The choice of a foreign price measure in a Bayesian estimated new-Keynesian model for Israel

We estimate a small DSGE model by full information Bayesian techniques on the basis of Israeli data from 1995 to 2006. The model was first developed and estimated by means of classical GMM in Argov and Elkayam (2010), and since then it has been used at the Bank of Israel for monetary policy analysis. It is widely believed that in 2007 (out of sample year) as elsewhere worldwide, inflation rose in Israel due to high commodity prices in global markets. However, our baseline model attributes most of the high inflation in 2007 to supply shocks. One conjecture is that this model's result derives from the inappropriate original use of the unit value of imported consumer goods (which do not include unprocessed food and energy) as the main foreign price measure. We test this conjecture by re-estimating the model with various other foreign price measures that typically do reflect the global rise in commodity prices and compare the log-marginal likelihoods. We find that no other price measure outperforms the original choice in the sample period. Only the foreign trade-weighted CPI equals the performance of the original choice while improving the 2007 interpretation of inflation, and should therefore be considered the main foreign price measure. The proposed methodology for comparing the suitability of alternative measures for observable variables can be applied to any model with exogenous variables that are characterized by univariate equations.     

Dynamic modeling under linear-exponential loss

We develop a methodology of parametric modeling of time series dynamics when the underlying loss function is linear-exponential (Linex). We propose to directly model the dynamics of the conditional expectation that determines the optimal predictor. The procedure hinges on the exponential quasi-maximum likelihood interpretation of the Linex loss and nicely fits the multiplicative error modeling framework. Many conclusions relating to estimation, inference and forecasting follow from results already available in the econometric literature. The methodology is illustrated using data on United States GNP growth and Treasury bill returns.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

Has the transmission mechanism of European monetary policy changed in the run-up to EMU?

This paper studies empirically the transmission mechanism of European monetary policy by means of time-varying, heterogeneous coefficient models estimated in a numerical Bayesian fashion. Based on pre-European Monetary Union evidence from Germany, France, Italy, and Spain, we find that (i) the long-run cumulative impact on output of a common, homoskedastic monetary policy shock has decreased in all countries after 1991. These declines are statistically significant and accompanied by some changes in the conduct of monetary policy over the same period. At the same time, we also find that (ii) cross-country differences in the effects of this shock have not decreased over time.     

Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash

Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

Testing the predictive power of the term structure without data snooping bias

It is well documented that the term structure of interest rates has predictive power for real economic growth. Applying the stepwise superior predictive ability test, we find that superior models contain both a short-term rate and a term spread.     

What drives German foreign direct investment? New evidence using Bayesian statistical techniques

Despite the importance of Germany as an issuer of foreign direct investment (FDI), the studies analyzing its determinants are far from conclusive. This research contributes to filling this gap providing new evidence for the period 1996–2012. In order to reduce model uncertainty, we adopt a Bayesian model averaging (BMA) approach. We find that determinants associated with horizontal FDI appear to be dominant for explaining FDI in developed countries while for the group of developing countries covariates associated with vertical FDI motives play a larger role. Within Europe, while the majority of FDI is horizontally driven in “core” countries, in the “periphery” vertical motivations seem to prevail. Moreover, our results are compatible with more complex FDI models where vertical determinants and institutional variables are gaining prominence as does the leading role currently played by Germany in global value chains (GVC). Our results may provide hints for policymakers’ strategies to attract German investment.     

Monetary transmission mechanisms in a small open economy: a Bayesian structural VAR approach

Abstract This paper develops a Bayesian structural VAR model for Canada in order to estimate the effects of monetary policy shocks, using the overnight rate target as the policy instrument. I allow the policy variable and other home and foreign variables to interact with each other contemporaneously. The key finding is that monetary policy affects the real economy through both the market interest rate and the exchange rate. I also find that the Bank of Canada responds to any home and foreign variables that embodies information about future inflation and that external shocks are an important source of output fluctuations.     

The role of labor markets for euro area monetary policy

In this paper, we explore the role of labor markets for monetary policy in the euro area in a New Keynesian model in which labor markets are characterized by search and matching frictions. We first investigate to which extent a more flexible labor market would alter the business cycle behavior and the transmission of monetary policy. We find that while a lower degree of wage rigidity makes monetary policy more effective, i.e. a monetary policy shock transmits faster onto inflation, the importance of other labor market rigidities for the transmission of shocks is rather limited. Second, having estimated the model by Bayesian techniques we analyze to which extent labor market shocks, such as disturbances in the vacancy posting process, shocks to the separation rate and variations in bargaining power are important determinants of business cycle fluctuations. Our results point primarily towards disturbances in the bargaining process as a significant contributor to inflation and output fluctuations. In sum, the paper supports current central bank practice which appears to put considerable effort into monitoring euro area wage dynamics and which appears to treat some of the other labor market information as less important for monetary policy.     

TESTING THE ONE‐WAY EFFECT IN THE PRESENCE OF TREND BREAKS*

This paper provides an approach to testing the measures of the one‐way effect for cointegrated vector time‐series in the presence of trend breaks. We propose Wald testing the measures and their computational algorithm, an extension of previous work by Hosoya and Yao and Hosoya, to the case where trend breaks are explicitly taken into account in the cointegration relationship. On the basis of the proposed inferential method and the derived evidence, we present a causal structure characterization of money supply and income as well as interest rates for the last 44 years of the Japanese economy, and contrast it with the results of Yao and Hosoya.     

Learnability and rationality of choice

In this paper we study the learnability of the class of rationalizable choice functions using the basic concept of PAC-learnability from statistical learning theory. We prove that the class of rationalizable choice functions on N alternatives is learnable from O(N) examples and is optimal in terms of PAC-learnability among classes which are invariant under permutations of the elements.     

Time varying structural VARs with sign restrictions: The case of Taiwan

I apply a Bayesian approach to a time-varying structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to study the time-varying nature of the Taiwanese economy. In particular, the structural parameters are identified via the sign information in a three-variable VAR system. The estimated results show that TVP-SVAR-SV model has the best fit to the data, compared to the time-varying parameters VAR model with constant volatility and a classical VAR model with constant parameters and volatilities. Moreover, I find the time-varying contemporaneous relationship between the output growth and inflation rates, particularly significant before the year 2000. Lastly, the impulse responses and the volatilities of all the variables are found to be time-varying.     

A Bayesian Measure of Poverty in the Developing World

We propose a new methodology to revise the international poverty line (IPL) after Ravallion et al . (2009) using the same database, but augmented with new variables to take into account social inclusion in the definition of poverty along the lines of Atkinson and Bourguignon (2001). We provide an estimation of the world income distribution and of the corresponding number of poor people in the developing world. Our revised IPL is based on an augmented two‐regime model estimated using a Bayesian approach, which allows us to take into account uncertainty when defining the reference group of countries where the IPL applies. The influence of weighting by population is discussed, as well as the IPL revision proposed in Deaton (2010). We also discuss the impact of using the new 2011 PPP and the recent IPL revision made by the World Bank.     

On the endogeneity of Cournot-Nash and Stackelberg equilibria: games of accumulation

We characterize equilibria of games with two properties: (i) Agents have the opportunity to adjust their strategic variable after their initial choices and before payoffs occur; but (ii) they can only add to their initial amounts. The equilibrium set consists of just the Cournot-Nash outcome, one or both Stackelberg outcomes, or a continuum of points including the Cournot-Nash outcome and one or both Stackelberg outcomes. A simple theorem that uses agents’ standard one-period reaction functions and the one-period Cournot-Nash and Stackelberg equilibria delineates the equilibrium set. Applications include contribution, oligopoly, and rent-seeking games.     

Bayesian estimation of the output gap for a small open economy: The case of Canada

We introduce a novel approach for estimating output gaps for small open economies. Identification is based on a multivariate trend-cycle decomposition in which transitory exchange rate movements are linked to the output gap and inflation. The model is then applied to Canadian data.     

On modeling heterogeneity in linear models using trend polynomials

The primary aim of the paper is to consider the problems and issues raised when the data exhibit time heterogeneity in the context of linear models. Ignoring time heterogeneity will undermine the reliability of inference and will give rise to untrustworthy evidence. Accounting for it using trend polynomials, however, is non-trivial because it raises several modeling issues. First, when the degree of the polynomial is greater than 4, or so, one needs to deal with the near-multicollinearity problem that arises. The second issue pertains to the type of polynomial that will adequately account for the time heterogeneity. Third, when the trend polynomials are treated as additional regressors, they will give rise to highly misleading statistical results. The paper investigates how different types of polynomials could deal with the near-multicollinearity and the modeling issues they raise, and makes recommendations to practitioners.     

Rational choice with status quo bias

Motivated by the empirical findings concerning the importance of one's current situation on her choice behavior, the main objective of this paper is to propose a rational choice theory that allows for the presence of a status quo bias, and that incorporates the standard choice theory as a special case. We follow a revealed preference approach, and obtain two nested models of rational choice that allow phenomena like the status quo bias and the endowment effect, and that are applicable in any choice situation to which the standard (static) choice model applies.     

Quasi-stationary cardinal utility and present bias

This note gives an axiomatic foundation for utility exhibiting quasi-geometric discounting. In addition, it introduces a wider class of utility functions satisfying weakened stationarity, called quasi-stationary utility. Both are established as von Neumann-Morgenstern utility indices in a model of risk preference.     

Bayesian estimation of cointegrating thresholds in the term structure of interest rates

Threshold Error Correction Models are used to analyse the term structure of interest Rates. The paper develops and uses a generalisation of existing models that encompasses both the Band and Equilibrium threshold models of [Balke and Fomby ((1997) Threshold cointegration. Int Econ Rev 38(3):627–645)] and estimates this model using a Bayesian approach. Evidence is found for threshold effects in pairs of longer rates but not in pairs of short rates. The Band threshold model is supported in preference to the Equilibrium model.