Knowledge that Transforms

This paper extends the spatial theory of voting to the case in which policy choices depend upon the interaction between executive and the legislature. Voters are strategic and to analyze equilibrium the authors apply 'coalition proof' type refinements. The model has implications consistent with voting behavior in the United States: (1) split-ticket with some voters choosing one party for the presidency and the other for Congress; (2) for some parameter values, a divided government with different parties controlling the executive and the majority of the legislature; and (3) the mid-term electoral cycle with the party holding the presidency always losing votes in mid-term legislative elections.

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at

Inequality measures are often used to summarize information about empirical income distributions. However the resulting picture of the distribution and of changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general depend upon the underlying properties of the inequality measure. This issue is investigated theoretically using a technique based on the influence function, and the magnitude of the effect is illustrated using a simulation. Both direct nonparametric estimation from the sample, and indirect estimation using a parametric model are considered; in the latter case the application of a robust estimation procedure is demonstrated. The results are applied to two micro-data examples.

Digitised version produced by the EUI Library and made available online in 2020.

Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms' choices on whether to liquidate and on the quantities of inputs demanded should they continue depend on their productivity, we use an equilibrium model to suggest an estimation algorithm that takes into account the relationship between productivity on the one hand. and both input demand and survival on the other. A fully parametric version of the estimation algorithm would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semi parametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown. of unproductive plants and by disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.

Suppose an observed time series is generated by a stochastic volatility model-i.e., there is an unobservable state variable controlling the volatility of the innovations in the series. As shown by Nelson (1992), and Nelson and Foster (1994), a misspecified ARCH model will often be able to consistently (as a continuous time limit is approached) estimate the unobserved volatility process, using information in the lagged residuals. This paper shows how to more efficiently estimate such a volatility process using information in both lagged and led residuals. In particular, this paper expands the optimal filtering results of Nelson and Foster (1994) and Nelson (1994) to smoothing.

It is widely known that conditional covariances of asset returns change over time. Researchers adopt many strategies to accommodate conditional heteroskedasticity. Among the most popular: (a) chopping the data into short blacks of time and assuming homoskedasticity within the blocks, (b) performing one-sided rolling regressions, in which only data from, say, the preceding five year period is used to estimate the conditional covariance of returns at a given date, and (c) two-sided rolling regressions which use, say, five years of leads and five years of lags. GARCH amounts to a one-sided rolling regression with exponentially declining weights. We derive asymptotically optimal window lengths for standard rolling regressions and optimal weights for weighted rolling regressions. An empirical model of the S&P 500 stock index provides and example.