Model-Free Impulse Responses

This paper introduces methods for computing impulse response functions that do not require specification and estimation of the unknown dynamic multivariate system itself. The central idea behind these methods is to estimate flexible local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is usually done in vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) standard error calculation is direct; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. An application to a simple, closed-economy monetary model suggests that the output loss and inflation effects of an interest rate shock depend on the stage of the business cycle.impulse response function, local projection, vector autoregression, nonlinear

Model-Free Impulse Responses

This paper introduces methods for computing impulse response functions that do not require specification and estimation of the unknown dynamic multivariate system itself. The central idea behind these methods is to estimate flexible local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is usually done in vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) standard error calculation is direct; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. An application to a simple, closed-economy monetary model suggests that the output loss and inflation effects of an interest rate shock depend on the stage of the business cycle.impulse response function, local projection, vector autoregression, nonlinear

Model-Free Impulse Responses

This paper introduces methods for computing impulse response functions that do not require specification and estimation of the unknown dynamic multivariate system itself. The central idea behind these methods is to estimate flexible local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is usually done in vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) standard error calculation is direct; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. An application to a simple, closed-economy monetary model suggests that the output loss and inflation effects of an interest rate shock depend on the stage of the business cycle.impulse response function, local projection, vector autoregression, nonlinear

Carry Trade

To appear in the Encyclopedia of Financial Globalizationcarry trade, uncovered interest rate parity, purchasing power parity, arbitrage

Decision Rules for Selecting between Exponential and Logistic STAR

A new LM specification procedure to choose between Logistic and Exponential Smooth Transition Autoregressive (STAR) models is introduced. This procedure has better consistency and power properties than that previously available in the literature. Monte-Carlo simulations and empirical evidence are provided in support of our claims.STAR, expontenial

Estimation and Inference by the Method of Projection Minimum Distance

A covariance-stationary vector of variables has a Wold representation whose coefficients can be semiparametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of minimum distance to estimate model parameters. We call this estimator projection minimum distance (PMD) and show that its parameter estimates are consistent and asymptotically normal. In many cases, PMD is asymptotically equivalent to maximum likelihood estimation (MLE) and nests GMM as a special case. In fact, models whose ML estimation would require numerical routines (such as VARMA models) can often be estimated by simple least-squares routines and almost as efficiently by PMD. Because PMD imposes no constraints on the dynamics of the system, it is often consistent in many situations where alternative estimators would be inconsistent. We provide several Monte Carlo experiments and an empirical application in support of the new techniques introduced.impulse response, local projection, minimum chi-square, minimum distance

Measuring Monetary Policy Interdependence

This paper measures the degree of monetary policy interdependence between major industrialized countries from a new perspective. The analysis uses a special data set on central bank issued policy rate targets for 14 OECD countries. Methodologically, our approach is novel in that we separately examine monetary interdependence due to (1) the coincidence in time of when policy actions are executed from (2) the nature and magnitude of the policy adjustments made. The first of these elements requires that the timing of events be modeled with a dynamic discrete duration design. The discrete nature of the policy rate adjustment process that characterizes the second element is captured with an ordered response model. The results indicate there is significant policy interdependence among these 14 countries during the 1980-1998 sample period. This is especially true for a number of European countries which appeared to respond to German policy during our sample period. A number of other countries appeared to respond to U.S. policy, though this number is smaller than that suggested in preceding studies. Moreover, the policy harmonization we find appears to work through channels other than formal coordination agreements.policy, interdependance

Measuring Systematic Monetary Policy

The 1970s and early 1980s witnessed two main approaches to the analysis of monetary policy. The first is the early new classical approach of Lucas, based on the assumptions of rational expectations and market clearing. The second is the atheoretical econometrics of Sims’s VAR program. Both have developed: the new classical approach has been enriched through various accounts of price stickiness, cost of adjustment or alternative expectational schemes; the original VAR program has developed into the structural VAR program. This paper clarifies the relationship between these two programs. Based on work of Cochrane (1998), it shows that the typical method of evaluating unanticipated, unsystematic monetary policy is correct only if the conditions necessary for Lucas’s policy-ineffectiveness proposition hold, while recent methods for evaluating systematic monetary policy violate Lucas’s policy-noninvariance proposition (“the Lucas critique”). The paper shows how to construct and estimate (using regime changes) a model in which some agents form rational-expectations and others follow rules of thumb. In such a model, monetary policy actions can be validly decomposed into systematic and unsystematic components and valid counterfactual experiments on alternative systematic monetary-policy rules can be evaluated.monetary policy

Measuring Systematic Monetary Policy

The 1970s and early 1980s witnessed two main approaches to the analysis of monetary policy. The first is the early new classical approach of Lucas, based on the assumptions of rational expectations and market clearing. The second is the a theoretical econometrics of Simsâ??s VAR program. Both have developed: the new classical approach has been enriched through various accounts of price stickiness, cost of adjustment or alternative expectational schemes; the original VAR program has developed into the structural VAR program. This paper clarifies the relationship between these two programs. Based on work of Cochrane (1998), it shows that the typical method of evaluating unanticipated, unsystematic monetary policy is correct only if the conditions necessary for Lucasâ??s policy-ineffectiveness proposition hold, while recent methods for evaluating systematic monetary policy violate Lucasâ??s policy-noninvariance proposition (â??the Lucas critiqueâ??). The paper shows how to construct and estimate (using regime changes) a model in which some agents form rational-expectations and others follow rules of thumb. In such a model, monetary policy actions can be validly decomposed into systematic and unsystematic components and valid counterfactual experiments on alternative systematic monetary-policy rules can be evaluated.

Projection Minimum Distance: An Estimator for Dynamic Macroeconomic Models

This paper introduces an estimator for dynamic macroeconomic models where possibly the dynamics and the variables described therein are incomplete representations of a larger, unknown macroeconomic system. We call this estimator projection minimum distance (PMD) and show that it is consistent and asymptotically normal. Many times, PMD can provide consistent estimates of structural parameters even when the dynamics of the macroeconomic model are insufficient to account for the serial correlation of the data or correlation with information omitted from the model. PMD provides an overall specification chi-squared test based on the distance between the impulse responses of the model and their semi-parametric estimates from the data. PMD only requires two, simple, least-squares steps and can be generalized to more complex, nonlinear environments.