A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function

A dynamic Tobit model with Time-varying parameters is proposed for the daily reaction function of the Open Market Desk of the US Federal Reserve. Such a model offers a more realistic depiction of the Desk's behavior than those of past contributions in the literature as it allows for both possible dynamics in the Desk's daily operations and for day-to-day time varying responses of the Desk to changing conditions in the reserves markets and in the short-term interest rates. Ensuing computational complications are overcome by employing Markov Chain Monte Carlo techniques for the estimation of the model. The results reveal a rich pattern of dynamic behavior by the Open Market Desk both inside the maintenance period and across maintenance periods and point towards a Desk which is highly adaptable to evolving conditions both in the economy in general and in the market for reserves in particularReserves, Federal Funds Rate, Open Market Operations, Open Market Desk, Censored Models, Data Augmentation, Markov Chain Monte Carlo, Gibbs Sampling, Time-Varying Parameter Models

Growth forecasts using time series and growth models

The authors consider two alternative methods of forecasting real per capita GDP at various horizons: 1) univariate time series models estimated country by country; and 2) cross-country growth regressions. They evaluate the out-of-sample forecasting performance of both approaches for a large sample of industrial and developing countries. They find only modest differences between the two approaches. In almost all cases, differences in median (across countries) forecast performance are small relative to the large discrepancies between forecasts and actual outcomes. Interestingly, the performance of both models is similar to that of forecasts generated by the World Bank's Unified Survey. The results do not provide a compelling case for one approach over another, but they do indicate that there are potential gains from combining time series and growth-regression-based forecasting approaches.Statistical&Mathematical Sciences,Economic Theory&Research,Scientific Research&Science Parks,Educational Technology and Distance Education,Public Health Promotion,Economic Forecasting,Economic Theory&Research,Achieving Shared Growth,Scientific Research&Science Parks,Science Education

Dynamic Limited Dependent Variable Modeling and US Monetary Policy

I estimate, using real-time data, a forward-looking monetary policy reaction function that is dynamic and that also accounts for the fact that there are substantial restrictions in the period-to-period changes of the Fed's policy instrument. I find a substantial contrast between the periods before and after Paul Volcker's appointment as Fed Chairman in 1979, both in terms of the Fed's response to expected inflation and in terms of its response to the (perceived) output gap: In the pre-Volcker era the Fed's response to inflation was substantially weaker than in the Volcker-Greenspan era; conversely, the Fed seems to have been more responsive to real activity in the pre-Volcker era than later

Nowcasting in Real Time Using Popularity Priors

This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth

Nowcasting in Real Time Using Popularity Priors

This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth

Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues

We revisit a central task of the extant liquidity literature, which is to identify effective measures of liquidity. We critically assess the influential practice of identifying the best liquidity measures based on monthly correlations by comparing and contrasting correlations between monthly and daily averages of high-frequency benchmarks and low-frequency proxies of liquidity, as well as by examining the coherences between such measures. Furthermore, we propose MIDAS regressions as a way of investigating the bilateral relationships between benchmarks and proxies without averaging out potentially valuable high-frequency information, as is common practice. We conclude that the empirical correlations between benchmarks and proxies in general become weaker as the frequency over which these relationships are examined becomes higher, and that standard practices may lead to misleading conclusions. One implication of our results is that any liquidity measure needs to be assessed against the relevant timeframe for conversion into cash