Research
Working Papers
Should Macroeconomists Use Seasonally Adjusted Time Series? Structural Identification and Bayesian Estimation in Seasonal Vector Autoregressions
Revise and Resubmit at Review of Economics and Statistics
[PDF]
Abstract: When fitting structural vector autoregressions (VARs), macroeconomists should always prefer the original, unadjusted data over the seasonally adjusted versions. Seasonal adjustment will distort inferences about non-seasonal phenomena, such as structural parameters, impulse responses, and variance decompositions. This paper makes three contributions. First, I characterize how seasonal adjustment interferes with identification schemes in structural VARs, and how seasonal variation provides useful identifying information. Second, I provide a framework for Bayesian inference in seasonal VARs; the prior favors positive autocorrelation in season-specific means and spectral peaks at seasonal frequencies. Third, as an application, I incorporate seasonality into Baumeister and Hamilton's (2015) model of labor-market demand and supply. The model without seasonality is only partially identified, but the model with seasonality is fully identified and produces dramatically different empirical results.
Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks
With Keith O'Hara
Revise and Resubmit at Journal of Econometrics
[PDF]
Abstract: We introduce a new method for Bayesian estimation of fractionally integrated vector autoregressions (FIVARs). The FIVAR, which nests a standard VAR as a special case, allows each series to exhibit long memory, meaning that low frequencies can play a dominant role — a salient feature of many macroeconomic and financial time series. Although the parameter space is typically high-dimensional, our inferential procedure is computationally tractable and relatively easy to implement. We apply our methodology to the identification of technology shocks, an empirical problem in which business-cycle predictions depend on carefully accounting for low-frequency fluctuations.
Minimum Wages and Hours of Work
Revise and Resubmit at American Economic Journal: Macroeconomics
[PDF]
Abstract: I investigate, both theoretically and empirically, how minimum wages affect hours per worker. I document the fact that minimum-wage employees, on average, work longer hours when the minimum wage increases. To explain this pattern, I introduce a theoretical model of search and bargaining, subject to minimum-wage laws. Within a match, hours are determined by an upward-sloping labor-supply curve, so people are willing to work more when the minimum wage increases. However, higher wages diminish total profits, vacancy creation, and employment. I derive conditions under which minimum wages are welfare-improving and discuss empirical tests to determine whether those conditions are satisfied.
The Hazards of Unemployment: A Macroeconomic Model of Job Search and Résumé Dynamics
[PDF]
Abstract: I introduce a dynamic general-equilibrium model to investigate the relationship between the duration of unemployment and the probability of finding a job. Specifically, I analyze the hypothesis that a long unemployment spell sends a negative signal about a worker's quality, thereby affecting her probability of being hired. In the model, skills are unobservable. I refer to a worker's posterior probability of being highly skilled, conditional on her labor-market history, as the worker's résumé. Spending time in unemployment damages a worker's résumé, which alters her job-finding rate: Because high-skill workers are more likely to form matches, prospective employers infer that unmatched workers are less likely to be highly skilled. The match surplus incorporates the fact that hiring a worker improves her résumé, and the theory illustrates how the résumé value of being hired is priced into wages. I calibrate the model to match data on job-finding rates as a function of duration. The average worker experiences significant negative duration dependence, and incomplete information also generates considerable heterogeneity in job-finding rates. I extend the model to discuss how informational concerns interact with human capital decay as a source of duration dependence. Finally, I discuss the theory's empirical predictions and econometric implications.
Publications
Skill Flows: A Theory of Human Capital and Unemployment
Review of Economic Dynamics, Volume 31, January 2019
Published Version [DOI]
Final Working-Paper Version [PDF]
Abstract: I present a theoretical macroeconomic model that investigates the link between long-run growth and labor-market dynamics. Workers accumulate human capital on the job, while suffering human capital depreciation during unemployment. On the aggregate level, high unemployment hinders skill formation, creating a drag on growth. The model features endogenous growth, stochastic regime shifts, and a time-varying distribution of wages. Nevertheless, much of the model's value comes from the fact that it admits a sharp analytical characterization of the forces at work. I solve for a competitive equilibrium and derive conditions under which it will be efficient.
Posterior Sampling in Two Classes of Multivariate Fractionally Integrated Models: Corrigendum to Ravishanker, N. and B. K. Ray (1997) Australian Journal of Statistics 39 (3), 295-311
Australian & New Zealand Journal of Statistics, Volume 61, Issue 1, March 2019
With Keith O'Hara
Published Version [PDF]
Abstract: We discuss posterior sampling for two distinct multivariate generalizations of the univariate ARIMA model with fractional integration. The existing approach to Bayesian estimation, introduced by Ravishanker and Ray (1997), claims to provide a posterior-sampling algorithm for fractionally integrated vector autoregressive moving averages (FIVARMAs). We show that this algorithm produces posterior draws for vector autoregressive fractionally integrated moving averages (VARFIMAs), a model of independent interest that has not previously received attention in the Bayesian literature.