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Browsing Economics Working papers by Author "Doko Tchatoka, F."
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Item Metadata only Monetary policy and indeterminacy after the 2001 slump(Centre for Applied Macroeconomic Analysis, 2016) Doko Tchatoka, F.; Groshenny, N.; Haque, Q.G.; Weder, M.This paper estimates a New Keynesian model of the U.S. economy over the period following the 2001 slump, a period for which the adequacy of monetary policy is intensely debated. To relate to this debate, we consider three alternative empirical inflation series in the estimation. When using CPI or PCE, we find some support for the view that the Federal Reserve’s policy was extra easy and may have led to equilibrium indeterminacy. Instead, when measuring inflation with core PCE, monetary policy appears to have been reasonable and sufficiently active to rule out indeterminacy. We then relax the assumption that inflation in the model is measured by a single indicator. We re-formulate the artificial economy as a factor model where the theory’s concept of inflation is the common factor to the three empirical inflation series. We find that CPI and PCE provide better indicators of the latent concept while core PCE is less informative. Again, this procedure cannot dismiss indeterminacy.Item Metadata only On bootstrapping tests of equal forecast accuracy for nested models(Centre for Applied Macroeconomic Analysis, Australian National University, 2020) Doko Tchatoka, F.; Haque, Q.The asymptotic distributions of the recursive out-of-sample forecast accuracy test statistics depend on stochastic integrals of Brownian motion when the models under comparison are nested. This often complicates their implementation in practice because the computation of their asymptotic critical values is costly. Hansen and Timmermann (2015, Econometrica) propose a Wald approximation of the commonly used recursive Fstatistic and provide a simple characterization of the exact density of its asymptotic distribution. However, this characterization holds only when the larger model has one extra predictor or the forecast errors are homoscedastic. No such closed-form characterization is readily available when the nesting involves more than one predictor and heteroskedasticity is present. We first show both the recursive F-test and its Wald approximation have poor finite-sample properties, especially when the forecast horizon is greater than one. We then propose a hybrid bootstrap method consisting of a block moving bootstrap (which is nonparametric) and a residual based bootstrap for both statistics, and establish its validity. Simulations show that our hybrid bootstrap has good finite-sample performance, even in multi-step ahead forecasts with heteroscedastic or autocorrelated errors, and more than one predictor. The bootstrap method is illustrated on forecasting core inflation and GDP growth.Item Open Access Testing for stochastic dominance in social networks(University of Adelaide, School of Economics, 2017) Doko Tchatoka, F.; Garrard, R.; Masson, V.This paper illustrates how stochastic dominance criteria can be used to rank social networks in terms of efficiency, and develops statistical inference procedures for as- sessing these criteria. The tests proposed can be viewed as extensions of a Pearson goodness-of-fit test and a studentized maximum modulus test often used to partially rank income distributions and inequality measures. We establish uniform convergence of the empirical size of the tests to the nominal level, and show their consistency under the usual conditions that guarantee the validity of the approximation of a multinomial distribution to a Gaussian distribution. Furthermore, we propose a bootstrap method that enhances the finite-sample properties of the tests. The performance of the tests is illustrated via Monte Carlo experiments and an empirical application to risk sharing networks in rural India