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Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

This paper quantifies the effects of recent Federal Reserve interest rate changes, specifically recent hikes and cuts in the federal funds rate since 2015, on Midwest farmland values.

The authors apply three autoregressive distributed lag (ARDL) models to a panel data of state-level farmland values from 1963 to 2018 to estimate the dynamic effects of interest rate changes on the US farmland market. We focus on the I-states, Lakes states and Great Plains states. The models in the study capture both short-term and long-term impacts of policy changes on land values.

The authors find that changes in the federal funds rate have long-lasting impacts on farmland values, as it takes at least a decade for the full effects of an interest rate change to be capitalized in farmland values. The results show that the three recent federal funds rate cuts in 2019 were not sufficient to offset the downward pressures from the 2015–2018 interest rate hikes, but the 2020 cut is. The combined effect of the Federal Reserve's recent interest rate moves on farmland values will be positive for some time starting in 2022.

This paper provides the first empirical quantification of the immediate and long-run impacts of recent Federal Reserve interest rate moves on farmland values. The authors demonstrate the long-lasting repercussions of Federal Reserve's policy choices in the farmland market.

This study aims to address the fundamental question on how the major players in the economy dynamically interact with each other: among the central bank, the investors in the bond market and the firms and consumers that contribute to the economic growth, who gets information from whom, when and why?

To answer “who follows whom,” the authors apply a novel technique to examine the lead–lag relations between three time series, the federal funds rate, the treasury yield curve and the gross domestic product (GDP). To investigate “when and why,” the authors combine the lead–lag relations with principal component analysis to cluster economic states that are similar with respect to the eight macroeconomic variables.

The authors show that during the period 1977–2019, the bond market potentially obtained information from the federal funds rate (61% of the time) and less often (34% of time) from the changes in the GDP. Meanwhile, the funds rate decision by the Federal Reserve seems to lead the economic growth about 63% of the time. The analysis also suggests that the bond market obtained information directly from GDP when unemployment and inflation was high. In addition, the authors find that the federal funds rate was leading the GDP when the GDP deviated from the target value, consistent with the Federal Reserve’s policy of boosting and damping the economy when the GDP growth is low or high, respectively.

This study provides insights into the fundamental questions that have important implications for empirical work on the monetary policy, financial stability and economic activities.

This chapter examines the increased levels of cross-asset price comovement and its relationship with the recent rounds of “extraordinary intervention” from the US Federal Reserve. The results show that, even after controlling for the preceding financial crisis, asset return volatility, investor risk perceptions, and channels of monetary stimulus, historically unrelated financial asset returns experienced abnormal changes in their conditional correlations. The strength of these cross-asset correlations is directly linked to periods of Federal Reserve interventions yet disappear when the interventions were (in fact or were perceived to be) withdrawn. Despite being studied extensively in the academic literature, no traditional intervention channels can explain the changes in cross-comovement. It is proposed that the Fed’s extraordinary stimulus caused investors to use Fed announcements as a common, low-cost information source on which they used to make common portfolio-allocation decisions. The changes in comovement during the intervention period may have reduced investor welfare for those with longer-horizon allocation strategies, those not prepared for the eventual ending of the stimulus, and for underfunded liability-optimizing portfolio managers (e.g., state pension funds).

The purpose of this paper is to better understand the differences between community and non-community banks (CBs and Non-CBs) in the US. As the former have been declining in numbers, previous literature shows inherent differences between the business models of CBs and Non-CBs. This study attempts to gauge whether the impact of the reserve elimination during the Covid pandemic affected all banks similarly or whether community banks showed a differentiated response.

On March 26, 2020, the Federal Reserve, at the onset of the Covid pandemic, altered the depository institution reserve requirement for the first time since 1992. This significant change in policy led to the reserve requirement reduction from 10% to 0%. This study examines the impact of the 2020 reserve elimination on all community banks and non-community banks in the US and finds that although the level of cash to assets increased at both types of depository institutions post reserve elimination, the impact on liquidity-focused ratios was more pervasive at community banks in the first quarter post the regulatory shift. Among community banks, the largest depository institutions experienced the biggest balance sheet adjustments in the June 2020 quarter that followed the change in Federal Reserve’s policy. Further, the study finds that over two-quarters post reserve elimination, the non-community banks demonstrate a greater increase in balance sheet liquidity. Past literature shows that community banks tend to carry more liquidity than non-community banks and small community banks tend to carry more liquidity than their larger counterparts. These previous findings may provide some explanation for the different speed documented in this study at which various banks have reacted to the reserve elimination in 2020.

This research finds that community banks had a quicker response to the change in the reserve elimination, showing quick increases across liquidity ratios. The larger non-community banks tended to play catch up, increasing their liquidity in the subsequent quarter. The study also shows that the changes in liquidity were initially driven by the segment of large community banks.

This study looks at how the reserve elimination enacted by the Federal Reserve in March 2020 in response to the Covid pandemic affected community versus non-community banks. Currently, as far as the authors know, there are no other published papers that look at this issue.

This study is motivated by the view that Democrats are concerned with reducing unemployment in the short‐run, while Republicans are concerned with keeping inflation low to promote economic stability and growth. The purpose of this paper is to ask whether the Federal Reserve forecasts of nonfarm payroll employment are accurate and free of systematic bias during the 1977‐2000 Democratic and Republican administrations.

The authors employ comparable forecasts from a univariate autoregressive integrated moving‐average (ARIMA) model to assess forecast accuracy. An ARIMA model efficiently utilizes past information and thus yields desirable forecasts commonly used as benchmarks.

Federal Reserve forecasts during the Democratic Administrations, while failing to outperform the ARIMA forecasts, display systematic under‐prediction. Such evidence is consistent with a discretionary approach to monetary policy when the bias is in the direction of full employment. During the Republican Administrations, the Federal Reserve forecasts, while superior to the ARIMA forecasts, are free of systematic bias. This, we argue, is consistent with a monetary policy that approximated the Taylor rule.

The distinct behavior over the two administrations is unique to the nonfarm payroll employment forecasts and cannot be substantiated for the Federal Reserve forecasts of other macroeconomic variables.

This study provides new insights into the monetary policies pursued during the 1977‐2000 Democratic and Republican administrations. The findings are useful and informative for the design and implementation of monetary policy.

This chapter examines the long-run and short-run spillover effects of US quantitative easing (QE) on the money market in India. The study adopts the autoregressive distributed lag bounds testing co-integration approach for monthly data from September 2008 to May 2019 to investigate the spillover impact. The results reveal that a 10% rise in US QE led to a 25 bps softening of the weighted average call rate and a 2–5 bps hardening of the Treasury Bill. This impact was beyond the active participation by the Reserve Bank of India on the policy rate and liquidity in the system during the QE episodes. It suggests that the Indian money market is susceptible to US QE.

This chapter analyses the causes and effects of the financial crisis that commenced in 2008, and it examines the dramatic government rescues and reforms. The outcomes of this, the most severe collapse to befall the United States and the global economy for three-quarters of a century, are still unfolding. Banks, homeowners and industries stood to benefit from government intervention, particularly the huge infusion of taxpayer funds, but their future is uncertain. Instead of extending vital credit, banks simply kept the capital to cover other firm needs (including bonuses for executives). Industry in the prevailing slack economy was not actively seeking investment opportunities and credit expansion. The property and job markets languished behind securities market recovery. It all has been disheartening and scary – rage against those in charge fuelled gloom and cynicism. Immense private debt was a precursor, but public debt is the legacy we must resolve in the future.

The purpose of this analysis is to present the history of anti‐money laundering efforts in the United States as it applies both domestically and internationally, and demonstrate how this new legislation, if enacted, will mark a dramatic change in the customary treatment of international financial transactions and to international long‐arm jurisdiction and law enforcement. If enacted as proposed, this legislation may provide the tools necessary to achieve substantial progress in this effort.