LABOUR SUPPLY ESTIMATION AND PUBLIC POLICY

Abstract. Economists devote considerable energies towards refining their econometric techniques to overcome difficulties connected with conducting empirical research. Despite advances in technique. it is not clear whether further refinement in this direction is worthwhile for policy purposes. It may be that no further amount of statistical adjustment of inadequate data will increase understanding, and that better data is simply necessary to add to our knowledge. But rarely is sufficient credit paid to new forms of data. In short, econometric technique is emphasized to the neglect of data innovation, as if new data were merely lying about waiting for an ingenious suggestion for use. This paper surveys advances of the last twenty five years in estimating labour supply for policy purposes with a view towards appreciating the relative contribution of both improvements in econometric technique as well as developments of new data.
After briefly detailing the key parameters which economists have sought to estimate, we describe the early 'first generation' research (circa 1970), which is plagued by problems of unobservable variables, measurement errors, truncation and selectivity bias, and non linear budget constraints. 'Second generation' research constitute attempts to resolve one or more of these difficulties, and the respective contribution of econometric technique and new data is acknowledged and assessed, including the contribution of data generated by large scale social experiments in which participants are randomly assigned to different guaranteed income plans and their labour supply behaviour measured.     

Some Cracks in the Empire of Chance (Flaws in the Foundations of Reliability)

Making quantified statements about the uncertainty associated with the lifelength of an item is one of the most fundamental tasks of reliability assessment. Most practitioners routinely do this using one of the several available statistical techniques. The purpose of this paper is two-fold. The first is to give the user an overview of the key tenets of two of the most commonly used parametric approaches. The second is to point out that these commonly used approaches involve strategies that are either ad hoc, or are in violation of some of the underlying tenets. A method that is devoid of logical flaws can be proposed, but this method is difficult to implement. The user must therefore resign to using that technique against which the fewest objections can be hurled.     

The Robustness of the APT to Alternative Estimators

We test the robustness of the APT to two alternative estimation procedures: the Fama and MacBeth (1973) two-step methodology; and the one-step procedure due to Burmeister and McElroy (1988). We find that the APT is indeed sensitive to the chosen estimator and assumptions about the factor structure of stock returns. We believe that our findings have implications for the estimation of asset pricing models in general.     

宽松的环境政策能吸引更多的外商直接投资吗——基于中国工业省级面板数据的分析

利用我国1997~2009年的工业省级面板数据和动态面板GMM估计方法,考察了相对宽松的环境政策是否是吸引FDI的主要原因。研究发现,环境政策较宽松的地区能够吸引更多的外资流入,但这种影响并不大,所以,以降低环境标准、牺牲环境为代价来吸引FDI不是明智之举;与东、中部地区相比,西部在吸引FDI上处于明显劣势,这在一定程度上加剧了我国经济结构的失衡。     

Monetary policy matters: Evidence from new shocks data

The evidence suggests that monetary policy post 1988 became more forward-looking, invalidating the identifying assumptions in conventional methods of measuring monetary policy's effects, leading to spurious and unlikely results for this period. We propose a new identification scheme that uses factors extracted from Fed Funds futures to measure exogenous changes in policy. Using this shock series in a VAR, we recover the contractionary effect of monetary tightening on output. Moreover, we find that as much as half of the variability in output was driven by monetary policy shocks, and that there is a mild price puzzle.     

An improved bootstrap test of stochastic dominance

We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities so that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test’s limiting distribution.     

Functional-coefficient models for nonstationary time series data

This paper studies functional coefficient regression models with nonstationary time series data, allowing also for stationary covariates. A local linear fitting scheme is developed to estimate the coefficient functions. The asymptotic distributions of the estimators are obtained, showing different convergence rates for the stationary and nonstationary covariates. A two-stage approach is proposed to achieve estimation optimality in the sense of minimizing the asymptotic mean squared error. When the coefficient function is a function of a nonstationary variable, the new findings are that the asymptotic bias of its nonparametric estimator is the same as the stationary covariate case but convergence rate differs, and further, the asymptotic distribution is a mixed normal, associated with the local time of a standard Brownian motion. The asymptotic behavior at boundaries is also investigated.     

Systematic risk estimation in the presence of large and many outliers

It is well recognized that the effect of extreme points on systematic risk estimates is not adequately captured through least squares estimation. This article uses the reweighted least median squares (RWLMS) approach, first proposed by Rousseeuw (1984), which accurately detects outlier presence. Using a large sample of 1350 NYSE/AMEX firms, the article demonstrates that least squares does indeed mask several potentially influential points, that this masking is very pervasive over the sample, and that it may persist even after conventional robust estimation techniques are applied. When these masked points are “unmasked” by RWLMS and zero weights assigned to such observations, the resulting RWLMS estimates of beta are on average 10%–15% smaller. However, a Bayesian treatment of such points (assigning a priori nonzero weights) is possible in both one and two factor market models.     

Can interest rate spreads stabilize the euro area?

Since the onset of the financial crisis significant interest rate spreads have arisen between euro area countries, both for public and private debt. We check whether these spreads could be made to work towards the goal of providing more stability to the euro area. In particular, we focus on reducing the imbalances that arose between the core and peripheral members of the euro area in the first decade of its existence. The idea is that stable positive spreads in peripheral countries could have decreased domestic demand, preventing the boom–bust cycles that plagued these economies. They could also prevent such developments in the future. We construct a panel model for euro area countries and estimate the relationship between real interest rates and the current account balance. Next, we use the estimated parameters to perform simulations. We find that spreads on real interest rates of 0.6–5.5 percentage points would have been necessary to stabilize external positions of the four peripheral euro area member countries.