Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.

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Conditional Skewness in Asset Pricing Tests

Earnings Management and Investor Protection: An International Comparison

In this paper, recent progress of phase change memory (PCM) is reviewed. The electrical and thermal properties of phase change materials are surveyed with a focus on the scalability of the materials and their impact on device design. Innovations in the device structure, memory cell selector, and strategies for achieving multibit operation and 3-D, multilayer high-density memory arrays are described. The scaling properties of PCM are illustrated with recent experimental results using special device test structures and novel material synthesis. Factors affecting the reliability of PCM are discussed.

Phase Change Memory

The relative performance of no-load, growth-oriented mutual funds persists in the near term, with the strongest evidence for a one-year evaluation horizon. Portfolios of recent poor performers do significantly worse than standard benchmarks; those of recent top performers do better, though not significantly so. The difference in risk-adjusted performance between the top and bottom octile portfolios is six to eight percent per year. These results are not attributable to known anomalies or survivorship bias. Investigations with a different (previously used) data set and with some post-1988 data confirm the finding of persistence.

Hot Hands in Mutual Funds: Short‐Run Persistence of Relative Performance, 1974–1988

This paper tests the Kraus-Litzenberger (1976) three-moment capital asset pricing model using Hansen's (1982) generalized method-of-moments (GMM). The GMM approach does not impose strong distributional assumptions on the asset returns. This is an interest? ing issue since there is no obvious multivariate distribution for returns that also exhibits co-skewness. Using monthly stock returns to test the model, there is some evidence that systematic skewness is priced.

A New Test of the Three-Moment Capital Asset Pricing Model

The reversible and fast phase transitions induced by picosecond electrical pulses are observed in the nanostructured GeSbTe materials, which provide opportunities in the application of high speed nonvolatile random access memory devices. The mechanisms for fast phase transition are discussed based on the investigation of the correlation between phase transition speed and material size. With the shrinkage of material dimensions, the size effects play increasingly important roles in enabling the ultrafast phase transition under electrical activation. The understanding of how the size effects contribute to the phase transition speed is of great importance for ultrafast phenomena and applications.

Fast phase transitions induced by picosecond electrical pulses on phase change memory cells

The quest for universal memory is driving the rapid development of memories with superior all-round capabilities in non-volatility, high speed, high endurance and low power. Phase-change materials are highly promising in this respect. However, their contradictory speed and stability properties present a key challenge towards this ambition. We reveal that as the device size decreases, the phase-change mechanism changes from the material inherent crystallization mechanism (either nucleation- or growth-dominated), to the hetero-crystallization mechanism, which resulted in a significant increase in PCRAM speeds. Reducing the grain size can further increase the speed of phase-change. Such grain size effect on speed becomes increasingly significant at smaller device sizes. Together with the nano-thermal and electrical effects, fast phase-change, good stability and high endurance can be achieved. These findings lead to a feasible solution to achieve a universal memory.

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Enabling Universal Memory by Overcoming the Contradictory Speed and Stability Nature of Phase-Change Materials

We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean-variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical.

Kian Guan Lim

Papers

A New Test of the Three-Moment Capital Asset Pricing Model

Fast phase transitions induced by picosecond electrical pulses on phase change memory cells

Enabling Universal Memory by Overcoming the Contradictory Speed and Stability Nature of Phase-Change Materials

Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

Arbitrage and price behavior of the Nikkei stock index futures