Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and GARCH Models

This paper revisits the issue of conditional volatility in real GDP growth rates for Canada, Japan, the United Kingdom, and the United States. Previous studies find high persistence in the volatility. This paper shows that this finding largely reflects a nonstationary variance. Output growth in the four countries became noticeably less volatile over the past few decades. In this paper, we employ the modified ICSS algorithm to detect structural change in the unconditional variance of output growth. One structural break exists in each of the four countries. We then use generalized autoregressive conditional heteroskedasticity (GARCH) specifications modeling output growth and its volatility with and without the break in volatility. The evidence shows that the time-varying variance falls sharply in Canada, Japan, and the U.K. and disappears in the U.S., excess kurtosis vanishes in Canada, Japan, and the U.S. and drops substantially in the U.K., once we incorporate the break in the variance equation of output for the four countries. That is, the integrated GARCH (IGARCH) effect proves spurious and the GARCH model demonstrates misspecification, if researchers neglect a nonstationary unconditional variance.

Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.

Total Factor Productivity and Monetary Policy: Evidence from Conditional Volatility

This paper empirically assesses whether monetary policy affects real economic activity through its affect on the aggregate supply side of the macroeconomy. Analysts typically argue that monetary policy either does not affect the real economy, the classical dichotomy, or only affects the real economy in the short run through aggregate demand new Keynesian or new classical theories. Real business cycle theorists try to explain the business cycle with supply-side productivity shocks. We provide some preliminary evidence about how monetary policy affects the aggregate supply side of the macroeconomy through its affect on total factor productivity, an important measure of supply-side performance. The results show that monetary policy exerts a positive and statistically significant effect on the supply-side of the macroeconomy. Moreover, the findings buttress the importance of countercyclical monetary policy as well as support the adoption of an optimal money supply rule. Our results also prove consistent with the effective role of monetary policy in the Great Moderation as well as the more recent rise in productivity growth.

Dynamic Stock Market Interactions between the Canadian, Mexican, and the United States Markets: The NAFTA Experience

This paper explores the dynamic linkages that portray different facets of the joint probability distribution of stock market returns in NAFTA (i.e., Canada, Mexico, and the US). Our examination of interactions of the NAFTA stock markets considers three issues. First, we examine the long-run relationship between the three markets, using cointegration techniques. Second, we evaluate the dynamic relationships between the three markets, using impulse-response analysis. Finally, we explore the volatility transmission process between the three markets, using a variety of multivariate GARCH models. Our results also exhibit significant volatility transmission between the second moments of the NAFTA stock markets, albeit not homogenous. The magnitude and trend of the conditional correlations indicate that in the last few years, the Mexican stock market exhibited a tendency toward increased integration with the US market. Finally, we do note that evidence exists that the Peso and Asian financial crises as well as the stock-market crash in the US affect the return and volatility time-series relationships.

Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors

In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models.

Testing for Balance Sheet Effects in Emerging Market Countries

This paper tests the presence of balance sheets effects and analyzes the implications for exchange rate policies in emerging markets. The results reveal that the emerging market bond index (EMBI) is negatively related to the banks' foreign currency leverage, and that these banks' foreign currency exposures are relatively unhedged. Panel SVAR methods using EMBI instead of advanced country lending rates find, contrary to the literature, that the amplitude of output responses to foreign interest rate shocks are smaller under relatively fixed regimes. The findings are robust to the local projections method of obtaining impulse responses, using country specific and GARCH-SVAR models.

The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis

Recently, Fagiolo et al. (2008) find fat tails of economic growth rates after adjusting outliers, autocorrelation and heteroskedasticity. This paper employs US quarterly real output growth, showing that this finding of fat tails may reflect the Great Moderation. That is, leptokurtosis disappears after GARCH adjustment once we incorporate the break in the variance equation.