作为 Ridge Regressions 的 Time-Varying Parameters
Time-Varying Parameters as Ridge Regressions
AI总结：
本文指出时变参数模型本质上是岭回归，从而简化了计算、调参和实现，并支持稀疏性与降秩扩展，高效应用于加拿大货币政策演变研究。
摘要：
Time-varying parameters (TVPs) 模型在经济学中常用于捕捉结构性变化。我强调了一个相当未被充分利用的事实——它们实际上是 ridge regressions。这使得计算、调参和实现比在 state-space 范式中容易得多。除其他方面外，求解等价的对偶 ridge 问题在高维情况下计算速度非常快，且关键的“时间变化量”通过交叉验证进行调参。波动的波动性通过两步 ridge regression 处理。我考虑了包含稀疏性（算法选择哪些参数变化，哪些不变化）和降秩限制（变化与因子模型相关联）的扩展。为了展示该方法的实用性，我使用大型时变局部投影来研究加拿大货币政策的演变。该应用需要估计约 4600 个 TVPs，这一任务完全在新方法的处理能力范围内。
英文摘要：
Time-varying parameters (TVPs) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact -- that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial "amount of time variation" is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections. The application requires the estimation of about 4600 TVPs, a task well within the reach of the new method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
