Should foreign aid be tied or untied? We study this question in the context of a dynamic growth model in which agents can accumulate human capital through education. We compare the growth and welfare implications of three polar scenarios in which foreign aid is either (i) completely untied, (ii) tied to public investment in infrastructure, or (iii) tied to public spending on education. Our results indicate that tying aid to education is more beneficial from a long-run welfare perspective than the two alternative scenarios. We also compute the optimal allocation of foreign aid and find that the largest fraction of aid flows ought to be tied to public spending on education. Finally, we study the transitional dynamics of the recipient economy following an aid inflow and find that aid programs that are tied (entirely or partially) to public spending on education generally entail some welfare losses in the short run.

This paper introduces imperfect knowledge and learning behavior of economic agents into the Kiyotaki and Moore model and studies the interaction of agents' collateral price beliefs, collateral constraint, and aggregate economic activity over the business cycle. It establishes the E-stability condition and the convergence of the real time learning process. In addition, it shows that learning strengthens the role of collateral constraints in aggregate fluctuations.

A nation's economy is a complex nonlinear dynamical system with links to other national economies. Classical macroeconomic models typically incorporate linear approximations to the nonlinear world and add simple dynamics to capture adjustments over time. Most of these simplifications offer little insight into the nonlinear structure of economic relationships that exist. Nor do they provide useful predictions beyond the short-term predictions of the autoregressive linear models from which they are derived.

Income drops permanently after an involuntary job displacement, but it has never been clear what happens to long run wealth in the USA. Upon displacement, wealth falls 14% relative to workers of the same age and similar education from the Panel Study of Income Dynamics (PSID). Their wealth is still 18% lower 12 years after the event. A standard life cycle model calibrated to US data with permanent decreases in income after displacement behaves differently than these findings. The agents in the model also experience a large drop in wealth but they recover. The biggest culprit for these differences is small and statistically insignificant changes to consumption in the PSID whereas agents in the model decrease their consumption considerably. Extending the model to include habit formation reconciles some of these differences by generating similar long run effects on wealth. This allows for the examination of wealth at death through the lens of the model.

This paper systematically examines the interrelations between equilibrium indeterminacy, endogenous entry and exit of intermediate input firms, and increasing returns to specialization within two versions of a parsimonious one-sector monopolistically competitive RBC model. The technology for producing an intermediate good is postulated to display internal increasing returns to scale in our benchmark framework, whereas positive productive externalities are considered in the alternative setting. We analytically show that either formulation will exhibit belief-driven cyclical fluctuations provided the equilibrium wage-hours locus is positively sloped and steeper than the household’s labor supply curve. We also find that ceteris paribus our alternative macroeconomy is more susceptible to indeterminacy and sunspots than the baseline counterpart.

This paper quantitatively examines the macroeconomic effects of size-dependent financial frictions on capital misallocation and aggregate total factor productivity. Based on panel data from China’s manufacturing sector, I find that among non-state-owned enterprises, (i) the dispersion of the marginal product of capital is large and persistent and (ii) large firms tend to have higher leverage, and lower mean and dispersion of the marginal product of capital than their small counterparts. This paper analyzes a dynamic stochastic general equilibrium model with heterogeneous agents and size-dependent financial frictions. By calibrating the model to a Chinese firm-level dataset, I show that in addition to matching the aforementioned stylized facts, the economy with a size-dependent borrowing constraint is able to reproduce the observed negative correlation between firm size and the marginal product of capital, as well as generate quantitatively modest TFP loss. Furthermore, ignoring firms’ size-dependent financing patterns may lead to an overstatement of TFP loss due to financial frictions.

We present a new methodology that uses professional forecasts to estimate the effects of fiscal policy. We use short-term forecasts to better identify exogenous shocks to government spending by controlling for anticipatory information already in the public domain. We use longer-term forecasts to net out expectations from the future path of other variables, which improves accuracy and efficiency by focusing on more precise measures of the impact of shocks. We show that this improves the statistical fit relative to both local projection methods and vector autoregression-based analyses that do not control for the entire future path of expectations.

Economists have long been interested in the effect of business sentiment on economic activity. Using text analysis, I construct a new company-level indicator of sentiment based on the net balance of positive and negative words in Australian company disclosures. Company-level investment is very sensitive to changes in this corporate sentiment indicator, even controlling for fundamentals, such as Tobin’s Q, as well as controlling for measures of company-level uncertainty.

The high sensitivity of investment to sentiment could be due to several mechanisms. It could be because of animal spirits among managers or because of sentiment proxies for private information held by managers about company prospects. Overall, I find mixed evidence of the underlying causal mechanism. The effect of sentiment on investment is relatively persistent, which is consistent with managers having private information about company fundamentals. But the sensitivity of investment to sentiment is not any stronger at opaque companies in which managers are likely to be better informed than investors. Further, investment is sensitive to sentiment even when investors have an information advantage over managers by lagging the sentiment indicator by a year. Overall, the sensitivity of investment to sentiment appears to reflect both animal spirits and fundamentals.

Corporate investment has been weak since the global financial crisis (GFC) and demand-side factors, such as lower sales growth, explain more than half of this weakness. Low sentiment and heightened uncertainty weighed on investment during the GFC but have been less important factors since then.

This paper develops an efficient Stein-like shrinkage estimator for estimating slope parameters under structural breaks in seemingly unrelated regression models, which is then used for forecasting. The proposed method is a weighted average of two estimators: a restricted estimator that estimates the parameters under the restriction of no break in the coefficients, and an unrestricted estimator that considers break points and estimates the parameters using the observations within each regime. It is established that the asymptotic risk of the Stein-like shrinkage estimator is smaller than that of the unrestricted estimator, which is the method typically used to estimate the slope coefficients under structural breaks. Furthermore, this paper proposes an averaging minimal mean squared error estimator in which the averaging weight is derived by minimizing its asymptotic risk. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and through an empirical example of forecasting output growth of G7 countries.

This paper studies the role of stochastic volatility in a setting where housing serves as an important propagation mechanism. After showing time-varying volatility in US house prices, we estimate a dynamic stochastic general equilibrium model with housing, financial frictions, and stochastic volatility using a nonlinear approximation and Bayesian econometric techniques. Incorporating stochastic volatility into the model greatly improves model fit and accounts for approximately half of the increased volatility in house prices observed during the Great Recession. Increased stochastic volatility escalates uncertainty which has significant effects on macroeconomic variables. While uncertainty in most sectors has negative effects on the economy, uncertainty on collateral constraints has the largest role. Unlike other uncertainty shocks, the housing demand uncertainty creates positive spillovers in the economy. Credit conditions, adjustment costs of capital and housing, and monetary policy are important transmission mechanisms for the stochastic volatility shocks.

This paper extracts housing boom-bust cycle signals from metropolitan statistical area (MSA)-level housing prices using a Markov-switching dynamic factor model. To mitigate the estimation bias, it utilizes high-frequency housing prices that follow the methodology of the monthly Case–Shiller house price indices. The housing bust phases specified from weekly and daily housing prices precede those based on monthly prices by approximately 2 years. MSAs with top signal-to-noise ratios offer greater marginal contributions to improvements in forecasting housing cycles than MSAs with bottom ratios for all frequencies. The results highlight the importance of indicator quality and provide evidence against “The more, the better” since incorporating more MSA-level housing prices into housing factors does not guarantee more satisfactory housing cycle forecasts.

This paper studies the dynamic interactions between the money supply and the shape of the yield curve in the context of a regime-switching latent factor model. Estimates show that the money supply has important implications for the level, slope, and curvature of the yield curve. Moreover, the Divisia aggregates can provide more information than simple-sum aggregates based on parameter estimates and impulse response functions in understanding the dynamics of the yield curve. The favored broad Divisia aggregate could especially be associated with changes in the yield curve’s level, slope, and curvature over the business cycle. Therefore, this paper highlights the important role of Divisia aggregates in the linkage between financial markets and monetary policy.