7 resultados para factor analytic model

em Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom


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National inflation rates reflect domestic and international (regional and global) influences. The relative importance of these components remains a controversial empirical issue. We extend the literature on inflation co-movement by utilising a dynamic factor model with stochastic volatility to account for shifts in the variance of inflation and endogenously determined regional groupings. We find that most of inflation variability is explained by the country specific disturbance term. Nevertheless, the contribution of the global component in explaining industrialised countries’ inflation rates has increased over time.

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We analyze and quantify co-movements in real effective exchange rates while considering the regional location of countries. More specifically, using the dynamic hierarchical factor model (Moench et al. (2011)), we decompose exchange rate movements into several latent components; worldwide and two regional factors as well as country-specific elements. Then, we provide evidence that the worldwide common factor is closely related to monetary policies in large advanced countries while regional common factors tend to be captured by those in the rest of the countries in a region. However, a substantial proportion of the variation in the real exchange rates is reported to be country-specific; even in Europe country-specific movements exceed worldwide and regional common factors.

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In a neoclassical growth model with monopolistic competition in the product market, the presence of cyclical factor utilization enhances the stabilization role of countercyclical taxes. The costs of varying capital utilization take the form of varying rates of depreciation, which in turn have amplifying effect on investment decisions as well as the volatility of most aggregate variables. This creates an additional channel through which taxes affect the economy, a channel that enhances the stabilization role of countercyclical taxes, with particularly strong effects in the labor market. However, in terms of welfare, countercyclical taxes are welfare inferior due to reduced precautionary saving motives.

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This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

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Most of the expansion of global trade during the last three decades has been of the North-South kind - between capital-abundant developed and labour-abundant developing countries. Based on this observation, I argue that the recent growth of world trade is best understood from a factor-proportions perspective. I present novel evidence documenting that differences in capital-labour ratios across countries have increased in the wake of two shocks to the global economy: i) the opening up of China and ii) financial globalisation and the resulting upstream capital flows towards capital-abundant regions. I analyse their impact on specialisation and the volume of trade in a dynamic model which combines factor-proportions trade in goods with international trade in financial assets. Calibrating this model, I find that it can account for 60% of world trade growth between 1980 and 2007. It is also capable of predicting international investment patterns which are consistent with the data

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This paper undertakes a normative investigation of the quantitative properties of optimal tax smoothing in a business cycle model with state contingent debt, capital-skill complementarity, endogenous skill formation and stochastic shocks to public consumption as well as total factor and capital equipment productivity. Our main finding is that an empirically relevant restriction which does not allow the relative supply of skilled labour to adjust in response to aggregate shocks, signi cantly changes the cyclical properties of optimal labour taxes. Under a restricted relative skill supply, the government fi nds it optimal to adjust labour income tax rates so that the average net returns to skilled and unskilled labour hours exhibit the same dynamic behaviour as under fl exible skill supply.

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This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.