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Summary This paper provides doubly robust estimators for treatment effect parameters which are defined in a multivalued treatment effect framework. We apply this method to the unique dataset of the 1970 British Cohort Study (BCS70) to estimate returns to various levels of schooling. The analysis is carried out for female and male samples separately to capture possible gender differences. Average returns are estimated for the entire population, as well as conditional on…

Summary Two‐step nonparametric estimators have become standard in empirical auctions. A drawback concerns boundary effects which cause inconsistencies near the endpoints of the support and bias in finite samples. To cope, sample trimming is typically used, which leads to non‐random data loss. Monte Carlo experiments show this leads to poor performance near the support boundaries and on the interior due to bandwidth selection issues. We propose a modification that employs…

外商直接投资与时间对追赶的影响：条件非参数前沿分析的新见解AI 解读

Summary We use an appropriate nonparametric two‐step approach on conditional efficiencies to investigate how foreign direct investment (FDI) and time affect the process of catching up. By using a dataset of 44 countries over 1970–2007, we explore the channels under which FDI fosters productivity by disentangling the impact of this factor on the production process and its components: impact on the attainable production set (input–output space) and the impact on the…

We bring together some recent advances in the literature on vector autoregressive moving-average models, creating a simple specification and estimation strategy for the cointegrated case. We show that in this case with fixed initial values there exists a so-called final moving-average representation. We prove that the specification strategy is consistent. The performance of the proposed method is investigated via a Monte Carlo study and a forecasting exercise for US interest…

In this paper we, like several studies in the recent literature, employ a Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than complete excludability. When allowing for instrument imperfection of this type, the model is only partially identified, and as a consequence standard estimates obtained from the Gibbs simulations can be unacceptably imprecise. We thus describe a substantially improved…

Factor models have been applied extensively for forecasting when high-dimensional datasets are available. In this case, the number of variables can be very large. For instance, usual dynamic factor models in central banks handle over 100 variables. However, there is a growing body of literature indicating that more variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results. This paper investigates the usefulness of partial…

This paper compares the forecasting performance of models that have been proposed for forecasting in the presence of structural breaks. They differ in their treatment of the break process, the model applied in each regime and the out-of-sample probability of a break. In an extensive empirical evaluation, we demonstrate the presence of breaks and their importance for forecasting. We find no single model that consistently works best in the presence of breaks. In many cases,…

We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations…

不同时变波动率设定下的宏观经济预测表现AI 解读

This paper compares alternative models of time-varying volatility on the basis of the accuracy of real-time point and density forecasts of key macroeconomic time series for the USA. We consider Bayesian autoregressive and vector autoregressive models that incorporate some form of time-varying volatility, precisely random walk stochastic volatility, stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of…

Summary Many empirical applications of regression discontinuity (RD) models use a running variable that is rounded and hence discrete, e.g. age in years, or birth weight in ounces. This paper shows that standard RD estimation using a rounded discrete running variable leads to inconsistent estimates of treatment effects, even when the true functional form relating the outcome and the running variable is known and is correctly specified. This paper provides simple formulas to…

Abstract This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying…

巴黎住房特征价格：含嵌套随机效应的非平衡空间滞后伪面板模型AI 解读

Summary This paper estimates a hedonic housing model based on flats sold in the city of Paris over the period 1990–2003. This is done using maximum likelihood estimation, taking into account the nested structure of the data. Paris is historically divided into 20 arrondissements , each divided into four quartiers (quarters), which in turn contain between 15 and 169 blocks ( îlot , in French) per quartier . This is an unbalanced pseudo‒panel data containing 156,896…

总统支持率与经济的关系：多维半参数方法AI 解读

Summary Empirical studies analyzing the determinants of US presidential popularity have delivered quite inconclusive results concerning the role of economic variables by assuming linear relationships. We employ penalized spline smoothing in the context of semiparametric additive mixed models and allow for flexible functional forms and thus possible nonlinear effects for the economic determinants. By controlling for the well‐known politically motivated covariables, we find…

Summary Motivated by the need for a positive‐semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra‐high‐frequency asset prices in a state‐space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and expectation maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a covariance matrix estimate which is robust to both asynchronicity and…

医院生产分析：产出指数方法AI 解读

Summary In this study, we develop and implement an output index approach to the estimation of hospital cost functions that reflects the differentiated nature of hospital care. The approach combines the estimation of an output index within a flexible functional form. We find, in an application to California hospitals, evidence of scope economies across specialties within primary care, and diseconomies of scope within secondary and tertiary care. Minimum efficient scale is…

Summary When dealing with the estimation of Engel curves, measurement errors in expenditure data and simultaneity are likely sources of endogeneity. In this paper we study identification of the parameters that characterize an Engel curve in the presence of both. We consider specifications where budget shares are polynomials in the logarithm of total expenditure, which is the case frequently encountered in empirical applications. We propose an estimation procedure which is an…

Bottazzi and Peri (Economic Journal 2007; 117: 486–511) show the existence of a cointegrating relationship between the domestic stock of knowledge, domestic R&D and the international knowledge stock for a panel of OECD countries and interpret it as evidence supporting the semi-endogenous versus the endogenous growth theory. We replicate the baseline specification of their study and we show that main results are robust to the use of a different estimation strategy (Bai et…

This paper addresses the debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We construct global minimum variance portfolios based on the constituents of the S&P 500. HF-based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF-based predictions yield a significantly lower portfolio volatility…

Price indices for heterogeneous goods such as real estate or fine art constitute crucial information for institutional or private investors considering alternative investment decisions in times of financial markets turmoil. Classical mean-variance analysis of alternative investments has been hampered by the lack of a systematic treatment of volatility in these markets. In this paper we propose a hedonic regression framework which explicitly defines an underlying stochastic…

The coefficient of relative risk aversion is notoriously difficult to estimate. Recently, Barro and Jin (On the size distribution of macroeconomic disasters, Econometrica 2011; 79(3): 434–455) have come up with a new estimation approach that fits a power-law model to the tail of distribution of macroeconomic disasters. We show that their results can be successfully replicated using a more refined power-law fitting methodology and a more comprehensive dataset. Copyright ©…

评估DSGE模型的点预测与密度预测AI 解读

This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation are the model forecasts dominated by the…

政府债务何时挤出投资？AI 解读

We examine when government debt crowds out investment for the US economy using an estimated New Keynesian model with detailed fiscal specifications and accounting for monetary and fiscal policy interactions. Whether investment is crowded in or out in the short term depends on policy shocks triggering debt expansions: higher debt can crowd in investment for cutting capital tax rates or increasing government investment. Contrary to the conventional view, no systematic…

The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered. In addition, the presence of cross-section dependence among the panel units can distort the empirical size of the statistics. We therefore design a testing procedure that allows for both structural breaks and cross-section dependence when testing the null hypothesis of no cointegration. The paper…

使用 OLS 估计和检验含内生回归变量模型的结构变化AI 解读

We consider the problem of estimating and testing for multiple breaks in a single-equation framework with regressors that are endogenous, i.e. correlated with the errors. We show that even in the presence of endogenous regressors it is still preferable, in most cases, to simply estimate the break dates and test for structural change using the usual ordinary least squares (OLS) framework. Except for some knife-edge cases, it delivers estimates of the break dates with higher…