Forecastable Component Analysis (ForeCA)

I introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converging algorithm with a fast eigenvector solution. Applications to financial and macro-economic time series show that ForeCA can successfully discover informative structure, which can be used for forecasting as well as classification. The R package ForeCA (http://cran.r-project.org/web/packages/ForeCA/index.html) accompanies this work and is publicly available on CRAN.Comment: 10 pages, 4 figures; ICML 201

Dynamic Properties of the New Neoclassical Synthesis Model of Business Cycle

Linear and Hodrick-Prescott detrending methods do not provide a good approximation of the business cycle when output contains a unit root. I use the multivariate Beveridge-Nelson decomposition to document the main patterns of US postwar business cycle when output and some other variables are assumed to be integrated I(1) processes. I show that the business cycle identified in this way displays some important differences with those obtained from the preceding methods. I then evaluate the ability of various dynamic general equilibrium (DGE) models to replicate the main aspects of this business cycle. Among competing models, I find that the best specification involves an economy hit simultaneously by both technological and monetary shocks, in a context of price stickiness and limited (but not sufficient) accommodation by the monetary authorities. Hence, the data favor the model advocated by the New-Neoclassical Synthesis rather than its purely classical (RBC type) or purely Keynesian counterparts.

Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?

This paper develops univariate and multivariate forecasting models for realized volatility in Australian stocks. We consider multivariate models with common features or common factors, and we suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large. Our forecast analysis shows that multivariate models outperform univariate models, but that there is little difference between simple and sophisticated factor models.

Is the Business Cycles a Necessary Consequence of Stochastic Growth?

We compute the forecastable changes in output, consumption, and hours implied by a VAR that includes the growth rate of private value added, the share of output that is consumed, and the detrended level of private hours. We show that the size of the forecastable changes in output greatly exceeds that predicted by a standard stochastic growth model, of the kind studied by real business cycle theorists. Contrary to the model's implications, forecastable movements in labor productivity are small and only weakly related to forecasted changes in output. Also, forecasted movements in investment and hours are positively correlated with forecasted movements in output. Finally, and again in contrast to what the growth model implies, forecasted output movements are positively related to the current level of the consumption share and negatively related to the level of hours. We also show that these contrasts between the model and the observations are robust to allowance for measurement error and a variety of other types of transitory disturbances.

Indeterminacy, Aggregate Demand, and the Real Business Cycle

We show that under indeterminacy aggregate demand shocks are able to explain not only aspects of actual °uctuations that standard RBC models predict fairly well, but also aspects of actual °uctuations that standard RBC models cannot explain, such as the hump-shaped, trend reverting impulse responses to transitory shocks found in US output (Cogley and Nason, AER, 1995); the large forecastable movements and comovements of output, consumption and hours (Rotemberg and Woodford, AER, 1996); and the fact that consumption appears to lead output and investment over the business cycle. Indeterminacy arises in our model due to capacity utilization and mild increasing returns to scale.

Dynamic General Equilibrium Models and the Beveridge-Nelson Facts

Linear and Hodrick-Prescott detrending methods do not provide a good approximation of the business cycle when output contains a unit root. I use the multivariate Beveridge-Nelson decomposition to document the main patterns of US postwar business cycles when output and some other variables are assumed to be integrated I(1) processes. I show that the business cycle identified in this way displays some important differences with those obtained from the preceding methods. I then evaluate the ability of various dynamic stochastic general equilibrium (DSGE) models to replicate the main aspects of this business cycle. Among competing models, I find that the best specification involves an economy hit simultaneously by both technological and monetary shocks, in a context of price stickiness and limited (but insufficient) accommodation by the monetary authorities. Hence, the data favor the model advocated by the New-Neoclassical Synthesis rather than its purely classical (RBC type and flexible price) counterparts.Business cycles, Beveridge-Nelson decomposition, Prices rigidity

The Evolution of Inequality, Heterogeneity and Uncertainty in Labor Earnings in the U.S. Economy

A large empirical literature documents a rise in wage inequality in the American economy. It is silent on whether the increase in inequality is due to greater heterogeneity in the components of earnings that are predictable by agents or whether it is due to greater uncertainty faced by agents. Applying the methodology of Cunha, Heckman and Navarro (2005) to data on agents making schooling decisions in different economic environments, we join choice data with earnings data to estimate the fraction of future earnings that is forecastable and how this fraction has changed over time. We find that both predictable and unpredictable components of earnings have increased in recent years. The increase in uncertainty is substantially greater for unskilled workers. For less skilled workers, roughly 60% of the increase in wage variability is due to uncertainty. For more skilled workers, only 8% of the increase in wage variability is due to uncertainty. Roughly 26% of the increase in the variance of returns to schooling is due to increased uncertainty. Using conventional measures of income inequality masks the contribution of rising uncertainty to the rise in the inequality of earnings for less educated groups.

Family networks and household outcomes in an economic crisis

Thesis (Ph.D.)--Boston UniversityThis thesis theoretically and empirically analyzes the nature and consequences of interactions between family members. The first chapter tests whether children's human capital accumulation was significantly affected by earnings shocks to their nonresident kin in the context of the 1997-8 financial crisis in Indonesia. The crisis produced sudden, heterogeneous shocks that facilitate the construction of an exogenous measure of earnings changes. Results indicate that earnings shocks to nonresident kin - including extended family and relatives living in other districts- significantly affected children's human capital accumulation between 1997 and 2000, and ultimate educational attainment measured nearly a decade after the crisis hit. Supplementary results point to intra-family transfers, underpinned by ex post altruism, as an important channel of causation. The second chapter develops a theoretical model of private transfers underpinned by ex post altruism among members of a network. I use this model to analyze equilibrium transfer patterns and inequality under alternative income distributions and network structures. I demonstrate the general intuition that transfers obtain in equilibrium when the amount of altruism is sufficiently strong relative to income inequality. Within the networks that I analyze, every equilibrium involving transfers takes the same form: unique income thresholds separate senders from receivers. Effective risk sharing takes place among senders and receivers, while those at intermediate incomes remain in autarky. Every equilibrium gives rise to the same set of allocations. I contrast these predictions with insurance-based theories of transfers in which risk sharing is operative for small in come differences and may fall apart at large income differences. The third chapter uses longitudinal data spanning nearly fifteen years to test whether transfers among family members within Indonesia are consistent with ex post altruism, against the alternative of insurance. I use the predicted effects of permanent versus transitory income on transfers, as well as theoretical predictions from the second chapter regarding the shape of transfer functions , to carry out this test. The results provide some evidence that transfer motives are inconsistent with insurance but consistent with ex post altruism

Macro Factors in Bond Risk Premia

Empirical evidence suggests that excess bond returns are forecastable by financial indicators such as forward spreads and yield spreads, a violation of the expectations hypothesis based on constant risk premia. But existing evidence does not tie the forecastable variation in excess bond returns to underlying macroeconomic fundamentals, as would be expected if the forecastability were attributable to time variation in risk premia. We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond returns and macroeconomic fundamentals. We find that several common factors estimated from a large dataset on U.S. economic activity have important forecasting power for future excess returns on U.S. government bonds. Following Cochrane and Piazzesi (2005), we also construct single predictor state variables by forming linear combinations of either five or six estimated common factors. The single state variables forecast excess bond returns at maturities from two to five years, and do so virtually as well as an unrestricted regression model that includes each common factor as a separate predictor variable. The linear combinations we form are driven by both "real" and "inflation" macro factors, in addition to financial factors, and contain important information about one year ahead excess bond returns that is not captured by forward spreads, yield spreads, or the principal components of the yield covariance matrix.