Remeasuring and decomposing stochastic trends in business cycles

This paper is motivated by the fact that the standard deviation of cyclical TFP derived from the standard approach under the stochastic trend is not even close to the real-world data. The main part of the paper devotes to developing a new method to apply geometric Brownian motion to characterize TFP in continuous time and converting it to an estimated process of random walk with drift. As a result, the drift estimate together with the lagged TFP in the random walk process are the stochastic trend of TFP and the stochastic error term in the random walk with drift process is the cyclical component of TFP. I then have two findings: the first one is that the standard deviation of cyclical TFP derived from the new approach is much closer to the real-world data; the second one is that stochastic trend of TFP can be decomposed into three parts: an initial value, a deterministic trend, and a term involved with Weiner process. Moreover, this paper argues that, by recalculating the business cycle statistics based on a rational expectations model, if we remeasure the stochastic trend and cyclical component of TFP using the new approach, then the ability of real business cycle model to mimic real-world economic fluctuations will be significantly improved.     

Moment Estimation of the Probit Model with an Endogenous Continuous Regressor

We propose a generalized method of moments (GMM) estimator with optimal instruments for a probit model that includes a continuous endogenous regressor. This GMM estimator incorporates the probit error and the heteroscedasticity of the error term in the first‐stage equation in order to construct the optimal instruments. The estimator estimates the structural equation and the first‐stage equation jointly and, based on this joint moment condition, is efficient within the class of GMM estimators. To estimate the heteroscedasticity of the error term of the first‐stage equation, we use the k‐nearest neighbour (k‐nn) non‐parametric estimation procedure. Our Monte Carlo simulation shows that in the presence of heteroscedasticity and endogeneity, our GMM estimator outperforms the two‐stage conditional maximum likelihood estimator. Our results suggest that in the presence of heteroscedasticity in the first‐stage equation, the proposed GMM estimator with optimal instruments is a useful option for researchers.     

Trans‐Tasman Transmission of Government Spending Shocks

This paper investigates the international transmission of fiscal shocks between two closely‐linked, open economies. We estimate impulse response functions using a semi‐structural vector auto regressive (VAR) model and quarterly data from Australia and New Zealand for the period 1973:3–2008:4. We compare our empirical results with impulse response functions from a calibrated two‐country international real business cycle model with habit formation and adjustment costs to investment. We show that a positive shock to Australian government consumption leads to an increase in Australian output initially and then to a decline in the medium term, while the New Zealand output is negatively affected both in the short and medium term. This result is in line with the recent literature that reports beggar‐thy‐neighbour effect of positive government spending shocks.     

Portfolio optimization using dynamic factor and stochastic volatility: evidence on Fat‐tailed errors and leverage

The portfolio optimization problem is investigated using a multivariate stochastic volatility model with factor dynamics, fat‐tailed errors and leverage effects. The efficient Markov chain Monte Carlo method is used to estimate model parameters, and the Rao–Blackwellized auxiliary particle filter is used to compute the likelihood and to predict conditional means and covariances. The proposed models are applied to sector indices of the Tokyo Stock Price Index (TOPIX), which consists of 33 stock market indices classified by industrial sectors. The portfolio is dynamically optimized under several expected utilities and two additional static strategies are considered as benchmarks. An extensive empirical study indicates that our proposed dynamic factor model with leverage or fat‐tailed errors significantly improves the predictions of the conditional mean and covariances, as well as various measures of portfolio performance.     

A STRUCTURAL MODEL OF ELECTORAL ACCOUNTABILITY

This article proposes a structural approach to measuring the effects of electoral accountability. We estimate a political agency model with imperfect information in order to identify and quantify discipline and selection effects, using data on U.S. governors. We find that the possibility of reelection provides a significant incentive for incumbents to exert effort, that is, a disciplining effect. We also find a positive but weaker selection effect. According to our model, the widely used two‐term regime improves voter welfare by 4.2% compared to a one‐term regime, and better voter information about the effort of the governors would further increase voter welfare by up to 0.5%.     

Do Long‐Term Unemployed Workers Benefit from Targeted Wage Subsidies?

We evaluate a wage subsidy program that is targeted at long‐term unemployed workers in Germany. We use an alternative identification procedure compared to empirical studies conducted so far. Exploiting the particular program regulations and large administrative data we estimate the impact of program availability using a regression discontinuity framework. Our results suggest no significant impact of the availability of the subsidy on labor market outcomes of the target group. Even though our analysis lacks some statistical power, our findings do not support the substantial positive effects obtained from matching studies. As our approach does not require observability of all drivers of selection, previous empirical studies justifying government expenditures on wage subsidies based on matching methods should be reconsidered.     

AN EQUILIBRIUM SEARCH MODEL OF SYNCHRONIZED SALES*

We demonstrate the existence of periodic nonstationary equilibria with self‐generating cycles in a simple model of random search. Our results provide a theory of synchronized sales based on product market search by heterogeneous consumers. That is, our model explains how it can be optimal for all sellers to follow a repeated pattern of posting a high price for several periods and then posting a low price for one period.     

IMPLICIT CONTRACTS,LIFE CYCLE LABOR SUPPLY,AND INTERTEMPORAL SUBSTITUTION

The implicit contract model is a serious alternative to the spot market interpretation of the labor market. However, its usefulness has been limited because the wage is unobserved, and hence it has not been possible to estimate an intertemporal (Frisch) supply elasticity for the model using microdata. In this article, we show that one can estimate this elasticity from microdata within the context of the implicit contract model under relatively weak assumptions based on consumer theory. We implement our approach on two micro data sets and, for both, obtain a reasonably precise elasticity estimate of approximately 1.0.     

Generalized least squares and maximum likelihood estimation of the logistic function for technology diffusion

Given a simple stochastic model of technology adoption, we derive a function for technological diffusion that is logistic in the deterministic part and has an error term based on the binomial distribution. We derive two estimators—a generalized least squares (GLS) estimator and a maximum likelihood (ML) estimator—which should be more efficient than the ordinary least squares (OLS) estimators typically used to estimate technological diffusion functions. We compare the two new estimators with OLS using Monte-Carlo techniques and find that under perfect specification, GLS and ML are equally efficient and both are more efficient than OLS. There was no evidence of bias in any of the estimators. We used the estimators on some example data and found evidence suggesting that under conditions of misspecification, the estimated variance-covariance of the ML estimator is badly biased. We verified the existence of the bias with a second Monte-Carlo experiment performed with a known misspecification. In the second experiment, GLS was the most efficient estimator, followed by ML, and OLS was least efficient. We conclude that the GLS estimator of choice.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.     

EFFECTS OF STOCHASTIC INTEREST RATES AND VOLATILITY ON CONTINGENT CLAIMS*

We investigate the effects of stochastic interest rates and the volatility of the underlying asset price on contingent claim prices including futures and options prices. The futures price can be decomposed into the forward price and an additional term; the options price can be decomposed into the Black‐Scholes formula and several additional terms by applying the asymptotic expansion approach of the small disturbance asymptotics developed by Kunitomo and Takahashi (1995, 1998, 2001, 2003a, 2003b). The technical method is based on a new application of the Malliavin‐Watanabe Calculus or the Watanabe‐Yoshida Theory on Malliavin Calculus in stochastic analysis. We illustrate our new formulae and their numerical accuracy.     

Shocks and rigidities as determinants of CEE labour markets’ performance

In this article, the impact of real wage, productivity, labour demand and supply shocks on eight Central and Eastern European (CEE) economies from 1996–2007 is analysed with a panel structural vector error correction model. A set of long‐run restrictions derived from the dynamic stochastic general equilibrium (DSGE) model is used to identify structural shocks, and fluctuations in foreign demand are controlled for. We find that the propagation of shocks on CEE labour markets resembles that found for OECD countries. Labour demand shocks emerge as the main determinant of employment and unemployment variability in the short‐to‐medium run, but wage rigidities were equally important for observed labour market performance, especially in Poland, Czech Republic and Lithuania. We associate these rigidities with collective bargaining, minimum wage, active labour market policies and employment protection legislation.     

A note on Johansen's cointegration procedure when trends are present

This note discusses some issues that arise when Johansen's (1991) framework is used to analyze cointegrating relationships among variables with deterministic linear time trends. We cistinguish “stochastic” and “deterministic” cointegration, arguing that stochastic cointegration is sufficient for the existence of an error correction representation and that it is often the hypothesis of interest in empirical applications. We show that Johansen's (1991) method, which includes only a constant term in the estimated regession system, does not allow for stochastic cointegration. We propose to modify Johansen's method by including a vector of deterministic linear trends in the estimated model. We present tabulated critical values of the maximal eigenvalue and trace statistics appropriate for this case. We discuss the circumstances under which our modification may be useful.     

Sovereign Default Risk and Recovery Rates: What Government Bond Markets Expect for Greece

Bond market data on sovereign bond yields is used to estimate sovereign default risk and the amount of the expected “hair‐cut” for Greece between 2008 and 2011. Using a structural pricing model that relies on compound option theory short‐term and long‐term default probabilities and their dependencies can be inferred. Thereby bond yield spreads for different maturities are integrated. In addition, a reduced form model is applied to infer the recovery rate expected by bond market participants. The paper shows that sovereign default risk and recovery rate dynamics reflect events that are important for Greece's repayment capacity.     

Why is it so difficult to outperform the random walk? An application of the Meese–Rogoff puzzle to stock prices

Some economists suggest that the Meese–Rogoff puzzle is equally applicable to the stock market, in the sense that no model of stock prices can outperform the random walk in out-of-sample forecasting. We argue that this is not a puzzle and that we should expect nothing, but this result if forecasting accuracy is measured by the root mean square error (RMSE) and similar metrics that take into account the magnitude of the forecasting error only. We demonstrate by using two models for dividend-paying and nondividend-paying stocks that as price volatility rises, the RMSE of the random walk rises, but the RMSE of the model rises even more rapidly, making it unlikely for the model to outperform the random walk.     

Joint estimation of the Lerner index and cost efficiency using copula methods

This paper deals with the estimation of market power, measured by the Lerner index, and cost efficiency at the bank level, using the stochastic frontier (SF) methodology. Both market power and cost efficiency are estimated jointly in a single step. We use the copula method to incorporate dependence between market power and cost efficiency. In contrast to earlier works that used a two-step approach, the SF approach used herein estimates a bank-specific nonnegative Lerner index free from random shocks. We showcase the advantages of our proposed methodology in terms of an empirical study on the banking sectors of five former communist countries during the period 2000–2008. Compared to the conventional approach, our model gives higher mean values of the Lerner index and smaller standard deviations. Further, we find a significant positive relationship between cost efficiency and market power of banks, thereby rejecting the “quiet life hypothesis.”     

Productivity Dispersion and Measurement Error

Several reasons have been put forward to explain the high dispersion of productivity across establishments: quality of management, different input usage and market distortions, to name but a few. Although it is acknowledged that a sizable portion of productivity dispersion may also be due to measurement error, little research has been devoted to identifying how much they contribute. We outline a novel procedure for identifying the role of measurement error in explaining the empirical dispersion of productivity across establishments. The starting point of our framework is the errors-in-variable model consisting of a measurement equation and a structural equation for latent productivity. We estimate the variance of the measurement error and subsequently estimate the variance of the latent productivity variable, which is not contaminated by measurement error. Using Norwegian data on the manufacture of food products, we find that about one percent of the measured dispersion stems from measurement error.     

Estimating the Cox,ingersoll and Ross model of the term structure: a multivariate approach

In this paper we suggest a new methodology to estimate the Cox, Ingersoll and Ross model of the term structure. The approach is based on a multivariate non-linear least squares procedure, which allows us to simultaneously take into account the cross-sectional relations which exist among bond prices at each instant of time and the dynamics of each bond price over time. The methodology involves the use of a fairly simple econometric specification and is developed to deal with both the case of independently and identically distributed error terms and the case of autocorrelated error terms. We estimate and test the model using nominal prices of Italian Treasury bonds.     

Exchange rate fluctuations and labour market adjustments in Canadian manufacturing industries

We estimate the link between exchange rate fluctuations and the labour input of Canadian manufacturing industries. The analysis is based on a dynamic model of labour demand and the econometric strategy employs a panel two‐step approach for cointegrating regressions. Our data are drawn from a panel of 20 manufacturing industries from the KLEMS database and cover a long sample period that includes all cycles of appreciation and depreciation of the Canadian dollar over the last 50 years. Our results indicate that exchange rate fluctuations have significant long‐term effects on the labour input of Canada's manufacturing industries, especially for trade‐oriented industries, but that these long‐term impacts materialize very gradually following shocks.