Oscar Jorda

Estimation and Inference of Impulse Responses by Local Projections

This paper introduces methods to compute impulse responses without specification and estimation of the underlying multivariate dynamic system. The central idea consists in estimating local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is done with 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) joint or point-wise analytic inference is simple; and (4) they easily accommodate experimentation with highly nonlinear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. Monte Carlo evidence and an application to a simple, closed-economy, new-Keynesian model clarify these numerous advantages.

A Model for the Federal Funds Rate Target

This paper is a statistical analysis of the manner in which the Federal Reserve determines the level of the federal funds rate target, one of the most publicized and anticipated economic indicators in the financial world. The paper introduces new statistical tools for forecasting a discrete-valued time series such as the target and suggests that these methods, in conjunction with a focus on the institutional details of how the target is determined, can significantly improve on standard vector autoregression forecasts of the effective federal funds rate. We further show that the news that the Fed has changed the target has statistical content substantially different from the news that the Fed failed to make an anticipated target change, causing us to challenge some of the conclusions drawn from standard linear VAR impulse-response functions.

IMPROVED TESTING AND SPECIFICATION OF SMOOTH TRANSITION REGRESSION MODELS

This paper extends previous work in Escribano and Jorda (1997) and introduces new LM specification procedures to choose between Logistic and Exponential Smooth Transition Regression (STR) Models. These procedures are simpler, consistent and more powerful than those previously available in the literature. An analysis of the properties of Taylor approximations around the transition function of STR models permits one to understand why these procedures work better and it suggests ways to improve tests of the null hypothesis of linearity versus the alternative of STR-type nonlinearity. Monte-Carlo experiments illustrate the performance of the different tests introduced. The new procedures are then implemented on a study of the dynamics of the U.S. unemployment rate.

Betting the House

Is there a link between loose monetary conditions, credit growth, house price booms, and financial instability? This paper analyzes the role of interest rates and credit in driving house price booms and busts with data spanning 140 years of modern economic history in the advanced economies. We exploit the implications of the macroeconomic policy trilemma to identify exogenous variation in monetary conditions: countries with fixed exchange regimes often see fluctuations in short-term interest rates unrelated to home economic conditions. We use novel instrumental variable local projection methods to demonstrate that loose monetary conditions lead to booms in real estate lending and house prices bubbles; these, in turn, materially heighten the risk of financial crises. Both effects have become stronger in the postwar era.

Sovereigns Versus Banks: Credit, Crises, and Consequences

Two separate narratives have emerged in the wake of the Global Financial Crisis. One interpretation speaks of private financial excess and the key role of the banking system in leveraging and deleveraging the economy. The other emphasizes the public sector balance sheet over the private and worries about the risks of lax fiscal policies. However, the two may interact in important and understudied ways. This paper examines the co-evolution of public and private sector debt in advanced countries since 1870. We find that in advanced economies significant financial stability risks have mostly come from private sector credit booms rather than from the expansion of public debt. However, we find evidence that high levels of public debt have tended to exacerbate the effects of private sector deleveraging after crises, leading to more prolonged periods of economic depression. We uncover three key facts based on our analysis of around 150 recessions and recoveries since 1870: (i) in a normal recession and recovery real GDP per capita falls by 1.5 percent and takes only 2 years to regain its previous peak, but in a financial crisis recession the drop is typically 5 percent and it takes over 5 years to regain the previous peak; (ii) the output drop is even worse and recovery even slower when the crisis is preceded by a credit boom; and (iii) the path of recovery is worse still when a credit-fueled crisis coincides with elevated public debt levels. Recent experience in the advanced economies provides a useful out- of-sample comparison, and meshes closely with these historical patterns. Fiscal space appears to be a constraint in the aftermath of a crisis, then and now.

Simultaneous Confidence Regions for Impulse Responses

Inference about an impulse response is a multiple testing problem with serially correlated coefficient estimates. This paper provides a method to construct simultaneous confidence regions for impulse responses and conditional bands to examine significance levels of individual impulse response coefficients given propagation trajectories. The paper also shows how to constrain a subset of impulse response paths to anchor structural identification and how to formally test the validity of such identifying constraints. Simulation and empirical evidence illustrate the new techniques. A broad summary of asymptotic analytic formulas is provided to make the methods easy to implement with commonly available statistical software.

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.

Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models

Abstract. A new LM specification procedure to choose between Logistic and Exponential Smooth Transition Autoregressive (STAR) models is introduced. The new decision rule has better properties than those previously available in the literature when the model is ESTAR and similar properties when the model is LSTAR. A simple natural extension of the usual LM-test for linearity is introduced and evaluated in terms of power. Monte-Carlo simulations and empirical evidence are provided in support of our claims.

Macrofinancial History and the New Business Cycle Facts

In advanced economies, a century-long, near-stable ratio of credit to GDP gave way to rapid financialization and surging leverage in the last forty years. This “financial hockey stick” coincides with shifts in foundational macroeconomic relationships beyond the widely noted return of macroeconomic fragility and crisis risk. Leverage is correlated with central business cycle moments, which we can document thanks to a decade-long international and historical data collection effort. More financialized economies exhibit somewhat less real volatility, but also lower growth, more tail risk, as well as tighter real-real and real-financial correlations. International real and financial cycles also cohere more strongly. The new stylized facts that we discover should prove fertile ground for the development of a new generation of macroeconomic models with a prominent role for financial factors.

The Harrod-Balassa-Samuelson Hypothesis: Real Exchange Rates and their Long-Run Equilibrium

Frictionless, perfectly competitive traded-goods markets justify thinking of purchasing power parity (PPP) as the main driver of exchange rates in the long-run. But differences in the traded/non-traded sectors of economies tend to be persistent and affect movements in local price levels in ways that upset the PPP balance (the underpinning of the Harrod-Balassa-Samuelson hypothesis, HBS). This paper uses panel-data techniques on a broad collection of countries to investigate the long-run properties of the PPP/HBS equilibrium using novel local projection methods for cointegrated systems. These semi-parametric methods isolate the long-run behavior of the data from contaminating factors such as frictions not explicitly modelled and thought to have effects only in the short-run. Absent the short-run effects, we find that the estimated speed of reversion to long-run equilibrium is much higher. In addition, the HBS effects means that the real exchange rate is converging not to a steady mean, but to a slowly to a moving target. The common failure to properly model this effect also biases the estimated speed of reversion downwards. Thus, the so-called