Asymmetric cycles in unobserved components models

A class of structural time series models with an asymmetric cyclical component is presented and used in order to test for asymmetry in economic time series. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past observations of the stochastic process being modelled. Due to the conditional Gaussianity of the model, Kalman filtering techniques can be used in the estimation of the parameters, and a standard test for equality of cyclical frequency can be used as a symmetry test. Applying the test to US economic time series reveals strong cyclical asymmetries in unemployment and industrial production, and no significant deviation from symmetry in GDP. The test is also applied to industrial production data in EU countries, with mixed results.asymmetry

Natural disasters and human capital accumulation

The author assesses empirically the relationship between natural disaster risk and investment in education. Although the results in the empirical literature tend to be inconclusive, using model averaging methods in the framework of cross-country and panel regressions, this paper finds an extremely robust negative partial correlation between secondary school enrollment and natural disaster risk. This result is exclusively driven by geological disasters. Natural disaster risk exposure is a robust determinant of differences in secondary school enrollment between countries, but not within countries, which implies that the effect can be interpreted as a long-run phenomenon.Natural Disasters,Hazard Risk Management,Disaster Management,Population Policies,Access to Finance

We just estimated twenty million fiscal multipliers

We analyse the role played by data and specification choices as determinants of the size ofthe fiscal multipliers obtained using structural vector autoregressive models. The results, based on over twenty million fiscal multipliers estimated for European countries, indicate that many seemingly harmless modelling choices have a significant effect on the size and precision of fiscal multiplier estimates. In addition to the structural shock identification strategy, these modelling choices include the definition of spending and taxes, the national accounts system employed, the use of particular interest rates or inflation measures, or whether data are smoothed prior to estimation. The cumulative effects of such arguably innocuous methodological choices can lead to a change in the spending multipliers of as much as 0.4 points

Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach

We propose a Bayesian Averaging of Thresholds (BAT) approach for assessing the existence and quantifying the effect of threshold effects in cross-country growth regressions in the presence of model uncertainty. The BAT method extends the Bayesian Averaging of Classical Estimates (BACE) approach proposed by Sala-i-Martin, Doppelhofer, and Miller (2004) by allowing for uncertainty over nonlinear threshold effects. We apply our method to a set of determinants of long-term economic growth in the cross section of 88 countries. Our results suggest that when model uncertainty is taken into account there is no evidence for robust threshold effects caused by the Initial Income, measured by GDP capita in 1960, but that the Number of Years an Economy has Been Open is an important source of nonlinear effects on growth

Forecasting euro exchange rates: How much does model averaging help?

We analyze the performance of Bayesian model averaged exchange rate forecasts for euro/US dollar, euro/Japanese yen, euro/Swiss franc and euro/British pound rates using weights based on the out-of-sample predictive likelihood. The paper also presents a simple stratified sampling procedure in the spirit of Sala i Martin et alia (2004) to obtain model weights based on predictive accuracy. Our results indicate that accounting explicitly for model uncertainty when constructing predictions of euro exchange rates leads to improvements in predictive accuracy as measured by the mean square forecast error. While the forecasting error of the combined forecast tends to be systematically smaller than that of the individual model that would have been chosen based on predictive accuracy in a test sample, random walk forecasts cannot be beaten significantly in terms of squared forecast errors. Direction of change statistics, on the other hand, are significantly improved by Bayesian model averaging

Explaining the persistence of profits: A time-varying approach

The present paper analyzes the determinants of profit persistence using a newly developed methodology that allows for the persistence parameter to vary with time. It therefore addresses a significant limitation of previous persistence models, which have assumed unrealistically that persistence is fixed over relatively long period of 20 years upwards. The concentration and the size of the industry are found to have a significant positive impact on profit persistence. However, at firm level, market share and risk have surprisingly a negative impact on profit persistence.

Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach

We propose a framework for assessing the existence and quantifying the effect of threshold effects in cross-country growth regressions in the presence of model uncertainty. The method is based on Bayesian model averaging tech- niques and generalizes the Bayesian Averaging of Classical Estimates (BACE) method put forward by Sala-i-Martin, Doppelhofer, and Miller (2004). We ap- ply the method presented in this paper to a set of 21 variables that have been found to be robustly related to economic growth in a cross-section of 88 coun- tries. We find no evidence of robust threshold effects generated by the initial level of GDP per capita. However, we find that the proportion of years a country has been open to trade is an important source of nonlinear effects on economic growth.

Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive model, whose lag length is chosen to minimize Akaike's AIC criterion. The results are very negative for the SETAR model when the Monte Carlo procedure is used to generate multi-step forecasts. When the "naive" procedure of generating forecasts is used, the results are surprisingly better for the SETAR model in long-term predictions. Due to the characteristics of the residuals, a bootstrapping method of forecasting was also used, yielding even poorer results for the nonlinear model.Nonlinear Time Series Models, SETAR Models, Forecasting

Interest Rate Pass-Through in Central and Eastern Europe: Reborn from Ashes Merely to Pass Away?

In this study, we seek to better understand the interest rate pass-through in five Central and Eastern European countries -- the Czech Republic, Hungary, Poland, Slovakia and Slovenia, the CEE-5. Our pass-through estimates for several retail rates are generally lower than those reported in the literature, given the absence of cointegration between policy rates and long- or even short-term market rates. In addition, the pass-through has been declining over time in the CEE-5, and we argue that it is likely to decrease further in the future. Finally, the pass-through appears similar in the CEE-5 than in Spain and is higher than in core euro area countries. Hence, euro adoption by the CEE-5 would not further increase heterogeneity within the euro area with regard to the interest rate passthrough. However, substantially more research is needed to establish commonalities and differences between the CEE-5 and the euro area with respect to the reaction of prices and output to monetary policy action.http://deepblue.lib.umich.edu/bitstream/2027.42/57231/1/wp851 .pd