Short-run and long-run comovement of GDP and some expenditure aggregates in Germany, France and Italy
TLDR
In this paper, the authors present empirical work on short-run and long-run comovement between the German, French and Italian GDP as well as the aggregates of private consumption, business investment, exports, imports, and changes in inventories.Abstract:
The paper presents empirical work on short-run and long-run comovement between the German, French and Italian GDP as well as the aggregates of private consumption, business investment, exports, imports, and changes in inventories. In country-specific data sets, cointegration analyses are carried out both to identify long-run economic relationships and to remove the trend components from the nonstationary series. Analytically, this is done by reparametrizing the vector error correction model in its common trends representation. The resulting (Beveridge-Nelson) trend and cycle components as well as the series of changes in inventories are analyzed with a focus on synchronicity. To measure cross-country comovement at different frequencies, “cohesion”, a summary statistic developed by Croux et al. (2001), is applied. Sampling variability and parameter uncertainty are captured by bootstrapped confidence intervals.read more
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A comparison of the effects of exogenous oil supply shocks on output and inflation in the G7 countries
TL;DR: A comparison of the effects of exogenous shocks to global crude oil production on seven major industrialized economies suggests a fair degree of similarity in the real growth responses as discussed by the authors, which is consistent with a monetary explanation of the inflation of the 1970s.
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The Effects of Exogenous Oil Supply Shocks on Output and Inflation: Evidence from the G7 Countries
Lutz Kilian,Lutz Kilian +1 more
TL;DR: In this article, the authors compared the effects of exogenous shocks to global oil production on seven major industrialized economies and found that there is a fair degree of similarity in the real growth responses.
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On trend-cycle decomposition and data revision
TL;DR: In this paper, the authors give a novel explanation for the negative correlation between trend and cycle innovations originating from the Jacobs-van Norden (2011) data revision model and discuss economic interpretations and implications, including ltering and smoothing properties.
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Who do you trust while bubbles grow and blow? A comparative analysis of the explanatory power of accounting and patent information for the market values of German firms
Fred Ramb,Markus Reitzig +1 more
TL;DR: In this paper, the authors present a theoretical and empirical analysis of the fitness of national German and international accounting information, as well as European patent data to explain the market values of German manufacturing firms.
References
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Co-integration and Error Correction: Representation, Estimation and Testing
TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
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Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
TL;DR: In this paper, a test of the null hypothesis that an observable series is stationary around a deterministic trend is proposed, where the series is expressed as the sum of deterministic trends, random walks, and stationary error.
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Time series analysis
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models / Søren Johansen
TL;DR: In this paper, the authors present the likelihood methods for the analysis of cointegration in VAR models with Gaussian errors, seasonal dummies, and constant terms, and show that the asymptotic distribution of the maximum likelihood estimator is mixed Gausssian.