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竞争企业数量未知时在线价格竞争模型的识别与估计AI 解读

Summary This paper considers identification and estimation of a general model for online price competition. We show that when the number of competing firms is unknown the underlying parameters of the model can still be identified and estimated employing recently developed results on measurement errors. We illustrate our methodology using UK data for personal digital assistants and employ the estimates to simulate competitive effects. Our results reveal that heightened…

Firpo、Fortin 与 Lemieux（2009）无条件分位数回归的复现AI 解读

Summary This paper is a narrow replication of Firpo, Fortin and Lemieux (Unconditional quantile regressions. Econometrica 2009; 77(3): 953–973), who propose a new estimation method, called ‘unconditional quantile regressions’. Using their empirical example, we confirm their results for the effects of unionization on US wage inequality during the period 1983–1985 for both conditional and unconditional quantile regressions. In addition, this paper applies the proposed…

内生性与不依从下已处理者及其他群体平均处理效应的尖锐工具变量界限AI 解读

Summary In the presence of an endogenous binary treatment and a valid binary instrument, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance assumptions to derive sharp bounds on the average treatment effects on the…

泡沫与危机：房价和信贷的作用AI 解读

Summary This paper utilizes quarterly panel data for 20 OECD countries over the period 1975:Q1–2014:Q2 to explore the importance of house prices and credit in affecting the likelihood of a financial crisis. Estimating a set of multivariate logit models, we find that booms in credit to both households and non‐financial enterprises are important to account for when evaluating the stability of the financial system. In addition, we find that global housing market developments…

全球向量自回归模型的预测：贝叶斯方法AI 解读

Summary This paper develops a Bayesian variant of global vector autoregressive (B‐GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B‐GVAR models in terms of point and density forecasts for one‐quarter‐ahead and four‐quarter‐ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce…

欧元区与美国繁荣衰退的联动：贝叶斯面板马尔可夫转换VAR模型AI 解读

Summary The proposed panel Markov‐switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time‐varying transition matrices of country‐specific Markov chains, allowing for interconnections. An efficient multi‐move sampling algorithm draws time‐varying Markov‐switching chains. Using industrial production growth and credit spread data, several important data features are obtained. Three regimes appear, with slow growth becoming…

基于决策的模型组合下的最优投资组合选择AI 解读

Summary We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying…

非因果贝叶斯向量自回归AI 解读

Summary We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by‐product. We apply the methods to postwar US inflation and GDP growth. The noncausal model is found superior in terms of both in‐sample fit and out‐of‐sample forecasting performance over its conventional…

非线性Granger因果关系：多变量分析指南AI 解读

Summary We propose an extension of the bivariate nonparametric Diks–Panchenko Granger non‐causality test to multivariate settings. We first show that the asymptotic theory for the bivariate test fails to apply to the multivariate case, because the kernel density estimator bias and variance cannot both tend to zero at a sufficiently fast rate. To overcome this difficulty we propose to reduce the order of the bias by applying data sharpening prior to calculating the test…

消费预测：大量预测变量下消费者信心的实时作用AI 解读

Summary We study the role of consumer confidence in forecasting real personal consumption expenditure, and contribute to the extant literature in three substantive ways. First, we re‐examine existing empirical models of consumption and consumer confidence, not only at the quarterly frequency, but using monthly data as well. Second, we employ real‐time data in addition to commonly used revised vintages. Third, we investigate the role of consumer confidence in a rich…

含非期望产出的随机方向性技术距离函数：成本系统方法与美国银行应用AI 解读

Summary This paper offers a methodology to address the endogeneity of inputs in the directional technology distance function (DTDF)‐based formulation of banking technology which explicitly accommodates the presence of undesirable nonperforming loans—an inherent characteristic of the bank's production due to its exposure to credit risk. Specifically, we model nonperforming loans as an undesirable output in the bank's production process. Since the stochastic DTDF describing…

部门与地区冲击对价格设定的重要性AI 解读

Summary We use novel disaggregate sectoral‐regional euro‐area data to investigate the sources of price changes, introducing a new method to extract factors from overlapping data blocks that allows for estimation of aggregate, sectoral, country‐specific and regional components of price changes. Our sectoral component explains much less variation in disaggregate inflation rates and exhibits much less volatility and more persistence than previous findings for the US indicate.…

结合时变参数与混合频率：意大利政府支出乘数分析AI 解读

Summary In this paper, we propose a time‐varying parameter vector autoregression (VAR) model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (Drifts and volatilities: monetary policies and outcomes in the post WWII U.S. Review of Economic Studies 2005; 8 : 262–302) and Primiceri (Time varying structural vector autoregressions and…

异方差宏观经济模型的最优控制AI 解读

Summary This paper analyses the implications of heteroscedasticity for optimal macroeconomic policy and welfare. We find that changes in the variance structure driven by exogenous processes like generalized autoregressive conditional heteroscedasticity (GARCH) affect welfare but not the optimal feedback rule. However, changes in the variance structure driven by state‐dependent processes affect both. We also derive certainty‐equivalent transformations of state‐dependent…

异常值稳健的贝叶斯多项选择模型AI 解读

Summary A Bayesian method for outlier‐robust estimation of multinomial choice models is presented. The method can be used for both correlated as well as uncorrelated choice alternatives and guarantees robustness towards outliers in the dependent and independent variables. To account for outliers in the response direction, the fat‐tailed multivariate Laplace distribution is used. Leverage points are handled via a shrinkage procedure. A simulation study shows that estimation…

通过典型相关确定误差修正 VARMA 模型的长期与短期动态AI 解读

Summary This article studies a simple, coherent approach for identifying and estimating error‐correcting vector autoregressive moving average (EC‐VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short‐run VARMA dynamics, using the scalar component methodology. Finite‐sample performance is evaluated via Monte Carlo simulations and the approach is applied to…

利用专业预测估计贝叶斯向量自回归并开展预测AI 解读

Summary We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts…

PANICCA：结合横截面均值的面板非平稳性分析AI 解读

Summary The cross‐section average (CA) augmentation approach of Pesaran (A simple panel unit root test in presence of cross‐section dependence. Journal of Applied Econometrics 2007; 22 : 265–312) and Pesaran et al. (Panel unit root test in the presence of a multifactor error structure. Journal of Econometrics 2013; 175 : 94–115), and the principal components‐based panel analysis of non‐stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (A PANIC attack…

贝叶斯模糊断点回归分析与义务教育回报AI 解读

Summary This paper is concerned with the use of a Bayesian approach to fuzzy regression discontinuity (RD) designs for understanding the returns to education. The discussion is motivated by the change in government policy in the UK in April of 1947, when the minimum school leaving age was raised from 14 to 15—a change that had a discontinuous impact on the probability of leaving school at age 14 for cohorts who turned 14 around the time of the policy change. We develop a…

截面依赖指数：估计与推断AI 解读

Summary This paper provides a characterisation of the degree of cross‐sectional dependence in a two dimensional array, { x i t , i = 1,2,... N ; t = 1,2,..., T } in terms of the rate at which the variance of the cross‐sectional average of the observed data varies with N . Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of x t =( x 1 t , x 2 t ,..., x N t )′ rises with N . We represent the degree of…

含共同随机趋势的面板误差修正检验AI 解读

Summary This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

每日房价指数：构建、建模与较长期预测AI 解读

Summary We construct daily house price indices for 10 major US metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat‐sales method that closely mimics the methodology of the popular monthly Case–Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional…

老龄人口健康需求估计：灵活稳健的贝叶斯联合模型AI 解读

Summary We analyse two frequently used measures of the demand for health—hospital visits and out‐of‐pocket health care expenditure—which have been analysed separately in the existing literature. Given that these two measures of health demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out‐of‐pocket medical expenditure, which allows for the presence of nonlinear effects of covariates using splines to capture the…

用于估计动态微观经济模型的序贯蒙特卡洛方法AI 解读

Summary This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single‐agent dynamic discrete‐choice models and dynamic games of incomplete information. We propose a full‐solution…