In this paper, we analyze the effects of the stimulus packages adopted by the German government during the Great Recession. We employ a standard medium-scale dynamic stochastic general equilibrium (DSGE) model extended by non-optimizing households and a detailed fiscal sector. In particular, the dynamics of spending and revenue variables are modeled as feedback rules with respect to the cyclical components of output, hours worked and private investment. Based on the estimated rules, fiscal shocks are identified. According to the results, fiscal policy, in particular public consumption, investment, and transfers prevented a sharper and prolonged decline of German output at the beginning of the Great Recession, suggesting a timely response of fiscal policy. The overall effects, however, are small when compared to other domestic and international shocks that contributed to the economic downturn. Our overall findings are not sensitive to considering fiscal foresight.

Gold is a globally traded asset and held in large quantities by investors and central banks. Since there is no established model to assess whether the price of gold is overvalued or undervalued, we propose a relative valuation framework based on gold price ratios. This idea is not confined to gold but offers the foundation for relative valuation of a broad range of different assets or asset classes. We analyze gold prices relative to commodity prices, consumer prices, stock prices, dividend, and bond yields and find that the relative value of gold varies significantly over time. An analysis of the factors which drive these variations demonstrates that inflation expectations and uncertainty have a strong influence on gold ratios while macroeconomic fundamentals are less important. More specifically, a boost in confidence decreases the relative price of gold while heightened uncertainty increases the relative price of gold, which confirms the role of gold as a safe haven.

This paper extends the ongoing literature on the macroeconomic effects of money supply volatility. We use monthly data for the USA and a bivariate, Markov switching, structural vector error correction model that is modified to accommodate generalized autoregressive conditional heteroscedasticity-in-mean errors to isolate the effects of money growth volatility on output growth. The model allows us to study how monetary uncertainty affects economic growth across different macroeconomic regimes.

We consider a model-averaged forecast-based estimate of the output gap to measure economic slack in 10 industrialized economies. Our measure takes changes in the long-run growth rate into account and, by addressing model uncertainty using equal weights on different forecast-based estimates, is robust to different assumptions about the underlying structure of the economy. For all 10 countries in the sample, we find that the estimated output gap has much larger negative movements during recessions than positive movements in expansions, suggesting business cycle asymmetry is an intrinsic characteristic of industrialized economies. Furthermore, the estimated output gap is always strongly negatively correlated with future output growth and unemployment and positively correlated with capacity utilization. It also implies a convex Phillips Curve in many cases. The model-averaged output gap is reliable in real time in the sense of being subject to relatively small revisions.

We build a two-country New-Keynesian DSGE model of a Currency Union to study the effects of fiscal policy coordination, by evaluating the stabilization properties and welfare implications of different fiscal policy scenarios. Our main findings are that a government spending rule which targets the net exports gap rather than the domestic output gap produces more stable dynamics and that consolidating government budget constraints across countries with symmetric tax rate movements provides greater stabilization. A key role is played by the trade elasticity which determines the impact of the terms of trade on net exports. In fact, when goods are complements, the stabilization properties of coordinating fiscal policies are no longer supported. These findings point out to possible policy prescriptions for the Euro Area: to coordinate fiscal policies by reducing international demand imbalances, either by stabilizing trade flows across countries or by creating some form of Fiscal Union or both.

Understanding the causes of the Industrial Revolution, namely the process of transition from a Malthusian equilibrium to modern economic growth, has been the subject of passionate debate. This paper contributes to insights into the process of industrialization and the demographic transition that followed. We present a model that proposes a mechanism behind the claim that landed elites had strong incentives to block education reforms. By applying the theory of interest groups to landownership, landowners could delay education. However, they could not prevent its introduction indefinitely since gains for the landed elites derived from education would at some moment surpass the costs associated with them. We also sustain that improvements in agricultural productivity prior to the Industrial Revolution may have induced a positive impact on the landowners’ decision to educate the population, which led to an earlier introduction of education reforms. The conclusions fit the patterns of the late boom of industrialization and demographic transition and help explain why some countries (e.g., Britain and The Netherlands) had accelerated education reforms and a faster process of industrialization than most continental countries. A theoretical model is presented, and numerical simulations are exhibited to illustrate our claims.

In this paper we estimate a New-Keynesian dynamic stochastic general equilibrium (NK DSGE) model with heterogeneity in price and wage setting behavior. In a recent study, Coibion and Gorodnichenko develop a DSGE model, in which firms follow four different types of price setting schemes: sticky prices, sticky information, rule-of-thumb, or flexible prices. We enrich Coibion and Gorodnichenko framework by incorporating heterogeneity in nominal wage setting behavior among households. We solve this DSGE model and estimate it using Bayesian techniques for the US economy from 1955 to 2008. The estimation results show the relevance of heterogeneity in wage setting among households. More importantly, we identify qualitative and quantitative business cycle features allowed by the heterogeneity in wage rigidity, such as the persistence in price and wage inflation, which a standard NK model with only Calvo-type wage rigidity fails to achieve. We also show that modeling wage-rigidity heterogeneity—as opposed to standard Calvo wages—amplifies the macroeconomic output fluctuations resulting from a technology shock while it mitigates the output fluctuations following a monetary tightening.

We investigate how key monetary policy instruments and financial regulation affect the banking firm. We take the user-cost approach to the construction of prices for financial services and use quarterly data on the U.S. commercial banking sector, over the period from 1992 to 2016, obtained from the Federal Deposit Insurance Corporation. We use the symmetric generalized Barnett variable profit function to derive demands for and supplies of monetary and nonmonetary goods and provide evidence consistent with neoclassical microeconomic theory. We find that the compensated price elasticities of banking technology are small in magnitude. Yet a hypothetical policy experiment shows that even small changes in the holding costs of financial goods can result in significant changes in user costs and the quantities demanded and supplied.

This paper studies how model uncertainty influences economic fluctuation when trend inflation is high. We introduce Hansen and Sargent’s [(2008) Robustness, Princeton University Press] robust control techniques into a New Keynesian model with non-zero trend inflation. We reveal the following three points. First, we find that robust monetary policy responds more aggressively. This aggressiveness increases with trend inflation. Second, as the trend inflation rises, the response of macroeconomic variables is larger under robust policy. Third, stronger robustness tends to lead to indeterminate equilibrium as trend inflation increases. Consequently, the economy might be volatile when trend inflation is high due to robustness from the view of both variance and determinacy. We interpret the results as indicating that the model uncertainty might be the one of the factors causing large macroeconomic fluctuations when trend inflation is high.

Table 4 of this article was incorrectly published with subheadings in Table 4 missing. The publisher regrets this error and the correct version of Table 4 is presented here. Table 4.

Present-value multipliers for different specifications of fiscal policy in the new Keynesian model with rational expectations and with adaptive learning

	Rational expectations				Adaptive learning
	Impact	1 year	4 years	6 years	Impact	1 year	4 years	6 years
Strategy 1: Lump-sum financing (baseline model)
$\frac{{PV\left( {\Delta Y} \right)}}{{PV\left( {\Delta G} \right)}}\$	0.51	0.50	0.46	0.43	1.01	1.00	0.99	0.97
$\frac{{PV\left( {\Delta C} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.29	−0.30	−0.34	−0.37	0.09	0.09	0.07	0.06
$\frac{{PV\left( {\Delta I} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.20	−0.20	−0.20	−0.20	−0.08	−0.08	−0.09	−0.09
Strategy 2: Capital tax financing
$\frac{{PV\left( {\Delta Y} \right)}}{{PV\left( {\Delta G} \right)}}\$	0.32	0.29	0.12	0.02	0.43	0.38	0.20	0.08
$\frac{{PV\left( {\Delta C} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.23	−0.26	−0.40	−0.48	−0.08	−0.12	−0.27	−0.37
$\frac{{PV\left( {\Delta I} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.68	−0.69	−0.70	−0.71	−0.80	−0.80	−0.81	−0.82
Strategy 3: Labor tax financing
$\frac{{PV\left( {\Delta Y} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.64	−0.68	−0.85	−0.95	0.48	0.47	0.43	0.40
$\frac{{PV\left( {\Delta C} \right)}}{{PV\left( {\Delta G} \right)}}\$	−1.07	−1.10	−1.24	−1.33	−0.37	−0.38	−0.41	−0.43
$\frac{{PV\left( {\Delta I} \right)}}{{PV\left( {\Delta G} \right)}}\$	−0.71	−0.71	−0.73	−0.74	−0.17	−0.18	−0.18	−0.18