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Working Paper
Inflation as a global phenomenon - some implications for policy analysis and forecasting
We evaluate the performance of inflation forecasts based on the open-economy Phillips curve by exploiting the spatial pattern of international propagation of inflation. We model these spatial linkages using global inflation and either domestic slack or oil price fluctuations, motivated by a novel interpretation of the forecasting implications of the workhorse openeconomy New Keynesian model (Martnez-Garca and Wynne (2010), Kabukcuoglu and Martnez-Garca (2014)). We find that incorporating spatial interactions yields significantly more accurate forecasts of local inflation in 14 advanced ...
Newsletter
Measuring the Effects of the Covid-19 Delta Wave on the U.S. Hourly Labor Market
In this article, we take a closer look at the implications of rising Covid-19 cases and vaccination rates for the U.S. hourly labor market. To do so, we rely on geographic variation in the high-frequency data collected by the firm Homebase with its timekeeping software. This data source allows us to make use of U.S. state-level variation on a daily basis in order to decompose the effects on hourly employees and hours worked from both rising cases and vaccinations.
Working Paper
How Much Should We Trust Regional-Exposure Designs?
Many prominent studies in macroeconomics, labor, and trade use panel data on regions to identify the local effects of aggregate shocks. These studies construct regional-exposure instruments as an observed aggregate shock times an observed regional exposure to that shock. We argue that the most economically plausible source of identification in these settings is uncorrelatedness of observed and unobserved aggregate shocks. Even when the regression estimator is consistent, we show that inference is complicated by cross-regional residual correlations induced by unobserved aggregate shocks. We ...
Working Paper
Pre-event Trends in the Panel Event-study Design
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (?pre-trends?) in the outcome. Alternative approaches perform poorly in our simulations
Working Paper
Non-linearity in the Inflation-Growth Relationship in Developing Economies: Evidence from a Semiparametric Panel Model
Using data on developing economies, we estimate a flexible semiparametric panel data model that incorporates potentially nonlinear effects of inflation on economic growth. We find that inflation is associated with significantly lower growth only after it reaches about 12 percent, which is notably lower than the comparable estimate obtained from a threshold model. Our results also suggest that models with restrictive functional form assumptions tend to underestimate marginal effects of inflation on economic growth. We also document significant variation in the effect of inflation on growth ...
Report
Identifying inflation's grease and sand effects in the labor market
Inflation has been accused of causing distortionary prices and wage fluctuations (sand) as well as lauded for facilitating adjustments to shocks when wages are rigid downwards (grease). This paper investigates whether these two effects can be distinguished from each other in a labor market by the following identification strategy: inflation-induced deviations among employer's mean wage-changes represent unintended intramarket distortions (sand), while inflation-induced, inter-occupational wage-changes reflect intended alignments with intermarket forces (grease). Using a unique 40-year panel ...
Working Paper
Oil, Volatility and Institutions: Cross-Country Evidence from Major Oil Producers
This paper examines the long-run effects of oil revenue and its volatility on economic growth as well as the role of institutions in this relationship. We collect annual and monthly data on a sample of 17 major oil producers over the period 1961-2013, and use the standard panel autoregressive distributed lag (ARDL) approach as well as its cross-sectionally augmented version (CS-ARDL) for estimation. Therefore, in contrast to the earlier literature on the resource curse, we take into account all three key features of the panel: dynamics, heterogeneity and cross-sectional dependence. Our ...
Working Paper
A Dummy Test of Identification in Models with Bunching
We propose a simple test of the main identification assumption in models where the treatment variable takes multiple values and has bunching. The test consists of adding an indicator of the bunching point to the estimation model and testing whether the coefficient of this indicator is zero. Although similar in spirit to the test in Caetano (2015), the dummy test has important practical advantages: it is more powerful at detecting endogeneity, and it also detects violations of the functional form assumption. The test does not require exclusion restrictions and can be implemented in many ...
Working Paper
Debt, inflation and growth robust estimation of long-run effects in dynamic panel data models
This paper investigates the long-run effects of public debt and inflation on economic growth. Our contribution is both theoretical and empirical. On the theoretical side, we develop a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in dynamic heterogeneous panel data models with cross-sectionally dependent errors. The relative merits of the CS-DL approach and other existing approaches in the literature are discussed and illustrated with small sample evidence obtained by means of Monte Carlo simulations. On the empirical side, using data on a ...
Report
Estimating dynamic panel models: backing out the Nickell Bias
We propose a novel estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the first-order conditions as a function of the autoregressive parameter and solving the resulting equation. We show that this estimator performs well as compared with approaches in current use. We also propose a general method for including predetermined variables in fixed-effects panel regressions that appears to perform well.