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Working Paper
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common ...
Working Paper
Reconciled Estimates of Monthly GDP in the US
In the US, income and expenditure-side estimates of GDP (GDPI and GDPE) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error ...
Report
Forecasting in large macroeconomic panels using Bayesian Model Averaging
This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time ...
Report
Prior elicitation in multiple change-point models
This paper discusses Bayesian inference in change-point models. Current approaches place a possibly hierarchical prior over a known number of change points. We show how two popular priors have some potentially undesirable properties, such as allocating excessive prior weight to change points near the end of the sample. We discuss how these properties relate to imposing a fixed number of change points in the sample. In our study, we develop a hierarchical approach that allows some change points to occur out of the sample. We show that this prior has desirable properties and handles cases with ...
Report
A flexible approach to parametric inference in nonlinear time series models
Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications - and does so in a simple way that allows for straightforward Bayesian inference. The basic idea underlying our model is that it adds two concepts to a standard state space framework. These ideas are ordering and distance. By ordering the data in different ways, we can accommodate a wide range of nonlinear time series models. By allowing the state equation variances to ...
Report
Are apparent findings of nonlinearity due to structural instability in economic time series?
Many modeling issues and policy debates in macroeconomics depend on whether macroeconomic times series are best characterized as linear or nonlinear. If departures from linearity exist, it is important to know whether these are endogenously generated (as in, for example, a threshold autoregressive model) or whether they merely reflect changing structure over time. We advocate a Bayesian approach and show how such an approach can be implemented in practice. An empirical exercise involving several macroeconomic time series shows that apparent findings of threshold-type nonlinearities could be ...
Working Paper
Tail Forecasting with Multivariate Bayesian Additive Regression Trees
We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our ...
Report
Reexamining the consumption-wealth relationship: the role of model uncertainty
In their influential work on the consumption-wealth relationship, Lettau and Ludvigson found that while consumption responds to permanent changes in wealth in the expected manner, most changes in wealth are transitory with no effect on consumption. We investigate the robustness of these results to model uncertainty using Bayesian model averaging. We find that there is model uncertainty with regard to the number of cointegrating vectors, the form of deterministic components, lag length, and whether the cointegrating residuals affect consumption and income directly. Whether this uncertainty has ...
Working Paper
A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations
A knowledge of the level of trend inflation is key to many current policy decisions, and several methods of estimating trend inflation exist. This paper adds to the growing literature which uses survey-based long-run forecasts of inflation to estimate trend inflation. We develop a bivariate model of inflation and long-run forecasts of inflation which allows for the estimation of the link between trend inflation and the long-run forecast. Thus, our model allows for the possibilities that long-run forecasts taken from surveys can be equated with trend inflation, that the two are completely ...
Working Paper
Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, ...