Rob Roy McGregor

Partisan Monetary Policies: Presidential Influence Through the Power of Appointment

We investigate the channels through which partisan influence from a Presidential administration could affect monetary policy-making. Influence could be a result of direct Presidential pressure exerted on members of the Federal Open Market Committee (FOMC), or it could be a result of partisan considerations in Presidential appointments to the Board of Governors. To investigate these two channels of influence, we devise and apply a method for estimating parameters of monetary policy reaction functions that can vary across individual members of the FOMC. Our results suggest that the appointments process is the primary mechanism by which partisan differences in monetary policies arise.

Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

Several studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GPs potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.

A Long History of FOMC Voting Behavior

We devise and apply a method for estimating monetary policy reaction functions for individual members of the Federal Open Market Committee (FOMC) of the Federal Reserve. Our method uses members’ votes on the monetary policy directive in FOMC meetings as the key source of data on individual preferences. The analysis provides a ranking by preference for ease for 84 FOMC members who served during the 1966–1996 period.

Deliberation and Learning in Monetary Policy Committees

We use records from Federal Open Market Committee (FOMC) meetings to investigate the importance of deliberation and learning in monetary policy decision making in the period from 1970 to 1978 when Arthur Burns served as Chairman. We first propose a model of Bayesian learning in which FOMC members learn from each other as they sequentially reveal their policy preferences in a committee meeting. Then, as an alternative, we investigate a model in which members defer to an emerging consensus. Neither model is supported by the data, suggesting that within‐meeting deliberation might have had little effect on the quality of monetary policy decisions in the Burns era.

Forecasting economic time series with the DyFor genetic program model

Genetic programming (GP) uses the Darwinian principle of survival of the fittest and sexual recombination to evolve computer programs that solve problems. Several studies have applied GP to forecasting with favourable results. However, these studies, like others, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ‘dynamic’ GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates methods to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested on real-world economic time series, namely the US Gross Domestic Product and Consumer Price Index Inflation. Results show that the DyFor GP model outperforms benchmark models from leading studies for both experiments. These findings affirm the DyFor GPs potential as an adaptive, nonlinear forecasting model.

Forecasting with a dynamic window of time: the DyFor genetic program model

Several studies have applied genetic programming (GP) to the task of forecasting with favourable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new dynamic GP model that is specifically tailored for forecasting in non-static environments. This Dynamic Forecasting Genetic Program (DyFor GP) model incorporates methods to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is realised and tested for forecasting efficacy on real-world economic time series, namely the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the DyFor GP model outperforms benchmark models from leading studies for both experiments. These findings affirm the DyFor GPs potential as an adaptive, non-linear model for real-world forecasting applications and suggest further investigations.

The lower bound and the causes of monetary policy inertia: evidence from Sweden

ABSTRACT In 2009, in the midst of a global recession, Sweden’s Riksbank approached a lower bound on nominal interest rates. This encounter with the lower bound provides a natural experiment for investigating the causes of monetary policy inertia. To exploit this experiment, we estimate Taylor rules with Tobit specifications that permit both interest rate smoothing and persistent shocks (serial correlation) as explanations for inertia. The interest rate smoothing hypothesis leads to a specification in which lagged actual values of the dependent variable appear on the right-hand side of the Taylor rule, while the persistent shocks hypothesis leads to a specification in which lagged values of an unobserved latent dependent variable appear on the right-hand side of the Taylor rule. The divergence of actual and latent dependent variables that occurs at the lower bound provides leverage in distinguishing the two hypotheses. For a conventional Taylor rule, we find evidence of both sources of inertia. For a modified Taylor rule that includes a measure of financial stress, our evidence suggests that interest rate smoothing is the principal source of monetary policy inertia.

Taylor Rules for Sweden's Riksbank

Sweden lapsed into a severe recession in 2008 but, unlike other countries, had a rapid and robust recovery. Because of its unique recession experience, it provides a revealing case for investigating monetary policy responses to macroeconomic fluctuations. We estimate Taylor rules for the Riksbank and for selected individuals who have served on the Riksbank monetary policy committee. Using pre-recession data, our estimates suggest that monetary policy was highly inertial. Estimates for samples including recession and recovery observations reveal more active responses to macroeconomic conditions and weaker indications of inertia. A key feature of our econometric work is the use of a dynamic Tobit specification to account for the lower bound on nominal interest rates encountered during the recession. Comparing alternative empirical specifications for handling the lower bound sheds light on why monetary policy might be inertial.

Economic growth among a cross-section of municipalities

Our work builds on the analysis of municipal population and income growth over the 1960-90 period presented by Glaeser et al. [Journal of Monetary Economics, 36, 1, August, 1995, pp. 11743]. We examine a broader sample of 600 U.S. municipalities and investigate differences in the growth experiences of larger and smaller cities and of wealthier and poorer cities. The results confirm the robustness of the conclusions of Glaeser et al. that municipal population and income growth are: 1) positively correlated with the initial level of educational attainment in the municipality; 2) negatively correlated with the initial municipal unemployment rate; and 3) negatively correlated with the initial share of municipal employment in manufacturing. Additionally, we find some evidence of convergence across cities in levels of population and per capita income and we introduce municipal annexation activity as an additional institutional determinant of municipal growth. Our results suggest that annexation has been an important source of population and income growth for U.S. municipalities over the 1960-90 period. This finding adds another dimension to the literature on the determinants and consequences of municipal annexation activity and suggests that annexation activity should not be ignored in analyses of municipal economic development.