UC Riverside

This dissertation attempts to explore the monetary transmission mechanism with an innovating empirical framework: mixed-frequency factor-augumented vector autoregressive model, which allows to include a large number of economic variable with different frequencies. Extensions of the model are also provided: a stacked-vector system equips the model with classic impulse response function analysis; Markov-switching feature is added to the model to study the dynamics of unobservable states. The first chapter proposes a mixed-frequency version of the factor-augmented vector autoregressive regression (FAVAR) model, which is used to construct a coincident index to measure the monetary transmission mechanism. The model divides the transmission of changes in monetary policy to the economy into three stages according to the timing and order of the impact. Indicators of each stage are measured and identified using different data frequencies: fast-moving variables (stage 1, asset returns at the weekly frequency), intermediate moving variables (stage 2, credit market data at the monthly frequency), and slow-moving variables (stage 3, macroeconomic variables at the quarterly frequency). The resulting coincident index exhibits leading signal for all recessions in the sample period and provides implications on the dynamics of the monetary transmission mechanism. The second chapter extend the analysis to unconventional monetary policy. The FAVAR with mixed frequency model is used to account for monetary transmission mechanism when not only the federal fund rate is used as a monetary tool but also the Large-Scale Asset Purchase program. The impact of unconventional monetary policy is captured by "shadow rate" constructed using one-month forward rates. This allows a more complete analysis of the impact of monetary policy on the economy also during the more recent years since the 2008 financial crisis. The third chapter aims to examine the lead-lag relationship between channels in monetary transmission mechanism. A two-state Markov-switching mixed-frequency factor-augumented vector autoregressive model that allows each channel to switch states individually is estimated to analyze lead-lag relationship between different channels over time. This will shed light on how to track the impact of monetary policy shocks in real time.