Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present 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. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.

Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data

Simple diagnostic tests for spatial dependence

We propose 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, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine-tune the forecast selection to current economic conditions. To this end, we propose a two-step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter-reduction methods for macroeconomic forecasting using a large number of predictors.

/pdf/tests-of-conditional-predictive-ability-577cvl72zr.pdf

Tests of Conditional Predictive Ability

Patents and innovation counts as measures of regional production of new knowledge

We examine the influence of venture capital on patented inventions in the United States across twenty industries over three decades. We address concerns about causality in several ways, including exploiting a 1979 policy shift that spurred venture capital fundraising. We find that increases in venture capital activity in an industry are associated with significantly higher patenting rates. While the ratio of venture capital to R&D averaged less than 3% from 1983-1992, our estimates suggest that venture capital may have accounted for 8% of industrial innovations in that period.(Publication abstract)

Assessing the Contribution of Venture Capital to Innovation

A TEST for spatial correlation in regression disturbances has been given by Cliff and Ord (1973). In their treatment, the test is defined by analogy with that first advanced by Durbin and Watson (1950) in the context of serial correlation. It is then shown that the asymptotic efficiency of the Cliff-Ord statistic relative to any other of the same functional form is not less than unity. An informal argument is then employed to demonstrate the convergence of their test statistic, when the spatial correlation parameter approWaches zero, to a quadratic form appearing in the likelihood ratio. The derivation given below enables their argument to be by-passed, as Silvey's (1959) general results on the asymptotic equivalence of Lagrange Multiplier, and likelihood ratio tests can now be invoked. Consider the usual linear regression model,

On the Cliff-Ord Test for Spatial Correlation

Three asymptotically equivalent tests for the presence of a common factor in a spatial autoregression model are presented. When such a common factor exists, the model reduces to the simple regressi...

Testing for a Common Factor in a Spatial Autoregression Model

Government- and charity-funded medical research and private sector research and development (RD 44 % of that additional private sector expenditure occurs within 1 year, with the remainder accumulating over decades. This spillover effect implies a real annual rate of return (in terms of economic impact) to public biomedical and health research in the UK of 15–18 %. When combined with previous estimates of the health gain that results from public medical research in cancer and cardiovascular disease, the total rate of return would be around 24–28 %. Overall, this suggests that government and charity funded research in the UK crowds in additional private sector R&D in the UK. The implied historical returns from UK government and charity funded investment in medical research in the UK compare favourably with the rates of return achieved on investments in the rest of the UK economy and are greatly in excess of the 3.5 % real annual rate of return required by the UK government to public investments generally.

/pdf/quantifying-the-economic-impact-of-government-and-charity-b8emqt5rjx.pdf

Quantifying the economic impact of government and charity funding of medical research on private research and development funding in the United Kingdom

Unemployment in the british metropolitan labour areas

Value-at-risk (VaR) measures are generated using extreme value theory by modelling the tails of the return distributions of six Asian financial markets during the recent volatile market conditions. The maxima and minima of these return series were found to be satisfactorily modelled within an extreme value framework and the value at risk measures generated within this structure were found to be different to those generated by variance–covariance and historical methods, particularly for markets characterised by high degrees of leptokurtosis such as Malaysia and Indonesia.

Value-at-risk: Applying the extreme value approach to Asian markets in the recent financial turmoil

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