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Housing demand and community choice: an empirical analysis
Rapaport, Carol (1996)
Housing demand reflects the household's simultaneous choice of neighborhood, whether to own or rent the dwelling, and the quantity of housing services demanded. Existing literature emphasizes the final two factors, but overlooks the choice of community. This paper develops an econometric model that incorporates all three components, and then estimates this model using a sample of households in Tampa, Florida. Incorporating community choice increases the price elasticity of demand and reduces the differential between white and comparable nonwhite households. The results are robust to the ...
Staff Reports , Paper 16

Working Paper
Evaluating Conditional Forecasts from Vector Autoregressions
McCracken, Michael W.; Clark, Todd E. (2014-10-02)
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical,Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout ...
Working Papers (Old Series) , Paper 1413

Working Paper
Large Vector Autoregressions with Stochastic Volatility and Flexible Priors
Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano (2016-06-30)
Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying ...
Working Papers (Old Series) , Paper 1617

Working Paper
FISCAL SURPRISES AT THE FOMC
van Norden, Simon; Croushore, Dean (2017-06-20)
This paper provides a detailed examination of a new set of fiscal forecasts for the U.S. assembled by Croushore and van Norden (2017) from FOMC briefing books. The data are of particular interest because (1) they afford a look at fiscal forecasts over six complete business cycles and several fiscal policy regimes, covering both peacetime and several wars, (2) the forecasts were precisely those presented to monetary policymakers, (3) they include frequently updated estimates of both actual and cyclically adjusted deficits, (4) unlike most other U.S. fiscal forecasts, they were neither partisan ...
Working Papers , Paper 17-13

Working Paper
Forecasting Consumption Spending Using Credit Bureau Data
Croushore, Dean; Wilshusen, Stephanie M. (2020-06-04)
This paper considers whether the inclusion of information contained in consumer credit reports might improve the predictive accuracy of forecasting models for consumption spending. To investigate the usefulness of aggregate consumer credit information in forecasting consumption spending, this paper sets up a baseline forecasting model. Based on this model, a simulated real-time, out-of-sample exercise is conducted to forecast one-quarter ahead consumption spending. The exercise is run again after the addition of credit bureau variables to the model. Finally, a comparison is made to test ...
Working Papers , Paper 20-22

Journal Article
Do Low Survey Response Rates Threaten Data Dependence?
Leduc, Sylvain; Oliveira, Luiz E.; Paulson, Caroline M. (2025-03-31)
Monetary policy is forward-looking and dependent on policymakers’ economic outlook. When the outlook is deemed highly uncertain, policymakers may put more weight on incoming data when making monetary policy considerations. However, falling survey response rates suggest employment and inflation data may have become less reliable. Analysis of payroll employment and consumer price inflation data shows that data revisions over the past few years have been in line with their pre-pandemic averages. This suggests that these data have not been an outsized source of uncertainty in recent years.
FRBSF Economic Letter , Volume 2025 , Issue 07 , Pages 5

Discussion Paper
The FRBNY DSGE Model Forecast
Eusepi, Stefano; Cocci, Matthew; Shahanaghi, Sara; Giannoni, Marc; Del Negro, Marco (2014-09-26)
The U.S. economy has been in a gradual but slow recovery. Will the future be more of the same? This post presents the current forecasts from the Federal Reserve Bank of New York’s (FRBNY) DSGE model, described in our earlier “Bird’s Eye View” post, and discusses the driving forces behind the forecasts. Find the code used for estimating the model and producing all the charts in this blog series here. (We should reiterate that these are not the official New York Fed staff forecasts, but only an input to the overall forecasting process at the Bank.)
Liberty Street Economics , Paper 20140926

Working Paper
GDPNow: A Model for GDP "Nowcasting"
Higgins, Patrick C. (2014-07-01)
This paper documents GDPNow, a "nowcasting" model for gross domestic product (GDP) growth that synthesizes the "bridge equation" approach relating GDP subcomponents to monthly source data with the factor model approach used by Giannone, Reichlin, and Small (2008). The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the U.S. Bureau of Economic Analysis. Using current vintage data, out-of-sample GDPNow model forecasts are found to be more accurate than a number of statistical benchmarks since 2000. Using real-time ...
FRB Atlanta Working Paper , Paper 2014-7

Discussion Paper
Reintroducing the New York Fed Staff Nowcast
O’Keeffe, Hannah; Sbordone, Argia M.; Baker, Katie; Almuzara, Martín (2023-09-08)
“Nowcasts” of GDP growth are designed to track the economy in real time by incorporating information from an array of indicators as they are released. In April 2016, the New York Fed’s Research Group launched the New York Fed Staff Nowcast, a dynamic factor model that generated estimates of current quarter GDP growth at a weekly frequency. The onset of the COVID-19 pandemic sparked widespread economic disruptions—and unprecedented fluctuations in the economic data that flow into the Staff Nowcast. This posed significant challenges to the model, leading to the suspension of publication ...
Liberty Street Economics , Paper 20230908

Working Paper
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
Chudik, Alexander; Sharifvaghefi, Mahrad; Pesaran, M. Hashem (2021-04-17)
This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows and exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection, which is complicated by time variations in the effects of signal variables. In this study we investigate whether or not we should use weighted observations at the variable selection stage in the presence of ...
Globalization Institute Working Papers , Paper 394

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