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Wenn die AfD hier gewinnt, wären die Folgen überall in Deutschland deutlich zu spürenReint GroppDer Spiegel, 8. Januar 2026
While the long run relation between money and inflation is well established, empirical evidence on the adjustment to the long run equilibrium is very heterogeneous. In this paper we show, that the development of US consumer price inflation between 1960Q1 and 2005Q4 is strongly driven by money overhang. To this end, we use a multivariate state space framework that substantially expands the traditional vector error correction approach. This approach allows us to estimate the persistent components of velocity and GDP. A sign restriction approach is subsequently used to identify the structural shocks to the signal equations of the state space model, that explain money growth, inflation and GDP growth. We also account for the possibility that measurement error exhibited by simple-sum monetary aggregates causes the consequences of monetary shocks to be improperly identified by using a Divisia monetary aggregate. Our findings suggest that when the money is measured using a reputable index number, the quantity theory holds for the United States.
This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown, Durbin, and Evans (1975) and Zeileis (2004), Nyblom (1989) and Hansen (1992), and Andrews, Lee, and Ploberger (1996). Power and size properties are derived using Monte Carlo simulations. Results emphasize that mostly the CUSUM type tests are affected by the presence of heteroscedasticity, whereas the individual parameter Nyblom test and AvgLM test are proved to be highly robust. However, each test is significantly affected by leptokurtosis. Contrarily to other tests, where skewness is far more problematic than kurtosis, it has no additional effect for any of the endogenous break tests we analyze. Concerning overall robustness the Nyblom test performs best, while being almost on par to more recently developed tests in terms of power.
Conventional Phillips-curve models that are used to estimate the output gap detect a substantial decline in potential output due to the present crisis. Using a multivariate state space model, we show that this result does not hold if the long run role of excess liquidity (that we estimate endogeneously) for inflation is taken into account.
While the long run relation between money and inflation is well established, empirical evidence on the adjustment to the long run equilibrium is very heterogeneous. In the present paper we use a multivariate state space framework, that substantially expands the traditional vector error correction approach, to analyze the short run impact of money on prices. We contribute to the literature in three ways: First, we distinguish changes in velocity of money that are due to institutional developments and thus do not induce inflationary pressure, and changes that reflect transitory movements in money demand. This is achieved with a newly developed multivariate unobserved components decomposition. Second, we analyze whether the high volatility of the transmission from monetary pressure to inflation follows some structure, i.e., if the parameter regime can assumed to be constant. Finally, we use our model to illustrate the consequences of the monetary policy of the Fed that has been employed to mitigate the impact of the financial crisis, simulating different exit strategy scenarios.
We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since
disregarding a threshold in cointegration models renders standard approaches
to the estimation of the cointegration vectors inefficient, TVECM necessitate a
simultaneous estimation of the cointegration vector(s) and the threshold. As far
as two cointegrated variables are considered this is commonly achieved by a grid
search. However, grid search quickly becomes computationally unfeasible if more
than two variables are cointegrated. Therefore, the likelihood function has to be
maximized using heuristic approaches. Depending on the precise problem structure
the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.
The present paper compares expected inflation to (econometric) inflation forecasts
based on a number of forecasting techniques from the literature using a panel of
ten industrialized countries during the period of 1988 to 2007. To capture expected
inflation we develop a recursive filtering algorithm which extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell-Tobin-effect.
The extracted unexpected inflation is compared to the forecasting errors of ten
econometric forecasts. Beside the standard AR(p) and ARMA(1,1) models, which
are known to perform best on average, we also employ several Phillips curve based approaches, VAR, dynamic factor models and two simple model avering approaches.
