Long-run forecasts of economic variables play an important role in policy, planning, and portfolio decisions. We consider forecasts of the long-horizon average of a scalar variable, typically the growth rate of an economic variable. The main contribution is the construction of prediction sets with asymptotic coverage over a wide range of data generating processes, allowing for stochastically trending mean growth, slow mean reversion, and other types of long-run dependencies. We illustrate the method by computing prediction sets for 10- to 75-year average growth rates of U.S. real per capita GDP and consumption, productivity, price level, stock prices, and population.

This paper proposes a model for, and investigates the consequences of, strong spatial dependence in economic variables. Our findings echo those of the corresponding “unit root” time series literature: Spatial unit root processes induce spuriously significant regression results, even with clustered standard errors or spatial HAC corrections. We develop large‐sample valid unit root and stationarity tests that can detect such strong spatial dependence. Finally, we use simulations to study strategies for valid inference in regressions with persistent spatial data, such as spatial analogues of first‐differencing transformations. Regressions from Chetty, Hendren, Kline, and Saez (2014) are used to illustrate the issues and methods.

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.

The conventional heteroskedasticity-robust (HR) variance matrix estimator for cross-sectional regression (with or without a degrees-of-freedom adjustment), applied to the fixed-effects estimator for panel data with serially uncorrelated errors, is inconsistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. We provide a bias-adjusted HR estimator that is √nT-consistent under any sequences (n T ) in which n and/or T increase to ∞. This estimator can be extended to handle serial correlation of fixed order.

Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. These and previously proposed estimators of cointegrating vectors are used to study long-run U.S. money (Ml) demand. Ml demand is found to be stable over 1900-1989; the 95% confidence intervals for the income elasticity and interest rate semielasticity are (.88,1.06) and (-.13, -.08), respectively. Estimates based on the postwar data alone, however, are unstable, with variances which indicate substantial sampling uncertainty.

Abstract Standard extreme value theory implies that the distribution of the largest observations of a large cross section is well approximated by a parametric model, governed by a location, scale and shape parameter. The extremes of a panel of independent cross sections are all governed by the same parameters as long as the underlying distribution as well as the size of the cross sections are time invariant. We derive inference about these parameters, and tests of the null hypothesis of time invariance, under asymptotics that do not require the number of extremes or the number of time periods to increase. We further apply Hamiltonian Monte Carlo techniques to estimate the path of time-varying parameters. We illustrate the approach in four examples of U.S. data: damages from weather-related disasters, financial returns, city sizes and firm sizes.

This paper examines empirically whether the measurement of trend inflation can be improved by using disaggregated data on sectoral inflation to construct indexes akin to core inflation but with a time-varying distributed lags of weights, where the sectoral weight depends on the timevarying volatility and persistence of the sectoral inflation series and on the comovement among sectors. The modeling framework is a dynamic factor model with time-varying coefficients and stochastic volatility as in Del Negro and Otrok (2008), and is estimated using U.S. data on seventeen components of the personal consumption expenditure inflation index.

Abstract-The mechanisms governing the relationship of money, prices and interest rates to the business cycle are the most studied and most disputed topics in macroeconomics. In this paper, we first document key empirical aspects of this relationship. We then ask how well three benchmark rational expectations macroeconomic models-a real business cycle model, a sticky price model and a liquidity effect model-account for these central facts. While the models have diverse successses and failures, none can account for the fact that real and nominal interest rates are "inverted leading indicators " of real economic activity. That is, none of the models captures the post-war U.S. business cycle fact that a high real or nominal interest rate in the current quarter predicts a low level of real economic activity two to four quarters in the future. Robert G. King and Mark W. Watson* In exploring the predictions of these models, we take the stock of money to be one of several exogenous variables in the system. All of our models are capable of generating a forecasting role for money relative to real economic activity, similar to that found in the U.S. data. In the real business model, monetary changes can forecast real activity because productivity is related to many underlying sources of shocks and because these real shocks also affect the money stock. In the models with "sticky prices " and "liquidity effects" I.

The US economy has performed better when the president of the United States is a Democrat rather than a Republican, almost regardless of how one measures performance. For many measures, including real GDP growth (our focus), the performance gap is large and significant. This paper asks why. The answer is not found in technical time series matters nor in systematically more expansionary monetary or fiscal policy under Democrats. Rather, it appears that the Democratic edge stems mainly from more benign oil shocks, superior total factor productivity (TFP) performance, a more favorable international environment, and perhaps more optimistic consumer expectations about the near-term future. (JEL D72, E23, E32, E65, N12, N42)

(CEPR) Business Cycle Dating Committee date business cycle turning points using a small number of aggregate measures of real economic activity. For example, in its memorandum explaining the December 2007 peak (NBER Business Cycle Dating Committee 2008), the NBER committee mentioned that it considers five series, quarterly real GDP and the “big four ” monthly series, real personal income less transfers, real manufacturing and wholesaleretail trade sales, industrial production, and nonfarm employment. (These series do not in general receive equal weight.) In contrast, when the NBER research program on dating business cycles commenced, researchers examined turning points in hundreds of series and dated business cycles by detecting clusters of specific-cycle turning points; see Arthur Burns and Wesley Mitchell (1946, 13 and 77–80). The dating of turning points evidently has shifted from aggregating the turning points of many disaggregated series to using the turning points of a few highly aggregated series. This shift raises a methodological question: should reference cycle turning points be determined by aggregating then dating, or by dating then aggregating? This paper provides some preliminary evidence on the question of whether it is better to date then aggregate or aggregate then date using 270 monthly disaggregated real economic indicators. The questions considered in this paper parallel those in the large literature on forecasting