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工具变量回归中基于因子的识别稳健推断AI 解读

Summary Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity…

双边市场的半参数分析：美国地方日报应用AI 解读

Summary This paper considers an empirical semiparametric model for two‐sided markets. Contrary to existing empirical literature on two‐sided markets, we specify network effects and probability distribution functions of net benefits of the two sides nonparametrically. We then estimate the model by nonparametric instrumental variables regression for local daily newspapers from the USA. We show that semiparametric specification is supported by the data and the network effects…

含 I(1) 与 I(0) 变量的混合系统建模：计量方法AI 解读

Summary This paper considers structural models with both I (1) and I (0) variables. The structural shocks associated with either set of variables could be permanent or transitory. We classify the shocks as (P1,P0) and (T1,T0), where P/T distinguishes permanent/transitory, while 1/0 means they are attached to structural equations with either I (1) or I (0) variables as their ‘dependent’ variable. We show that P0 shocks can affect cointegration analysis and provide a formula…

控制点与数据合并：因子结构模型的识别和推断AI 解读

Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine datasets that each identify some part of the likelihood, thereby recovering the identification of the complete…

异质专业能力下的贝叶斯决策问题估计AI 解读

We consider the recent novel two-step estimator of Iaryczower and Shum (American Economic Review 2012; 102: 202–237), who analyze voting decisions of US Supreme Court justices. Motivated by the underlying theoretical voting model, we suggest that where the data under consideration display variation in the common prior, estimates of the structural parameters based on their methodology should generally benefit from including interaction terms between individual and time…

电力市场放松管制影响的平滑转换 Logit 模型AI 解读

This paper introduces the smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties, together with a Lagrange multiplier test of the null hypothesis of linearity in the underlying latent index. The…

欧洲央行货币政策意外：通过利率共同跳跃识别AI 解读

This paper proposes a new econometric approach to disentangle two distinct response patterns of the yield curve to monetary policy announcements. Based on cojumps in intraday tick data of short- and long-term interest rate futures, we develop a day-wise test that detects the occurrence of a significant policy surprise and identifies the market perceived source of the surprise. The new test is applied to 133 policy announcements of the European Central Bank (ECB) in the…

多个变量缺失情形下的广义矩估计AI 解读

We consider efficient estimation in moment conditions models with non-monotonically missing-at-random (MAR) variables. A version of MAR point-identifies the parameters of interest and gives a closed-form efficient influence function that can be used directly to obtain efficient semi-parametric generalized method of moments (GMM) estimators under standard regularity conditions. A small-scale Monte Carlo experiment with MAR instrumental variables demonstrates that the…

利用不平等指标变化识别与估计干预的分布影响AI 解读

This paper presents estimators of distributional impacts of interventions when selection to the program is based on observable characteristics. Distributional impacts are calculated as differences in inequality measures of the marginal distributions of potential outcomes of receiving and not receiving the treatment. The estimation procedure involves a first non-parametric estimation of the propensity score. In the second step weighted versions of inequality measures are…

不稳定环境下的预测理性检验：美联储与调查预测的应用AI 解读

This paper proposes a framework to implement regression-based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and efficiency tests, commonly referred to as tests of forecast rationality. Our framework is general: it can be applied to model-based forecasts obtained either with recursive or rolling window estimation schemes, as well as to forecasts that are model free. The proposed tests provide more evidence against…

Copula的灵活估计：美国住房危机的应用AI 解读

Zimmer (‘The role of copulas in the housing crisis’, Review of Economics and Statistics 2012; 94: 607–620) provides an interesting case study of the pitfalls of using parametric copulas to understand the US housing crisis in the latter part of 2000s. The original study by Zimmer (2012) employs a finite-mixture copula to illustrate that the symmetry of the Gaussian copula may not be tenable, especially for US housing price data during the time period from 1975:Q2 to 2009:Q1.…

趋势通胀、NAIRU 与菲利普斯曲线时变性的有界模型AI 解读

In this paper, we develop a bivariate unobserved components model for inflation and unemployment. The unobserved components are trend inflation and the non-accelerating inflation rate of unemployment (NAIRU). Our model also incorporates a time-varying Phillips curve and time-varying inflation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather bounded random walks. For…

不确定性的测量与行为：欧洲央行专业预测者调查证据AI 解读

We examine matched point and density forecasts of output growth, inflation and unemployment from the ECB Survey of Professional Forecasters. We construct measures of uncertainty from individual histograms, and find that the measures display countercyclical behavior and have increased across all forecast horizons since 2007. We also derive measures of forecast dispersion and forecast accuracy, and find that they are not reliable proxies for uncertainty. There is, however,…

将劳动供给视为潜在工作选择：不可观测异质性与识别AI 解读

This paper discusses aspects of a modeling framework in which the notion of job choice is fundamental. In this framework, workers are assumed to have preferences over latent job opportunities belonging to worker-specific choice sets from which they choose their preferred job. The main contribution of this paper is an analysis of the identification problem under various conditions, when conventional cross-section micro-data are applied. The modeling framework is applied to…

模型不确定性与弱外生性下的面板增长实证分析AI 解读

This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian model averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed effects. An application of this econometric methodology to a panel of countries over the 1960–2000 period highlights the difficulties in identifying the sources of economic growth by…

识别消费变动的独立来源AI 解读

Summary By representing a system of budget shares as an approximate factor model we determine its rank, i.e. the number of common functional forms or factors, and we estimate a base of the factor space by means of approximate principal components. We assume that the extracted factors span the same space of basic Engel curves representing the fundamental forces driving consumers' behaviour. We identify these curves by imposing statistical independence and by studying their…

结构向量自回归过程的贝叶斯图模型AI 解读

Summary This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced‐form VAR model. The BGVAR approach is shown to be…

用于工具变量选择的Lasso：一项复现研究AI 解读

Summary Recently, Lasso methods have been applied to economic questions. In a seminal paper, Belloni et al . ( Econometrica ; 80 (6): 2369–2429) make use of (post‐)Lasso for instrumental variable selection in a setting where the number of instruments p is large or might even exceed the number of observations n —a situation which is prevalent in many current applications. We replicate their simulation study with the statistical package R (R Development Core Team ( )) and,…

J检验向空间面板数据框架的扩展AI 解读

Summary Kelejian ( Letters in Spatial and Resources Sciences ; 1 : 3–11) extended the J ‐test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras ( Regional Science and Urban Economics ; 41 : 281–292) generalized and modified Kelejian's test to account for all the available information. However, neither Kelejian ( ) nor Kelejian & Piras ( )…

内生友谊形成与选择偏差下的社会互动模型AI 解读

Summary This paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social interactions. When there are unobserved factors that affect both friendship decisions and economic outcomes, the spatial weight matrix (sociomatrix; adjacency matrix) in the SAR model, which represents the structure of a friendship network, might correlate with the disturbance term of the model, and…

定价核单调性的实证检验AI 解读

Summary A large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high. More generally, we expect the projection of the stochastic discount factor on the market portfolio—that is, the discounted pricing kernel evaluated at the market portfolio—to be a monotonically decreasing function of the market portfolio. Numerous recent empirical studies…

面板数据模型中的截面依赖：专题社论AI 解读

Journal of Applied EconometricsVolume 31, Issue 1 p. 1-3 Editorial Cross-Sectional Dependence in Panel Data Models: A Special Issue First published: 24 January 2016 https://doi.org/10.1002/jae.2507Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I…

大N面板数据模型中无截面相关的定向检验AI 解读

Summary The paper proposes tests for no cross‐correlation based on the information matrix equality. The tests rely on suitably weighted residual cross‐covariances or cross‐correlations, and are in this sense a generalization of Pesaran's (2004, CESifo working paper 1229) test for no cross‐sectional dependence. They follow chi‐squared distributions under joint N , T asymptotics without restrictive sphericity or distributional assumptions. When using the outcome of the…

横截面依赖下动态面板模型的估计：利用簇依赖提高效率AI 解读

Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses…