How should central banks optimally aggregate sectoral inflation rates in the presence of imperfect labor mobility across sectors? We study this issue in a two-sector New-Keynesian model and show that a lower degree of sectoral labor mobility, ceteris paribus, increases the optimal weight on inflation in a sector that would otherwise receive a lower weight. We analytically and numerically find that, with limited labor mobility, adjustment to asymmetric shocks cannot fully occur through the reallocation of labor, thus putting more pressure on wages, causing inefficient movements in relative prices, and creating scope for central bank’ s intervention. These findings challenge standard central banks’ practice of computing sectoral inflation weights based solely on sector size and unveil a significant role for the degree of sectoral labor mobility to play in the optimal computation. In an extended estimated model of the US economy, featuring customary frictions and shocks, the estimated inflation weights imply a decrease in welfare up to 10% relative to the case of optimal weights.

According to search and matching theory, a greater availability of unemployed workers should make it easier for a firm to fill a vacancy, but more vacancies at other firms should make recruitment more difficult. Simulating a theoretical model of a firm facing perfect competition in the product market and no convex adjustment costs (standard assumptions in the search and matching literature), I find that shocks to vacancies and unemployment lead to economically significant employment responses. Simulating a more realistic model with imperfect competition in the product market and convex adjustment costs, I find small employment effects of shocks to vacancies and unemployment. In particular, shocks to the number of unemployed seem to be unimportant. Estimating an employment equation on a panel of Swedish firms, I find that neither the number of unemployed workers nor the number of vacancies in the local labor market is important for firms’ employment decisions.

This paper proposes a model to jointly explain two stylized facts observed in the recent empirical literature—the existence of a significant size of wealthy hand-to-mouth consumers and negative marginal propensities to consume associated with housing upgrades. The key ingredients of the model are a realistic set of housing choices, sizable down payment requirements, transaction costs, and endogenous borrowing constraints. Moreover, in the presence of unanticipated income shocks, this richness in marginal propensities to consume has significant implications for aggregate consumption and helps explain the puzzling increase in savings by low net worth households observed during the Great Recession as well as the consumption responses to recent tax rebates.

This paper studies the impact of immigration on the US macroeconomy. I identify structural vector autoregressions (SVARs) with time-varying parameters (TVPs) and stochastic volatility (SV) using a novel set of restrictions. The TVP-SV-SVARs are estimated on a quarterly sample including average labor productivity (ALP), hours worked, immigration, consumption, and term spread from 1953 to 2017. An immigration supply shock increases domestic ALP and hours worked over the business cycle horizons. Movements in immigration are explained by its own shock and to a lesser extent by the productivity and news shocks. IRFs driven by these shocks vary over the sample, especially around changes in immigration policy such as the Immigration Act of 1990. In contrast, the forecast error variance decompositions exhibit little change over the sample. Immigration plays an important role in the US macroeconomy.

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 study detects a structural break in international consumer price index (CPI) inflation comovement. We estimate the dynamic common factor models with unknown breakpoints of cross-country inflation rates and global price index of all commodities. We identify two global factors from the models: a commodity global factor and a noncommodity global factor. The former is a common factor between national inflation rates and commodity price index growth; the latter is a common factor among national inflation rates. The estimation of 29 countries’ quarterly CPI inflation data from 2001:Q1 to 2018:Q2 shows a one-time break in cross-country inflation dynamics in 2008:Q4. Thereafter, the importance of global factors in explaining the national inflation rates is remarkably increased. Furthermore, the increased global inflation synchronization is mainly driven by the larger role of the noncommodity global factor rather than that of the commodity global factor.

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.

This paper quantitatively examines the macroeconomic and welfare effects of macroprudential policies in open economies. We develop a small open economy dynamic stochastic general equilibrium (DSGE) model, where banks choose their funding sources (domestic vs. foreign deposits) and are subject to financial constraints. Our model predicts that banks reduce leverage in response to a macroprudential policy tightening, but increasingly rely on foreign funding. This endogenous shifts of funding composition significantly undermine the stabilizing effect and welfare gains of macroprudential policies. Our results also suggest macroprudential policies are less effective in financially more open economies, and optimal policy should take capital flows into consideration. Finally, we find empirical support for the model predictions in a group of developing and emerging economies.

Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

We present a multiregional endogenous growth model in which forward-looking agents choose their regions to live in, in addition to consumption and capital accumulation paths. The spatial distribution of economic activity is determined by the interplay between production spillover effects and urban congestion effects. We characterize the global stability of the spatial equilibrium states in terms of economic primitives such as agents’ time preference and intra- and interregional spillovers. We also study how macroeconomic variables at the stable equilibrium state behave according to the structure of the spillover network.

We employ a novel approach for analyzing the effects of relative consumption and relative wealth preferences on economic growth. In the pertinent literature, these effects are usually assessed by examining the dependence of the growth rate on the two parameters of the utility function that seem to measure the strength of the relative consumption and the relative wealth motives. Applying our fundamental factor approach, we identify specifications in which the traditional approach yields incorrect qualitative conclusions. The problematic specifications have the common unpleasant property that the parameter that seems to determine the strength of the relative consumption motive actually also affects the elasticity of intertemporal substitution of absolute consumption (and the strength of the relative wealth motive). Since the standard approach is unaware of the additional effect(s), it attributes the total change in the growth rate incorrectly to the change in the strength of the relative consumption motive.