Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

Beside a priori theoretical assumptions on instantaneous or long‐run effects of structural shocks, sign restrictions have become a prominent means for structural vector autoregressive (SVAR) analysis. Moreover, changes in second order moments of systems of time series can be fruitfully exploited for identification purposes in SVARs. By means of Monte Carlo studies, we examine to what degree theory‐based and statistical identification approaches offer an accurate quantification of the true structural relations in a standard model for monetary policy analysis. Subsequently, we discuss how identifying information from theory‐based and statistical approaches can be combined on the basis of a low‐dimensional empirical model of US monetary policy.     

Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks

In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. Unfortunately, these shocks may not have a meaningful structural economic interpretation. It is discussed how statistical and conventional identifying information can be combined. The discussion is based on a VAR model for the US containing oil prices, output, consumer prices and a short-term interest rate. The system has been used for studying the causes of the early millennium economic slowdown based on traditional identification with zero and long-run restrictions and using sign restrictions. We find that previously drawn conclusions are questionable in our framework.     

Structural VAR and financial networks: A minimum distance approach to spatial modeling

In this paper, I interpret a time series spatial model (T-SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the T-SAR from the SVAR estimates. I also develop a Wald-type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.     

Structural evolution of the postwar U.S. economy

We consider a time-varying parameter vector autoregressive model with stochastic volatility and mixture innovations to study the empirical relevance of the Lucas critique for the postwar U.S. economy. The model allows blocks of parameters to change at endogenously estimated points of time. Contrary to the Lucas critique, there are large changes at certain points of time in the parameters associated with monetary policy that do not correspond to changes in “reduced-form” parameters for inflation or the unemployment rate. However, the structure of the U.S. economy has evolved considerably over the postwar period, with an apparent reduction in the late 1980s in the impact of monetary policy shocks on inflation, though not on the unemployment rate. Related, we find changes in the Phillips curve tradeoff between inflation and cyclical unemployment (measured as the deviation from the time-varying steady-state unemployment rate implied by the model) in the 1970s and especially since the mid-1990s.     

Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Hodges–Lehmann Detection of Structural Shocks – An Analysis of Macroeconomic Dynamics in the Euro Area

Structural shocks in multivariate dynamic systems are hidden and often identified with reference to a priori economic reasoning. Based on a non‐Gaussian framework of independent shocks, this work provides an approach to discriminate between alternative identifying assumptions on the basis of dependence diagnostics. Relying on principles of Hodges–Lehmann estimation, we suggest a decomposition of reduced form covariance matrices that yields implied least dependent (structural) shocks. A Monte Carlo study underlines the discriminatory strength of the proposed identification strategy. Applying the approach to a stylized model of the Euro Area economy, independent shocks conform with features of demand, supply and monetary policy shocks.     

Revealing the preferences of the US Federal Reserve

We use Bayesian methods to estimate changes in US post‐war monetary policy in the Smets and Wouters model. We perform the estimations by allowing for a break in monetary policy at the time of Volcker's appointment as chairman. This enables us to capture changes in the monetary policy regime introduced by Volcker during the Volcker–Greenspan period. We find support for the assumption that monetary policy in the Volcker–Greenspan period performed optimally under commitment. Our estimation strategy allows us to estimate the preferences of the US Federal Reserve in the Volcker–Greenspan period, where the main objective of policy appears to be inflation, followed by interest rate stabilization, output growth and interest rate smoothing. We find that the Great Moderation of output growth is explained by a combination of two factors: the decrease in the volatility of the structural shocks and the improved monetary policy conduct. Inflation Stabilization, however, is mainly due to the change in monetary policy that took place at the beginning of Volcker's mandate. Copyright © 2010 John Wiley & Sons, Ltd.     

Solving endogenous regime switching models

This paper solves rational expectations models in which structural parameters switch across multiple regimes according to state-dependent (endogenous) transition probabilities. Assuming small shocks and smooth transition probabilities, we apply a perturbation approach. We first provide for conditions under which a unique bounded equilibrium exists. We then compute first- and second-order approximations. In a new-Keynesian model with monetary policy switching, we document new effects of monetary policy switching when transition probabilities depend on inflation.     

Indirect inference with time series observed with error

We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. When the condition for identification is satisfied, the structural and measurement error parameters can be consistently estimated. The issues of identification and misspecification of measurement error are discussed in detail. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure noise.     

Monetary policy regimes and the term structure of interest rates

US monetary policy is investigated using a regime-switching no-arbitrage term structure model that relies on inflation, output, and the short interest rate as factors. The model is complemented with a set of assumptions that allow the dynamics of the private sector to be separated from monetary policy. The monetary policy regimes cannot be estimated if the yield curve is ignored during estimation. Counterfactual analysis evaluates importance of regimes in policy and shocks for the great moderation. The low-volatility regime of exogenous shocks plays an important role. Monetary policy contributes by trading off asymmetric responses of output and inflation under different regimes.     

The effects of U.S. monetary policy on Colombia and Panama (2002–2007)

I study the economies of Colombia (floating exchange rate) and Panama (dollarized) to illustrate how the monetary policy of a large economy can export capital structure distortions to small open economies that follow different exchange rate regimes. The paper contributes to the literature on international business cycles in two ways. First, it adds to recent research that extends the Mises–Hayek business cycle theory to an international context. Second, most current research abstracts from effects on the production structures of emerging market economies when analyzing the transmission of monetary policy shocks. This paper seeks to fill this gap by studying structural effects of U.S. monetary policy on the economies of Colombia (floating exchange rate) and Panama (dollarized).     

Sources of the great moderation: A time-series analysis of GDP subsectors

Recent work finds evidence that the volatility of the U.S. economy fell dramatically around the first quarter of 1984. We trace the timing of this so-called “Great Moderation” across many subsectors of the economy in order to better understand its root cause. We find that the interest rate sensitive sectors generally experience a much earlier volatility decline than other large sectors of the economy. The changes in Federal Reserve stabilization policies that occurred during the early 1980s support the view that an improved monetary policy played an important role in stabilizing real economic activity. We find only mild evidence that “good luck” was important and little evidence to support the claim that improved inventory management was important.     

Identification of simultaneous equation models with measurement error

This paper establishes identification conditions for a simultaneous equation model in which some of the exogenous variables are measured with error. It is assumed that observational information is confined to the covariance matrix of the observed variables and that prior information on the structural coefficients and error variances takes the form of zero restrictions. The primary result is an easily-applied assignment condition for checking whether or not there are an adequate number and variety of prior restrictions to identify the structural parameters.     

货币政策房地产市场传导机制研究--基于SVAR模型的实证分析

基于2000-2013年房地产市场相关月度时间序列数据,构建货币政策房地产市场传导机制的理论框架,建立8个变量的结构向量自回归模型（SVAR）分析货币政策、房地产供给需求和消费投资等实体经济变量的相互关系,研究表明货币政策尤其是利率政策能显著影响房地产供给需求,但房地产需求对消费的正向冲击影响十分有限,对投资反而产生负向冲击。由此提出,利率政策应当关注房地产价格波动,避免房地产价格非理性上涨对宏观经济的负面影响。     

Evaluating monetary policy of the euro area with cross-country heterogeneity: Evidence from a New Keynesian model

This paper empirically investigates the optimal monetary policy conduct for the euro area in the presence of heterogeneous economic conditions across member states. Based on the New Keynesian monetary framework, we compare welfare losses under the assumption that the central bank conducts monetary policy using area-wide aggregate data against the alternative assumption that the central bank exploits country-specific as opposed to area-wide data. Empirical results reveal a sizable gain in stabilization performance if the European Central Bank formulates monetary policy by explicitly taking into account cross-country heterogeneity within the euro area. The estimated gain is more pronounced in a hybrid variant than in the purely forward-looking version of the New Keynesian model.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

Inflation and output volatility under asymmetric incomplete information

The assumption of asymmetric and incomplete information in a standard New Keynesian model creates strong incentives for monetary policy transparency. We assume that the central bank has better information about its objectives than the private sector, and that the private sector has better information about shocks than the central bank. Transparency has the potential to trigger a virtuous circle in which all agents find it easier to make inferences and the economy is better stabilised. Our analysis improves upon existing work by endogenising the volatility of both output and inflation. Improved transparency most likely manifests itself in falling output volatility.     

Structural vulnerability and resilience to currency crisis: Foreign currency debt versus export

Is there any factor that is not analyzed in the literature but is important for preventing currency crises? I argue that exports are an important factor to prevent currency crises that has not been frequently analyzed in the existing theoretical literature. Using the third generation model of currency crises, I derive a simple and intuitive formula that captures an economy’s structural vulnerability characterized by the elasticity of exports and repayments for foreign currency denominated debt. I graphically show that the possibility of currency crisis equilibrium depends on this structural vulnerability and also analyze how this vulnerability impacts the effectiveness of monetary policy response.     

The macroeconomic impact of economic uncertainty and financial shocks under low and high financial stress

External financial frictions might increase the severity of economic uncertainty shocks. We analyze the impact of aggregate uncertainty and financial condition shocks using a threshold vector autoregressive (TVAR) model with stochastic volatility during distinct US financial stress regimes. We further examine the international spillover of the US financial shock. Our results show that the peak contraction in euro area industrial production due to uncertainty shocks during a financial crisis is nearly-four times larger than the peak contraction during normal times. The US financial shocks have an influential asymmetric spillover effect on the euro area. Furthermore, the estimates reveal that the European Central Bank (ECB) is more cautious in implementing a monetary policy against uncertainty shocks while adopting hawkish monetary policies against financial shocks. In contrast, the Fed adopts a more hawkish monetary policy during heightened uncertainty, whereas it acts more steadily when financial stress rises in the economy.