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A Very Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

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This article is published in Research Papers in Economics.The article was published on 1991-01-01 and is currently open access. It has received 736 citations till now. The article focuses on the topics: Covariance function & Estimation of covariance matrices.

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The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

TL;DR: In this article, the authors evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, recover both the points and the conditional distribution forecasts.
Report SeriesDOI

Forecasting coin demand

TL;DR: In this paper, a variety of models were developed, tested, and used in real-time forecasting for forecasting coin demand, and the forecast errors from the models were examined both in quasi-exante forecasting exercises and in realtime use.
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Yield spreads and real economic activity in East European transition economies

TL;DR: In this paper, the authors investigated the role of the yield spread on real economic activity and found that the term spread is a better indicator of future real growth in countries with low and stable inflation (Czech Republic, Poland, Hungary and Slovakia).
ReportDOI

Food Stamps and America’s Poorest

TL;DR: In this article, the authors provided the first assessment of America's progress in lifting the lower bound of the distribution of real income and whether the country's largest antipoverty program, SNAP (food stamps), helped do so.
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Adjusted Earnings Yields and Real Rates of Return

TL;DR: The authors presents methodologies that investors can use to estimate the accounting and debt adjustments for individual companies and offers evidence, derived from a predictive regression model, that investors should consider these adjustments important.
References
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Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data

TL;DR: The authors presented conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large.
Posted Content

Assessing the Contribution of Venture Capital to Innovation

TL;DR: This paper examined the influence of venture capital on patent applications in twenty industries over three decades and found that increases in venture capital activity in an industry are associated with significantly higher patenting rates.
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Approximately Normal Tests for Equal Predictive Accuracy in Nested Models

TL;DR: In this paper, the mean squared prediction error (MSPE) from the parsimonious model is adjusted to account for the noise in the large model's model. But, the adjustment is based on the nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size.
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Tests of Conditional Predictive Ability

TL;DR: This paper proposed a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodelled heterogeneity, incorrect functional form, or any combination of these.
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