Yu-chin Chen

Commodity Currencies and Empirical Exchange Rate Puzzles

This paper re-examines empirical exchange rate puzzles by focusing on three OECD economies (Australia, Canada, and New Zealand) where primary commodities constitute a significant share of their exports. For Australia and New Zealand especially, we find that the U.S. dollar price of their commodity exports (generally exogenous to these small economies) - has a strong and stable influence on their floating real rates, with the quantitative magnitude of the effects consistent with predictions of standard theoretical models. However, after controlling for commodity price shocks, there is still a PPP puzzle in the residual. Nevertheless, the results here are relevant to many developing country commodity exporters, as they liberalize their capital markets and move towards floating exchange rates.

What Does the Yield Curve Tell Us About Exchange Rate Predictability

Since the term structure of interest rates embodies information about future economic activity, we extract relative Nelson-Siegel (1987) factors from cross-country yield curve differences to proxy expected movements in future exchange rate fundamentals. Using monthly data for the United Kingdom, Canada, Japan, and the United States, we show that the yield curve factors predict exchange rate movements and explain excess currency returns one month to two years ahead. Our results provide support for the asset pricing formulation of exchange rate determination and offer an intuitive explanation to the uncovered interest parity puzzle by relating currency risk premiums to inflation and business cycle risks.

Forecasting Inflation Using Commodity Price Aggregates

This paper shows that for five small commodity-exporting countries that have adopted inflation targeting monetary policies, world commodity price aggregates have predictive power for their CPI and PPI inflation, particularly once possible structural breaks are taken into account. This conclusion is robust to using either disaggregated or aggregated commodity price indexes (although the former perform better), the currency denomination of the commodity prices, and to using mixed-frequency data. In pseudo out-of-sample forecasting, commodity indexes outperform the random walk and AR(1) processes, although the improvements over the latter are sometimes modest.

Monetary Policy Design under Imperfect Knowledge: An Open Economy Analysis

This paper incorporates adaptive learning into a standard New-Keynesian open economy dynamic stochastic general equilibrium (DSGE) model and analyze under what conditions policymakers should target domestic producer price inflation (DI) versus consumer price inflation (CI). Our goal is to examine how monetary policy rules should adjust when agents’ information sets deviate from those assumed under the rational expectation paradigm. When agents form expectations using an adaptive learning mechanism, even though the central bank has no informational advantage, monetary policy can nonetheless facilitate the learning process and thus mitigate distortions associated with imperfect knowledge. We assume the policy-maker follows a forwardlooking Taylor rule and focus on analyzing the interplay between the source of the dominant shock and the extent of knowledge imperfection. We find that when agents have very limited knowledge and have to learn the dynamics governing both the relevant economic indicators and the underlying structural shocks, a DI targeting rule introduces fewer forecast errors and is better at stabilizing the economy. However, when agents can observe contemporaneous shocks and need only learn how key economic variables evolve (a situation akin to a post-structural-shift economy), targeting away from the dominant shocks helps anchor expectations and improve welfare. A CI target can then become the preferred policy rule when the economy is subject to large domestic shocks.

Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach

This paper presents a generalized autoregressive distributed lag (GADL) model for conducting regression estimations that involve mixed-frequency data. As an example, we show that daily asset market information - currency and equity mar- ket movements - can produce forecasts of quarterly commodity price changes that are superior to those in the previous research. Following the traditional ADL lit- erature, our estimation strategy relies on a Vandermonde matrix to parameterize the weighting functions for higher-frequency observations. Accordingly, infer- ences can be obtained using ordinary least squares principles without Kalman fi ltering, non-linear optimizations, or additional restrictions on the parameters. Our fi ndings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price movements.

Adaptive Learning and Monetary Policy in an Open Economy: Lessons from Japan

Motivated by Japans economic experiences in recent decades, we incorporate adaptive learning into an open economy dynamic stochastic general equilibrium model to examine the volatility and welfare impact of alternative monetary policies. Comparing four Taylor-styled policy rules that reflect Japans monetary policy debates, we first show that imperfect knowledge and the associated learning process induce higher volatility in the economy and that explicit exchange rate stabilization is unwarranted. Moreover, contrary to results under the rational expectation paradigm, we find that while tight inflation controls raise output volatility, they can improve overall welfare under learning by smoothing inflation fluctuations. Copyright 2008 The Authors. Journal compilation 2008 Blackwell Publishing Asia Pty Ltd

Monitoring Structural Stability of Dynamic Factor Models

This paper aims to test the out-of-sample structural changes of the factor augmented regression (FAR) model as compared to the in-sample structural stability tests considered in Breitung and Eickmeier (2011), and Chen, Dolado, and Gonzalo (2011). We extends the fluctuation monitoring test of Chu, Stinchcombe, and White (1996) by proposing a rescaled monitoring test to deal with the scenario where there is a large number of cross-section series, N, each with T observations, and each series has some predictive ability for the variable of interest as in the study of Bai and Ng (2006). We recognize the difficulties of monitoring the predictive ability of the estimated factors by pointing out the randomness nature of estimated factor from principle component analysis (PCA) when new observation arrives sequentially. Nevertheless, under the null hypothesis of no structural change, a new rescaled monitoring test is proposed and proved to behave as a Brownian bridge asymptotically. Thus, the critical values suggested in Chu et al. (1996) can be applied to the dynamce factor model along with our method. Furthermore, the finite sample of performance of the proposed test is promising. We also apply the new test to the data of Ludvigson and Ng (2009) for monitoring the stability of the factor factor for forecasting U.S. industrial production (IP).