Abstract This article presents a theoretical case for replacement of conventional heteroskedasticity-consistent and cluster-robust variance estimators with jackknife variance estimators, in the context of linear regression with heteroskedastic and/or cluster-dependent observations. We examine the bias of variance estimation and the coverage probabilities of confidence intervals. Concerning bias, we show that conventional variance estimators have full downward worst-case bias, while our jackknife variance estimator is never downward biased. Concerning confidence intervals, we show that intervals based on conventional standard errors have worst-case coverage equalling zero, while the jackknife-based confidence interval has coverage probability bounded by the Cauchy distribution, under the auxiliary assumption of normal errors. We also extend the Bell and McCaffrey (2002) student t approximation to our jackknife t-ratio, resulting in confidence intervals with improved coverage probabilities. Our theory holds under broad assumptions, allowing arbitrary cluster sizes, regressor leverage, within-cluster correlation, heteroskedasticity, regression with a single treated cluster, fixed effects, and delete-cluster invertibility failures. Our theoretical findings are consistent with the extensive simulation literature investigating heteroskedasticity-consistent and cluster-robust variance estimation.

Abstract We propose a new model of internal migration, based on persistent and spatially correlated idiosyncratic utility. The model is motivated by a new fact in the data that simple moving cost models struggle to match: the t-year interstate migration rate is proportional to the square root of t. The new model maintains the tractability and flexibility of standard migration models, but better matches the dynamics of migration, including the new fact. It has substantially different welfare implications and makes different counterfactual predictions, especially in terms of dynamic adjustment and long-run responses.

R. Bentzel, B. Hansen; On Recursiveness and Interdependency in Economic Models1, The Review of Economic Studies, Volume 22, Issue 3, 1 January 1954, Pages

The paper studies the effects and the determinants of interregional redistribution in a model of residential and political choice. We find that paradoxical consequences of interjurisdictional transfers arise if people are mobile: while self-sufficient regions are necessarily identical with respect to policies and average incomes in our model, interregional redistribution always leads to the divergence of regional policies and per capita incomes. Thus, interregional redistribution prevents inter regional equality. At the same time, however, transfers may allow for more inter personal equality among the inhabitants of each region. The voting population may therefore in a decision over the fiscal constitution deliberately implement such a transfer scheme to foster regional divergence. Empirical evidence from panel data from OECD countries and Canadian provinces is consistent with the theory.

This paper studies the asymptotic properties of instrumental variable (IV) estimates of multivariate cointegrating regressions and allows for deterministic and stochastic regressors as well as quite general deterministic processes in the data-generating mechanism. It is found that IV regressions are consistent even when the instruments are stochastically independent of the regressors. This phenomenon, which contrasts with traditional theory for stationary time series, is a beneficial artifact of spurious regression theory whereby stochastic trends in the instruments ensure their relevance asymptotically. Problems of inference are also addressed and some promising new theoretical results are reported. These involve a class of Wald tests which are modified by semiparametric corrections for serial correlation and for endogeneity. The resulting test statistics which we term fully-modified Wald tests have limiting X2 distributions, thereby removing the obstacles to inference in cointegrated systems that were presented by the nuisance parameter dependencies in earlier work. Some simulation results are reported which seek to explore the sampling behaviour of our suggested procedures. These simulations compare our fully modified (semiparametric) methods with the parametric error-correction methodology that has been extensively used in recent empirical research and with conventional least squares regression. Both the fully-modified and errorcorrection methods work well in finite samples and the sampling performance of each procedure confirms the relevance of asymptotic distribution theory, as distinct from super-consistency results, in discriminating between statistical methods.

We provide the first direct empirical support for the importance of signalling in monetary policy by testing two key predictions from a novel structural model. First, all policymaker types should become less tough on inflation over time and secondly, types that weigh output more should have a more pronounced shift. Voting data from the Bank of England's Monetary Policy Committee strongly support both predictions. Counterfactual results indicate signalling has a substantial impact on interest rates over the business cycle, and improves the committee designer's welfare. Implications for committee design include allowing regular member turnover and transparency regarding publishing individual votes.

“… I suppose there exists an extremely powerful, and, if I may so speak, malignant being, whose whole endeavours are directed toward deceiving me.” Rene Descartes, Meditations, II.1

Abstract We present a tractable heterogeneous-agent version of the New Keynesian model that allows us to study the interaction between inequality and monetary policy. Though formulated as a precautionary-saving model à la Huggett–Aiyagari, its reduced form is a two-agent model with a highly concentrated wealth distribution. When prices are sticky and wages flexible, as in the textbook representative-agent model, monetary policy affects the distribution of consumption, but has no effect on output as workers choose not to change their hours worked in response to wage movements. This highlights a transmission mechanism of the textbook model that we find implausible: in response to a monetary stimulus, the representative worker’s labor supply is greatly affected by the profits she receives. First, the lower profits induced by higher wages raise labor supply through a wealth effect and, secondly, the mere presence of profits reduces the negative income effect of a wage rise. When wages are rigid, in contrast, our model exhibits plausible responses of output and hours worked to monetary policy shocks.

We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by including a relatively small number of variables whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of regressors. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the “post-double-selection†method. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. In contrast, standard post-model selection estimators fail to provide uniform inference even in simple cases with a small, fixed number of controls. Thus, our method resolves the problem of uniform inference after model selection for a large, interesting class of models. We also present a generalization of our method to a fully heterogeneous model with a binary treatment variable. We illustrate the use of the developed methods with numerical simulations and an application that considers the effect of abortion on crime rates.

Abstract We use decision theory to confront uncertainty that is sufficiently broad to incorporate “models as approximations.” We presume the existence of a featured collection of what we call “structured models” that have explicit substantive motivations. The decision-maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max–min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns. We then extend our analysis beyond the max-min case allowing for a more general criterion that encompasses a Bayesian formulation.