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on Econometrics |
| By: | Lunsford, Kurt Graden (Federal Reserve Bank of Cleveland) |
| Abstract: | This paper develops a simple estimator to identify structural shocks in vector autoregressions (VARs) by using a proxy variable that is correlated with the structural shock of interest but uncorrelated with other structural shocks. When the proxy variable is weak, modeled as local to zero, the estimator is inconsistent and converges to a distribution. This limiting distribution is characterized, and the estimator is shown to be asymptotically biased when the proxy variable is weak. The F statistic from the projection of the proxy variable onto the VAR errors can be used to test for a weak proxy variable, and the critical values for different VAR dimensions, levels of asymptotic bias, and levels of statistical significance are provided. An important feature of this F statistic is that its asymptotic distribution does not depend on parameters that need to be estimated. |
| Keywords: | F Statistic; Productivity Shocks; Proxy Variable; Structural Vector Autoregression; TFP; Weak IV |
| JEL: | C12 C13 C32 C36 O47 |
| Date: | 2015–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1528&r=ecm |
| By: | Hanson, Gordon H. (UC San Diego and NBER); Lind, Nelson (UC San Diego); Muendler, Marc-Andreas (UC San Diego and NBER) |
| Abstract: | This paper characterizes the dynamic empirical properties of country export capabilities in order to inform modelling of the long-run behavior of comparative advantage. The starting point for our analysis is two strong empirical regularities in international trade that have previously been studied incompletely and in isolation to one another. The literature has noted a tendency for countries to concentrate exports in a few sectors. We show that this concentration arises from a heavy-tailed distribution of industry export capabilities that is approximately log normal and whose shape is stable across countries, sectors, and time. Likewise, previous research has detected a tendency for mean reversion in national industry productivities. We establish that mean reversion in export capability, rather than indicative of convergence in productivities or degeneracy in comparative advantage, is instead consistent with a well behaved stochastic growth process that delivers a stationary distribution of country export advantage. In literature on the growth of cities and firms, economists have used stochastic processes to study the determinants of the long-run size distributions. Our contribution is to develop an analogous empirical framework for identifying the parameters that govern the stationarydistribution of export capability. The main result of this analysis is that a generalized gamma distribution, which nests many commonly studied distributions, provides a tight fit of the data but log normality offers a reasonable approximation. Importantly, the stochastic process that generates log normality can be estimated in its discretized form by simple linear regression. Log linearity allows for an extension of our approach to multivariate diffusions, in which one can permit innovations to productivity to be transmitted intersectorally and internationally, as in recent models of trade and growth. |
| Keywords: | International trade; comparative advantage; generalized logistic diffusion; estimation of diffusion process JEL Classification: F14, F17, C22 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:252&r=ecm |
| By: | Jan Witajewski-Baltvilks (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Elena Verdolini (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Massimo Tavoni (Fondazione Eni Enrico Mattei (FEEM), Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) and Politecnico di Milano) |
| Abstract: | This paper aims at improving the application of the learning curve, a popular tool used for forecasting future costs of renewable technologies in integrated assessment models (IAMs). First, we formally discuss under what assumptions the traditional (OLS) estimates of the learning curve can deliver meaningful predictions in IAMs. We argue that the most problematic of them is the absence of any effect of technology cost on its demand (reverse causality). Next, we show that this assumption can be relaxed by modifying the traditional econometric method used to estimate the learning curve. The new estimation approach presented in this paper is robust to the reverse causality problem but preserves the reduced form character of the learning curve. Finally, we provide new estimates of learning curves for wind turbines and PV technologies which are tailored for use in IAMs. Our results suggest that the learning rate should be revised downward for wind power, but possibly upward for solar PV. |
| Keywords: | Learning Curve, Renewable Technologies, Integrated Assessment Models |
| JEL: | Q42 Q55 C26 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2015.65&r=ecm |
| By: | Xin Li; Carlos F. Tolmasky |
| Abstract: | We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitive to the sign of past returns, i.e. asymmetric feedback or leverage effect. However, in the long time scale (300 to 1000 days) clustering becomes the main factor. We study non-stationary features by using moving windows and find that clustering and leverage effects display time evolutions that are rather nontrivial. The structure of our model allows us to shed light on a few surprising facts recently found by Chicheportiche and Bouchaud. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.01916&r=ecm |