Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
im Erscheinen
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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U.S. Monetary-Fiscal Regime Changes in the Presence of Endogenous Feedback in Policy Rules
Yoosoon Chang, Boreum Kwak
IWH Discussion Papers,
Nr. 15,
2017
Abstract
We investigate U.S. monetary and fiscal policy regime interactions in a model, where regimes are determined by latent autoregressive policy factors with endogenous feedback. Policy regimes interact strongly: Shocks that switch one policy from active to passive tend to induce the other policy to switch from passive to active, consistently with existence of a unique equilibrium, though both policies are active and government debt grows rapidly in some periods. We observe relatively strong interactions between monetary and fiscal policy regimes after the recent financial crisis. Finally, latent policy regime factors exhibit patterns of correlation with macroeconomic time series, suggesting that policy regime change is endogenous.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
Abstract
In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Monetary-fiscal Policy Interaction and Fiscal Inflation: A Tale of Three Countries
Martin Kliem, Alexander Kriwoluzky, Samad Sarferaz
European Economic Review,
2016
Abstract
We study the impact of the interaction between fiscal and monetary policy on the low-frequency relationship between the fiscal stance and inflation using cross-country data from 1965 to 1999. In a first step, we contrast the monetary–fiscal narrative for Germany, the U.S., and Italy with evidence obtained from simple regression models and a time-varying VAR. We find that the low-frequency relationship between the fiscal stance and inflation is low during periods of an independent central bank and responsible fiscal policy and more pronounced in times of non-responsible fiscal policy and accommodative monetary authorities. In a second step, we use an estimated DSGE model to interpret the low-frequency measure structurally and to illustrate the mechanisms through which fiscal actions affect inflation in the long run. The findings from the DSGE model suggest that switches in the monetary–fiscal policy interaction and accompanying variations in the propagation of structural shocks can well account for changes in the low-frequency relationship between the fiscal stance and inflation.
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Qual VAR Revisited: Good Forecast, Bad Story
Makram El-Shagi, Gregor von Schweinitz
Journal of Applied Economics,
Nr. 2,
2016
Abstract
Due to the recent financial crisis, the interest in econometric models that allow to incorporate binary variables (such as the occurrence of a crisis) experienced a huge surge. This paper evaluates the performance of the Qual VAR, originally proposed by Dueker (2005). The Qual VAR is a VAR model including a latent variable that governs the behavior of an observable binary variable. While we find that the Qual VAR performs reasonable well in forecasting (outperforming a probit benchmark), there are substantial identification problems even in a simple VAR specification. Typically, identification in economic applications is far more difficult than in our simple benchmark. Therefore, when the economic interpretation of the dynamic behavior of the latent variable and the chain of causality matter, use of the Qual VAR is inadvisable.
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Macroeconomic Trade Effects of Vehicle Currencies: Evidence from 19th Century China
Makram El-Shagi
IWH Discussion Papers,
Nr. 23,
2016
Abstract
We use the Chinese experience between 1867 and 1910 to illustrate how the volatility of vehicle currencies affects trade. Today’s widespread vehicle currency is the dollar. However, the macroeconomic effects of this use of the dollar have rarely been addressed. This is partly due to identification problems caused by its international importance. China had adopted a system, where silver was used almost exclusively for trade, similar to a vehicle currency. While being important for China, the global role of silver was marginal, alleviating said identification problems. We develop a bias corrected structural VAR showing that silver price fluctuations significantly affected trade.
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Much Ado About Nothing: Sovereign Ratings and Government Bond Yields in the OECD
Makram El-Shagi
IWH Discussion Papers,
Nr. 22,
2016
Abstract
In this paper, we propose a new method to assess the impact of sovereign ratings on sovereign bond yields. We estimate the impulse response of the interest rate, following a change in the rating. Since ratings are ordinal and moreover extremely persistent, it proves difficult to estimate those impulse response functions using a VAR modeling ratings, yields and other macroeconomic indicators. However, given the highly stochastic nature of the precise timing of ratings, we can treat most rating adjustments as shocks. We thus no longer rely on a VAR for shock identification, making the estimation of the corresponding IRFs well suited for so called local projections – that is estimating impulse response functions through a series of separate direct forecasts over different horizons. Yet, the rare occurrence of ratings makes impulse response functions estimated through that procedure highly sensitive to individual observations, resulting in implausibly volatile impulse responses. We propose an augmentation to restrict jointly estimated local projections in a way that produces economically plausible impulse response functions.
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Monetary-fiscal Policy Interaction and Fiscal Inflation: A Tale of Three Countries
Martin Kliem, Alexander Kriwoluzky, Samad Sarferaz
Abstract
We study the impact of the interaction between fiscal and monetary policy on the low-frequency relationship between the fiscal stance and inflation using cross-country data from 1965 to 1999. In a first step, we contrast the monetary-fiscal narrative for Germany, the U.S. and Italy with evidence obtained from simple regression models and a time-varying VAR. We find that the low-frequency relationship between the fiscal stance and inflation is low during periods of an independent central bank and responsible fiscal policy and more pronounced in times of high fiscal budget deficits and accommodative monetary authorities. In a second step, we use an estimated DSGE model to interpret the low-frequency measure structurally and to illustrate the mechanisms through which fiscal actions affect inflation in the long run. The findings from the DSGE model suggest that switches in the monetary-fiscal policy interaction and accompanying variations in the propagation of structural shocks can well account for changes in the low-frequency relationship between the fiscal stance and inflation.
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Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
Christiane Baumeister, James D. Hamilton
Econometrica,
im Erscheinen
Abstract
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
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