Do Panels Help Solve the Purchasing Power Parity Puzzle?
面板数据有助于解决购买力平价之谜吗?AI 解读 ↓
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面板数据有助于解决购买力平价之谜吗?AI 解读 ↓
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Although Rogoff described the “remarkable consensus” of 3- to 5-year half-lives of purchasing power parity (PPP) deviations among studies using long-horizon data, recent works using panel methods with post-1973 data report shorter half-lives of 2 to 2.5 years. But these studies do not use appropriate techniques to measure persistence. We extend median-unbiased estimation methods to the panel context, calculate both point estimates and confidence intervals, and provide strong evidence confirming Rogoff's original claim. Although panel regressions provide more information on the persistence of real exchange rate shocks than univariate regressions, they do not help solve the PPP puzzle.
条件线性的矩阵正态状态空间模型
We develop a class of linear state space models for matrix-valued time series data where the state is a latent matrix normal process. We derive matrix versions of the Kalman filter, log-likelihood, and smoother enabling estimation of the latent state matrix as well as the model's parameters. To conduct Bayesian inference, we provide algorithms that draw from the joint posterior distribution of the latent state matrices conditional on the observed data and parameters. We…
使用 R 包 bpvars 的贝叶斯面板向量自回归预测
The R package bpvars was designed to forecast employment, unemployment, and labour market participation rates of 189 countries. However, it is generally applicable to dynamic panel data due to the flexibility of its modelling framework and robust coding. It includes a family of Bayesian hierarchical panel Vector Autoregressions (VARs) that are characterised by: (i) country-specific VAR models (ii) with their parameters' priors centred around their global counterparts, and…
Summary The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption, ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends the standard SCM to account for spillovers and estimate both treatment and spillover effects. The…
气候敏感度是否因地区而异?变系数方法
Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that have different local geographical features experience unequal increases in temperature. In this paper, we develop a varying-coefficient dynamic panel data model and use it to measure local climate sensitivity, defined as the increase in temperature in a given location following a doubling of CO2…
非平稳宏观数据反事实分析的合成经济周期方法
This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic…
在 Stata 中检验和估计时间序列与面板数据的结构突变
Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007--2008 financial crisis and the 2020 COVID--19 outbreak. Detecting the existence of breaks, and dating them is therefore necessary, not only for estimation purposes but also for understanding drivers of change and their effect on…