs policy changes.

Modelling environmental risk

As environmental issues have become increasingly important in economic research and policy for sustainable development, firms in the private sector have introduced environmental and social issues in conducting their business activities. Such behaviour is tracked by the Dow Jones Sustainable Indexes (DJSI) through financial market indexes that are derived from the Dow Jones Global Indexes. The sustainability activities of firms are assessed using criteria in three areas, namely economic, environmental and social. Risk (or uncertainty) is analysed empirically through the use of conditional volatility models of investment in sustainability-driven firms that are selected through the DJSI. The empirical analysis is based on financial econometric models to determine the underlying conditional volatility, with the estimates showing that there is strong evidence of volatility clustering, short and long run persistence of shocks to the index returns, and asymmetric leverage between positive and negative shocks to returns.

MEASURING RISK IN ENVIRONMENTAL FINANCE

Environmental sustainability indices, such as the Dow Jones Sustainability Indexes and the Ethibel Sustainability Index, quantify the development and promotion of sustainable social, ethical and environmental values in the community. Moreover, such indices provide a benchmark for managing sustainability portfolios, and developing financial products and services that are linked to sustainable economic, environmental, social and ethical criteria. This paper reviews the existing data and risk indices in environmental finance. The main purpose of the paper is to analyse existing sustainability and ethical indices in environmental finance, and evaluate empirical environmental risk by estimating conditional volatility clustering that is inherent in these indices. Financial volatility models are estimated to analyse the underlying conditional volatility or time-varying risk that is inherent in alternative environmental sustainability indices. Volatility clustering is observed for most series, but some extreme observations are also evident. The log- and second-moment conditions suggest that valid inferences can be drawn for purposes of sensible empirical analysis. Copyright 2007 The Authors. Journal compilation

Multivariate volatility in environmental finance

There exist several important benchmark indexes in environmental finance, some computed by well-known financial index providers such as the Dow Jones group and others by independent agencies specializing in environmentally and socially responsible investing in finance. The construction of these sustainability indexes relies on two distinct screening methods, positive and negative, which aim to include or exclude candidate companies according to sustainable economic, environmental, social and ethical criteria. We investigate the presence and the importance of multivariate effects in conditional volatility in two major financial time-series indexes, namely the Dow Jones Sustainability Index (DJSI) World and the Ethibel Sustainability Index (ESI) Global, as a way to analyse their relative inherent risk. We further investigate empirically the existence of risk spillovers across these four indexes as a mean to assess the impact of the different screening criteria. Finally, the trends and volatility of two prominent financial indexes, the DJIA and S&P500, are analysed in the same manner to provide a comparison of the performance of the two types of indexes.

Stability tests for heterogeneous panel data

This paper proposes a new test for structural stability in panels by extending the testing procedure proposed in the seminal work of Andrews (2003) originally developed for time series. The test is robust to non-normal, heteroskedastic and serially correlated errors, and, importantly, allows for the number of post break observations to be small. Moreover, the test accommodates the possibility of a break affecting only some - and not all - individuals of the panel. Under mild assumptions the test statistic is shown to be asymptotically normal, thanks to the cross sectional dimension of panel data. This greatly facilitates the calculation of critical values with respect to the tests time series counterpart. Monte Carlo experiments show that the test has good size and power under a wide range of circumstances. Finally, the test is illustrated in practice, in a brief study of the euros effect on trade.

Inflation in Hong Kong, SAR - in Search of a Transmission Mechanism

Inflation in a country with a currency board is usually believed to be highly dependent on external factors. Important questions for understanding the dynamics of inflation are (i) how best to measure these factors and (ii) how to model the transmission mechanism. This paper brings evidence to both questions. First, the paper shows that using CPI-based measures of foreign inflation does not adequately capture external influences on inflation in Hong Kong. Second, the paper shows that import prices and wages have a significant causal role. Together these conclusions suggest that Hong Kongi¦s price dynamics can be modeled by a Phillips Curve in which marginal cost of production plays an important role. When we estimate a New Phillips curve model for Hong Kong, a significant forward-looking component to expectations is identified. In addition, we find that prices are relatively flexible in HK, with adjustments taking place almost twice as fast as in the United States. Finally import prices and property rental rates appear to be important components of marginal cost of production alongside wages. More traditional versions of the Phillips curve also fit the data quite well. Even in this traditional specification, however, measures based on changes in production cost outperform measures of excess demand as forcing variables.

A note on the estimation of optimal weights for density forecast combinations

The problem of finding appropriate weights for combining several density forecasts is an important issue that is currently being debated in the forecast combination literature. A recent paper by Hall and Mitchell (2007) proposes that density forecasts be combined using the weights obtained from solving an optimization problem. This paper documents the properties of this optimization problem through a series of simulation experiments. When the number of forecasting periods is relatively small, the optimization problem often produces solutions that are dominated by a number of simple alternatives.

Model specification in panel data unit root tests with an unknown break

Although the impact of structural breaks on testing for unit root has been studied extensively for univariate time-series, such impact on panel data unit root tests is still relatively unknown. A major issue is the choice of model in accommodating different types of break prior to testing for unit root. Model misspecification has been known to affect unit root tests performance in the univariate case but the effect of misspecification on panel tests is still unknown. This paper has two objectives: (i) it proposes a new test for unit root in the presence of structural break for panel data. The test allows the intercepts, the trend coefficients or both to change at different date for different individuals. Moreover, the test allows for the possibility that only some, but not all, of the individuals experienced structural breaks. Under some mild assumptions, the test statistics is shown to be asymptotically normal which greatly facilitates valid inferences. (ii) This paper provides a systematic study on the impact of structural instability on testing for unit root using Monte Carlo Simulation. The results show that correct specification is crucial for unit root testing in the presence of structural instability. In addition, the proportion of individuals experienced structural instability can also affect the performance of the test substantially.

Forecast Combination for Discrete Choice Models: Predicting FOMC Monetary Policy Decisions

This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecast associated with each model. The paper applies well-established scoring rules for qualitative response models in the context of forecast combination. Log-scores and quadratic-scores are both used to evaluate the forecasting accuracy of each model and to combine the probability forecasts. In addition to producing point forecasts, the effect of sampling variation is also assessed. This methodology is applied to forecast the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic fundamentals influencing the FOMC decisions are nonstationary over time and are modelled in a similar fashion to Hu and Phillips (2004a, JoE). The empirical results show that combining forecasted probabilities using scores mostly outperforms both equal weight combination and forecasts based on multivariate models.

Testing for structural change in heterogeneous panels with an application to the Euro’s trade effect

Abstract This paper presents a structural change test for panel data models in which the break (or the change) affects some, but not all, cross-section units in the panel. The test is robust to non-normal, heteroskedastic and autocorrelated errors, as well as end-of-sample structural change. The test amounts to computing and comparing pre- and post-break sample statistics as Chow (1960) type F statistics averaged over cross-section units. The cases of known and unknown break date are both considered. Under mild assumptions, the test has a limiting standard normal distribution as the number of cross-sections tends to infinity. Monte Carlo experiments show that the test has good size and power under a wide range of circumstances, including when the break date is unknown and differs across individual units, and when errors exhibit cross-section dependence. Finally, the test is illustrated by seeking a break in the dynamics of trade among euro area countries following the introduction of the euro.