Convergence and Long Run Uncertainty

In this paper the neoclassical convergence hypothesis is tested for the thirteen regions of Chile using crosssection and time-series techniques. Cross-section analysis in combination with a Bayesian Modeling Averaging strategy supports the convergence hypothesis, despite of some instability detected in the estimated speed of convergence. When applying time-series based tests, the no convergence null hypothesis cannot be rejected at usual significance levels. When clustering the Chilean regions into three different groups, however, evidence of cointegration within these groups is found, indicating that the regional growth process in Chile is driven by a lower number of common trends. The implementation of both cross-section and time-series tests allows coverage of two different situations: economies in transition dynamics and economies in stationary distribution. Because cross-section and time-series tests place different implications on the data one can claim that under the assumption that Chilean regions are in transition towards a stationary distribution, the convergence hypothesis is supported by the data. If one assumes, however, that Chilean regions already achieved their limiting distribution, the convergence hypothesis is not supported by the data.

A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts.

In this paper we evaluate the Central Bank of Chile annual GDP growth forecasts over the period 1991-2009 using a real-time database. We compare the Central Bank of Chile forecasts with those of the Survey of Professional Forecasters (SPF), Consensus Forecasts, and simple time-series models. We compare all forecasts to first and quasi-final GDP growth vintages. We evaluate a number of different forecast properties, including forecast accuracy and efficiency. We report mixed results in terms of root mean squared prediction errors. Depending on the sample period, the forecast horizon and the vintage used in the analysis, forecasts from the Central Bank of Chile may outperform or be outperformed by the benchmarks. Despite these mixed results, differences in root mean squared prediction errors are generally moderate and have no statistical significance. Nevertheless, our efficiency analysis, in addition to the fact that in some periods the forecasts produced by the Central Bank of Chile have been outperformed by alternative forecasts, opens the question about the room for improvement in the accuracy of the Central Bank of Chile forecasts. While the room for improvement may actually exist, our results suggest that this room seems to be small for point forecasts and larger for interval forecasts.

Forecasting Inflation Forecast Errors

We evaluate inflation forecasts from the Survey of Professional Forecasters (SPF) of the Central Bank of Chile. Forecast errors for the period 2000-2008 show an excess of autocorrelation and a statistically significant bias at the end of the sample. We take advantage of the autocorrelation structure of the forecast errors to build new and more accurate inflation forecasts. We evaluate these new forecasts in an out-of-sample exercise. The new forecasts display important reductions in bias and Mean Square Prediction Error. Moreover, these reductions are, in general, statistically significant.

Cooperatives and Area Yield Insurance:A Theoretical Analysis

The purpose of this paper is to theoretically investigate the potential benefits that arise from a cooperative selling a government subsidized area-yield contract (i.e., the Group Risk Plan). The indeminities in area-yield contracts are triggered by a geographically determined yield (e.g., a county-wide yield average) instead of the more conventional individual actual production history. Therefore, an area-yield contract would be appropriate for managing the cooperative's systemic throughput risk. The cooperative would also capture some of the substantial government subsidies that are normally given to a private insurance company. Our primary finding is that farmers should be indifferent when considering the decision to purchase area-yield insurance from a private company or encompass that business in their cooperative. We derive this result for the specific case of costless insurance and assume a Pareto Optimal contract. Under these assumptions, the government subsidies that the cooperative would hope to capture are simply a net deduction in their premiums. In other words, the benefit they capture from the subsidies is the same when they purchase the insurance from an outside firm or internally.Cooperatives, Area Yield Insurance, Optimal indemnity

Communicational Bias In Monetary Policy: Can Words Forecast Deeds?

Communication with the public is an ever-growing practice among central banks and complements their decisions of interest rate setting. In this paper we examine one feature of the communicational practice of the Central Bank of Chile (CBC) which summarizes the assessment of the Board about the most likely future of the monetary policy interest rate. We show that this assessment, known as communicational bias or simply c-bias, contains valuable information regarding the future stance of monetary policy. We do this by comparing, against several benchmarks, the c-bias’s ability to correctly forecast the direction of monetary policy rates. Our results indicate that the CBC has (in our sample period) matched words and deeds. The c-bias is a more accurate predictor of the future direction of monetary policy rates than a random walk and a uniformly-distributed random variable. It also improves the predictive ability of a discrete Taylor-Rule-type model that uses persistence, output gap and inflation-deviation-from-target as arguments. We also show that the c-bias can provide information to improve monetary policy rate forecasts based on the forward rate curve.

External Imbalances, Valuation Adjustments and Real Exchange Rate: Evidence of Predictability in an Emerging Economy

We evaluate the ability of a measure of external imbalances that combines the trade and the financial channels to forecast the real effective exchange rate for Chile. By making use of a quarterly database of external assets and liabilities for the period 1983 to 2005, and employing a recently developed test of out-of-sample predictive ability, we show that this measure is able to predict the real exchange rate at horizons of up to 2 years. Out-of-sample evidence of predictability tends to get stronger as the size of the window used to estimate the parameters increases. This is probably because of the greater relative importance of the external balance in the dynamics of the exchange rate in the last few years, or because of the increasing precision of parameter estimates with the sample size. When we break down our measure of external imbalances into its three components: exports to imports ratio, exports to assets ratio and assets to liabilities ratio, we find that out-of-sample predictability is mainly driven by the last two ratios.

Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates

In this paper we focus on combining out-of-sample test statistics of the Martingale Difference Hypothesis (MDH) to explore whether a new combined statistic may induce a test with higher asymptotic power. Asymptotic normality implies that more power can be achieved by finding the optimal weight in a combined t-ratio. Unfortunately, this optimal weight is degenerated under the null of no predictability. To overcome this problem we introduce a penalization function that attracts the optimal weight to the interior of the feasible combination set. The new optimal weight associated with the penalization problem is well defined under the null, ensuring asymptotic normality of the resulting combined test. We show, via simulations, that our proposed combined test displays important gains in power and good empirical size. In fact, the new test outperforms its single components displaying gains in power up to 45%. Finally, we illustrate our approach with an empirical application aimed at testing predictability of Chilean and Canadian exchange rate returns.

Shrinkage Based Tests of the Martingale Difference Hypothesis

In this paper we define a family of tests for the Martingale Difference Hypothesis (MDH) based upon a shrinkage principle. Tests within this family are such that rejection of the null implies that forecasts from the alternative model, adjusted by a shrinkage factor, will display lower Mean Square Prediction Error (MSPE) than forecasts from the null model. This generalizes most previous tests which compare forecast errors of one model, the null, to errors of the plain alternative model, not allowing for shrinkage. We argue that tests derived from this shrinkage approach display in general better small sample properties than MSPE based tests of the MDH. This occurs because the shrinkage based tests implicitly consider the reduced variance benefits of shrinkage estimators. Finally, we illustrate the use of our tests in an empirical application within the exchange rate literature

Conditional Evaluation of Exchange Rate Predictive Ability in Long Run Regressions

In this paper we evaluate exchange rate predictability using a new framework developed by Giacomini and White (2004). In this new framework we test for conditional predictive ability rather than for unconditional predictive ability, which has been the usual approach thus far. Using several shrinkage based forecasting methods, including new methods proposed here, we evaluate conditional predictability of five bilateral exchange rates at differing horizons. Our results indicate that for most currencies a random walk would not be the best forecasting method in a real time forecasting exercise, at least for some predictive horizons. We also show that our proposed shrinkage methods in general perform on par with Bayesian shrinkage and ridge regressions, and sometimes they even perform better.