Distribution Dynamics of CO<Subscript>2</Subscript> Emissions

This paper uses nonparametric methods to examine the convergence in CO₂ emissions per capita on a sample of 100 countries for the period 1966–1996. Industrial countries show a convergence pattern. However, there is little evidence of convergence for the whole sample.     

Two–step sequential sampling

Choosing the sample size in advance is a familiar problem: often, additional observations appear to be desirable. The final sample size then becomes a random variable, which has rather serious consequences.
Two such sample extension situations will be considered here. In the first situation, the observed sample variance determines whether or not to double the original sample size. In the second situation, the variances observed in two independent samples are compared; their ratio determines the number of additional observations.     

Nonparametric estimation of stochastic volatility models

This letter introduces nonparametric estimators of the drift and diffusion coefficient of stochastic volatility models which exploit techniques for estimating integrated volatility with high-frequency data. The performance of the proposed estimators is assessed on simulations of two popular stochastic volatility models.     

Recovering Hicksian Consumer Surplus within a Collective Model: Hausman's Method for the Household

The purpose of this paper is to describe the implications of the collective model of household behavior for the methods used to estimate the economic value of non-marketed environmental resources. After demonstrating how the separability restrictions inherent in the collective model allow individual preference and household income allocation choices to be distinguished, the paper demonstrates how the framework can be used to recover Hicksian consumer surplus. An algebraic example is used to illustrate how the framework can be used in valuing environmental resources.     

Explaining Real Commercial Rents Using an Error Correction Model with Panel Data

This paper presents rent models for retail and office property in the United Kingdom. Panel data are used covering eleven regions for 29 years, enabling us to overcome the limitations of a relatively short time series. We use an error correction model (ECM) framework to estimate long-run equilibrium relationships and short-term dynamic corrections. The combination of panel data and an ECM is an innovative approach that is still being developed in economics. We construct new supply series that combine infrequent stock data with more frequent construction data. Separate regional models are estimated for retail and office properties. The regions are then combined into a number of panels on the basis of the income and price elasticities in the long-run and short-run models. Unlike previous studies, we find no evidence of a board north–south divide between low growth and high growth regions. Like these studies we do find a London effect: in London, demand elasticities for space with respect to both price (rent) and income are much lower in magnitude. We conclude that, while the economic drivers may vary, there is no evidence of differences in the operation of the regional property markets outside London. Elasticities for retail and office are similar. Our final models are parsimonious with single measures of economic activity and of supply and always support the use of an ECM.     

论建设项目前期的工程造价合理确定

投资估算是控制工程造价的源头，是开展工程造价管理后续工作的关键，尤其在工程建设投资多元化的当今，能否合理确定投资估算、设计概算，将影响到投资资金筹措和计划的落实。章就如何合理地确定工程造价提出了建议。     

The relationship between stock returns and volatility in international stock markets

This study examines the relationship between expected stock returns and volatility in the 12 largest international stock markets during January 1980 to December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in 6 out of the 12 markets. The results lend some support to the recent claim [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42; Whitelaw, R., 2000. Stock market risk and return: an empirical equilibrium approach. Review of Financial Studies 13, 521–547] that stock market returns are negatively correlated with stock market volatility.     

U.S. Monetary Regimes and Optimal Monetary Policy in the Euro Area

Monetary policy in the United States has been documented to have switched from reacting weakly to inflation fluctuations during the 1970s, to fighting inflation aggressively from the early 1980s onward. In this paper, I analyze the impact of the U.S. monetary policy regime switches on the Eurozone. I construct a New Keynesian two‐country model where foreign (U.S.) monetary policy switches regimes over time. I estimate the model for the U.S. and the Euro Area using quarterly data and find that the United States has switched between those two regimes, in line with existing evidence. I show that foreign regime switches affect home (Eurozone) inflation and output volatility and their responses to shocks, substantially, as long as the home central bank commits to a time‐invariant interest rate rule reacting to domestic conditions only. Optimal policy in the home country instead requires that the home central bank reacts strongly to domestic producer‐price inflation and to international variables, such as imported goods relative prices. In fact, I show that currency misalignments and relative prices play a crucial role in the transmission of foreign monetary policy regime switches internationally. Interestingly, I show that only marginal gains arise for the Euro Area when the European Central Bank (ECB) adjusts its policy according to the monetary regime in the United States. Thus, a simple time‐invariant monetary policy rule with a strong reaction to Producer Price Index (PPI) inflation and relative prices is enough to counteract the effects of monetary policy switches in the United States.     

Inference with Dependent Data in Accounting and Finance Applications

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.     

A logistic regression point of view toward loss given default distribution estimation

We propose a new procedure to estimate the loss given default (LGD) distribution. Owing to the complicated shape of the LGD distribution, using a smooth density function as a driver to estimate it may result in a decline in model fit. To overcome this problem, we first apply the logistic regression to estimate the LGD cumulative distribution function. Then, we convert the result into the LGD distribution estimate. To implement the newly proposed estimation procedure, we collect a sample of 5269 defaulted debts from Moody’s Default and Recovery Database. A performance study is performed using 2000 pairs of in-sample and out-of-sample data-sets with different sizes that are randomly selected from the entire sample. Our results show that the newly proposed procedure has better and more robust performance than its alternatives, in the sense of yielding more accurate in-sample and out-of-sample LGD distribution estimates. Thus, it is useful for studying the LGD distribution.