No abstract

Multinational corporation has changed their host countries. The new wave of FDI inflow attracted the interest of policymakers. FDI has significant effects on both productivity and carbon dioxide emissions. The host countries should carefully consider the advantages and disadvantages of FDI to their nation. The previous literature has not illustrated the global context's theoretical halo or haven pollution hypothesis. Using panel data of 96 countries between 2004 and 2014, our empirical results confirm the haven pollution hypothesis in both developing and developed countries. We employ the different general methods of moments (GMMs) to engage FDI in traditional STIRPAT theoretical frameworks. The empirical results contribute to the evidence of the EKC theory. The country's income level has been used to modify our models. The affluence of the economy, urbanization, FDI, and industrial sector would cause harmful effects on carbon dioxin emissions globally. The paper implies the two models which can be used for both developed and developing countries. The policymaker can use both short-run and long-run elasticities from those models to implicate their country's FDI inflow strategy.

I generalize the New Keynesian Phillips Curve model of Galí and Gertler (J Monet Econ 44:195-222, 1999) to allow for time-varying parameters. The parameter of interest measures the trade-off between inflation and real economic activity, and it is particularly a nonlinear function of three underlying structural parameters: the discount factor, the degree of price rigidity and the strength of backward-looking behavior or information diffusion. The empirical results show that the estimated parameter of output-inflation trade-off is time varying and is larger in high inflation periods. Specifically, the time-variation of the trade-off between inflation and economic activity stems from the degree of price rigidity, which is negatively correlated with inflation. Moreover, the forward-looking price-setting behavior plays a dominant role in explaining inflation dynamics for most part of the sample period. KeywordsStability • Time-varying parameter • Generalized method of moments • New Keynesian Phillips Curve JEL Classification E31 • E32 I am indebted to Shawn Ni, whose suggestions improved the quality of the paper. I thank Gianni G.

We study the dynamics of U.S. inflation persistence and the sources resulting to it over the "Great Inflation" and "Great Moderation" periods. It is different from most of the current studies in that we consider a Bayesian VAR model with the DSGE prior, the so-called DSGE-VAR approach, in which the prior economic information is coming from a small-scale New Keynesian DSGE model. In the recursive estimation of the model, we find a decline in the inflation persistence, measured by the "half-life" response of inflation to the monetary policy shock, in the early 1980s. The stance of monetary policy, particularly the aggressive attitude toward the monetary policy implementation, plays an important role in explaining the structural change of the inflation persistence.

I take a Bayesian approach to estimate and forecast the effects of fiscal stimulus in various versions of the model by Smets and Wouters (2007) for the US economy. Specifically, I proxy various simpler DSGE sub-models by imposing a tight prior on a single parameter or a combination of tight priors on multiple parameters in the Smets-Wouters model. I find that the present-value government spending multipliers obtained are all in a reasonable range. Moreover, I forecast the effect of fiscal stimulus in a scenario similar to the 2008/2009 recession in the US, where the public expects a large and temporary increase in government spending to stimulate a fragile economy. The forecasts, generated individually by a group of representative models, are weighted averaging by means of the posterior model probabilities that are computed on the basis of their corresponding marginal data densities. According to the Diebold-Mariano test, I find that the forecast error of the combination forecast, computed via Bayesian model averaging (BMA), is statistically larger than the individual forecast, obtained only from the one that has the best fit among those DSGE models.