Konstantinos Theodoridis

Assessing the Economy-Wide Effects of Quantitative Easing

This paper examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England which started in March 2009. Although Bank Rate, the UK policy rate, was reduced to ½%, effectively its lower bound, the Bank’s Monetary Policy Committee felt that additional measures were necessary to meet the inflation target in the medium term. The policy of QE entailed buying private and mainly public assets in large quantities using central bank money, with the aim of injecting money into the economy and boosting nominal spending, in order to help achieve the Bank’s inflation target. Over the period from March 2009 to January 2010, the Bank of England purchased £200 billion of assets, mainly consisting of government securities. We attempt to quantify the effects of these purchases by focusing on the impact of lower long-term interest rates on the wider economy. This is motivated by empirical evidence indicating that QE purchases reduced long-term UK government bond yields by about 100 basis points. Other transmission channels are also possible, but are not considered in this paper. We use three different models to conduct counterfactual simulations to estimate the impact of QE on output and inflation: a large Bayesian VAR; a change-point structural VAR; and a time-varying parameter VAR. Our preferred average estimates from the three models suggest that QE may have had a peak effect on the level of real GDP of around 1½% and a peak effect on annual CPI inflation of about 1¼ percentage points. These estimates are shown to vary considerably across the different model specifications, and with the precise assumptions made to generate the counterfactual simulations, and are therefore subject to considerable uncertainty.

The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis

This article investigates if the impact of uncertainty shocks on the U.S. economy has changed over time. To this end, we develop an extended factor augmented vector autoregression (VAR) model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on a range of variables. We find that the impact of uncertainty shocks on real activity and financial variables has declined systematically over time. In contrast, the response of inflation and the short-term interest rate to this shock has remained fairly stable. Simulations from a nonlinear dynamic stochastic general equilibrium (DSGE) model suggest that these empirical results are consistent with an increase in the monetary authorities’ antiinflation stance and a “flattening” of the Phillips curve. Supplementary materials for this article are available online.

Forecasting UK GDP Growth, Inflation and Interest Rates under Structural Change: A Comparison of Models with Time-Varying Parameters

Evidence from a large and growing empirical literature strongly suggests that there have been changes in inflation and output dynamics in the United Kingdom. This is largely based on a class of econometric models that allow for time-variation in coefficients and volatilities of shocks. While these have been used extensively to study evolving dynamics and for structural analysis, there is little evidence on their usefulness in forecasting UK output growth, inflation and the short-term interest rate. This paper attempts to fill this gap by comparing the performance of a wide variety of time-varying parameter models in forecasting output growth, inflation and a short rate. We find that allowing for time-varying parameters can lead to large and statistically significant gains in forecast accuracy.

DSGE Model Restrictions for Structural VAR Identification

The identification of reduced-form VAR model had been the subject of numerous debates in the literature. Different sets of identifying assumptions can lead to very different conclusions in the policy debate. This paper proposes a theoretically consistent identification strategy using restrictions implied by a DSGE model. Monte Carlo simulations suggest the proposed identification strategy is successful in recovering the true structural shocks from the data. In the face of misspecified model restrictions, the data tend to push the identified VAR responses away from the misspecified model and closer to the true data generating process.

What do VARs Tell Us about the Impact of a Credit Supply Shock

This paper evaluates the performance of structural VAR models in estimating the impact of credit supply shocks. In a simple Monte-Carlo experiment, we generate data from a DSGE model that features bank lending and credit supply shocks and use SVARs to try and recover the impulse responses to these shocks. The experiment suggests that a proxy VAR that uses an instrumental variable procedure to estimate the impact of the credit shock performs well and is relatively robust to measurement error in the instrument. A structural VAR with sign restrictions also performs well under some circumstances. In contrast, VARs of the narrative variety, i.e. VAR models that include measures of the credit shock as endogenous variables are highly sensitive to ordering and measurement error. An application of the proxy VAR model and the VAR with sign restrictions to US data suggests, however, that the credit supply shock is hard to identify in practice.

Changing macroeconomic dynamics at the zero lower bound

ABSTRACT This article develops a change-point VAR model that isolates four major macroeconomic regimes in the US since the 1960s. The model identifies shocks to demand, supply, monetary policy, and spread yield using restrictions from a general equilibrium model. The analysis discloses important changes to the statistical properties of key macroeconomic variables and their responses to the identified shocks. During the crisis period, spread shocks became more important for movements in unemployment and inflation. A counterfactual exercise evaluates the importance of lower bond-yield spread during the crises and suggests that the Fed’s large-scale asset purchases helped lower the unemployment rate by about 0.6 percentage points, while boosting inflation by about 1 percentage point.

News and Labour Market Dynamics in the Data and in Matching Models

This paper uses a vector autoregression model estimated with Bayesian methods to identify the effect of productivity news shocks on labour market variables by imposing that they are orthogonal to current technology but they explain future observed technology. In the aftermath of a positive news shock, unemployment falls, whereas wages and the job finding rate increase. The analysis establishes that news shocks are important in explaining the historical developments in labour market variables, whereas they play a minor role for movements in real activity. We show that the empirical responses to news shocks are in line with those of a baseline search and matching model of the labour market and that the job destruction rate and real wage rigidities are critical for the variables’ responses to the news shock.

An Efficient Minimum Distance Estimator for DSGE Models

Recent studies illustrate that under some conditions dynamic stochastic general equilibrium models can be expressed as structural vector autoregressive models of infinite order. Based on this mapping and the theoretical results about vector autoregressive models of infinite order this paper proposes a minimum distance estimator that: A) matches the k-period responses of the whole vector of the observable variables described by the structural model – caused after a small perturbation to the entire vector of the structural errors – with those observed in the historical data, which have been recovered through the use of a structurally identified vector autoregressive model, and B) minimises the distance between the reduced-form error covariance matrix implied by the structural model and the one estimated in the data. This estimator encompasses those in the literature, is asymptotically consistent, normally distributed and efficient. The J-type overidentifying restrictions statistic that results from this methodology can be used for the evaluation of the structural model. Finally, this study also develops the theory of the bootstrapped version of the estimator and the statistic introduced here. Monte Carlo simulation evidences based on a medium-scale DSGE model reveal very encouraging results for the proposed estimator when it is compared against modern – Bayesian maximum likelihood – and less modern – maximum likelihood and non-efficient IR matching – DSGE estimators.

Unconventional Monetary Policies and the Macroeconomy: The Impact of the United Kingdom's QE2 and Funding for Lending Scheme

In this paper we assess the macroeconomic effects of two of the flagship unconventional monetary policies used by the Bank of England during the later stages of the global economic crisis: additional quantitative easing (QE) and the introduction of the Funding for Lending Scheme (FLS). We argue that these policies can be seen as complements, as QE effectively bypasses the banks by attempting to reduce risk-free yields directly in order to have a wider effect on asset prices, while FLS operates directly through banks by reducing their funding costs and increasing incentives to lend. We attempt to quantify the effects of these policies by estimating their impact on long-term interest rates and bank funding costs, respectively, and then tracing out their wider effects on the macroeconomy using simulations from a large Bayesian vector autoregression (VAR), which are cross-checked with a simpler auto-regressive distributed lag (ARDL) approach. We find that the second round of the Bank’s QE purchases during 2011-12 and the initial phase of the FLS each boosted GDP in the United Kingdom by around 0.5%-0.8%. Their effect on inflation was also broadly positive reaching around 0.6 percentage points, at its peak.

Do contractionary monetary policy shocks expand shadow banking

Using vector autoregressive models with either constant or time-varying parameters and stochastic volatility for the United States, we find that a contractionary monetary policy shock has a persistent negative impact on the asset growth of commercial banks, but increases the asset growth of shadow banks and securitisation activity. To explain this ‘waterbed’ effect, we propose a standard New Keynesian model featuring both commercial and shadow banking, and we show that the model comes close to explaining the empirical results. Our findings cast doubt on the idea that monetary policy can usefully ‘get in all the cracks’ of the financial sector in a uniform way.