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Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad (2023-01-18)
This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses the issue of whether weighted or unweighted observations should be used at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches, we focus on the One Covariate at a time ...
Globalization Institute Working Papers , Paper 394

Working Paper
Monetary Policy, Self-Fulfilling Expectations and the U.S. Business Cycle
Nicolo, Giovanni (2020-05-05)
I estimate a medium-scale New-Keynesian model and relax the conventional assumption that the central bank adopted an active monetary policy by pursuing inflation and output stability over the entire post-war period. Even after accounting for a rich structure, I find that monetary policy was passive prior to the Volcker disinflation. Sunspot shocks did not represent quantitatively relevant sources of volatility. By contrast, such passive interest rate policy accommodated fundamental productivity and cost shocks that de-anchored inflation expectations, propagated via self-fulfilling inflation ...
Finance and Economics Discussion Series , Paper 2020-035

Working Paper
Latent Variables Analysis in Structural Models: A New Decomposition of the Kalman Smoother
Chung, Hess T.; Fuentes-Albero, Cristina; Paustian, Matthias; Pfajfar, Damjan (2020-12-04)
This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables, like the data decomposition. However, by taking into account the transmission mechanisms of each type of shock, we can highlight the economic structure underlying the relationship between the data and the latent variables. We demonstrate the usefulness of this approach by detailing the role of ...
Finance and Economics Discussion Series , Paper 2020-100

Report
Inflation in the Great Recession and New Keynesian models
Giannoni, Marc; Del Negro, Marco; Schorfheide, Frank (2013)
It has been argued that existing DSGE models cannot properly account for the evolution of key macroeconomic variables during and following the recent great recession. We challenge this argument by showing that a standard DSGE model with financial frictions available prior to the recent crisis successfully predicts a sharp contraction in economic activity along with a modest and protracted decline in inflation following the rise in financial stress in the fourth quarter of 2008. The model does so even though inflation remains very dependent on the evolution of economic activity and of monetary ...
Staff Reports , Paper 618

Report
U.S. wage and price dynamics: a limited information approach
Sbordone, Argia M. (2006-08-01)
This paper analyzes the dynamics of prices and wages using a limited information approach to estimation. I estimate a two-equation model for the determination of prices and wages derived from an optimization-based dynamic model in which both goods and labor markets are monopolistically competitive; prices and wages can be reoptimized only at random intervals; and, when prices and wages are not reoptimized, they can be partially adjusted to previous-period aggregate inflation. The estimation procedure is a two-step minimum distance estimation that exploits the restrictions imposed by the model ...
Staff Reports , Paper 256

Working Paper
When Is the Use of Gaussian-inverse Wishart-Haar Priors Appropriate?
Inoue, Atsushi; Kilian, Lutz (2024-07-09)
Several recent studies have expressed concern that the Haar prior typically employed in estimating sign-identified VAR models is driving the prior about the structural impulse responses and hence their posterior. In this paper, we provide evidence that the quantitative importance of the Haar prior for posterior inference has been overstated. How sensitive posterior inference is to the Haar prior depends on the width of the identified set of a given impulse response. We demonstrate that this width depends not only on how much the identified set is narrowed by the identifying restrictions ...
Working Papers , Paper 2404

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Herbst, Edward P.; Bognanni, Mark (2014-11-12)
Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, ...
Working Papers (Old Series) , Paper 1427

Working Paper
Understanding the Estimation of Oil Demand and Oil Supply Elasticities
Kilian, Lutz (2020-09-03)
This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the ...
Working Papers , Paper 2027

Working Paper
Evaluating Conditional Forecasts from Vector Autoregressions
Clark, Todd E.; McCracken, Michael W. (2014-09-01)
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout ...
Working Papers , Paper 2014-25

Working Paper
The Role of the Prior in Estimating VAR Models with Sign Restrictions
Inoue, Atsushi; Kilian, Lutz (2020-12-03)
Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have been used in the literature to illustrate this potential problem are invalid. Specifically, we show that it does not make sense from a Bayesian point of view to characterize the impulse response prior based on the distribution of the impulse responses conditional on the maximum likelihood estimator of ...
Working Papers , Paper 2030

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