Time-varying parameters (TVPs) models are frequently used in economics to model structural change. I show that they are in fact 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. The application requires the estimation of about 4600 TVPs, a task well within the reach of the new method.

翻译：时间变化参数( TVP) 模型经常在经济学中用于模拟结构变化。 我显示它们事实上是山脊回归。 这让计算、调试和实施比国家空间范式容易得多。 除其他外, 解决等效的双脊问题在计算上非常快, 即使高度, 关键“ 时间变异” 由交叉校验来调整。 变化波动用两步脊回归处理 。 我考虑了包含宽度的扩展( 参数各不相同和没有变化的算法选择) 和降级限制( 变量与要素模型捆绑在一起 ) 。 为了证明这个方法的有用性, 我用它来研究加拿大货币政策的演变。 应用需要估算大约 4600 TVP, 这是一项在新方法范围内的任务。