Second-Order Approximation to the Rotemberg Model around a Distorted Steady State

Less is known about social welfare objectives when it is costly to change prices, as in Rotemberg (1982), compared with Calvo-type models. We derive a quadratic approximate welfare function around a distorted steady state for the costly price adjustment model. We highlight the similarities and differences to the Calvo setup. Both models imply inflation and output stabilization goals. It is explained why the degree of distortion in the economy influences inflation aversion in the Rotemberg framework in a way that differs from the Calvo setup.

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

Modelling Housing Prices using a Present Value State Space Model

This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregressive process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The Öltered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidenced for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.

Modelling Housing Prices using a Present Value State Space Model

This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregression process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The filtered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidence for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.

Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

The Taylor Principle and (In-) Determinacy in a New Keynesian Model with hiring Frictions and Skill Loss

We introduce duration dependent skill decay among the unemployed into a New-Keynesian model with hiring frictions developed by Blanchard/Gali (2008). If the central bank responds only to (current, lagged or expected future) inflation and quarterly skill decay is above a threshold level, determinacy requires a coefficient on inflation smaller than one. The threshold level is plausible with little steady-state hiring and firing ("Continental European Calibration") but implausibly high in the opposite case ("American calibration"). Neither interest rate smoothing nor responding to the output gap helps to restore determinacy if skill decay exceeds the threshold level. However, a modest response to unemployment guarantees determinacy. Moreover, under indeterminacy, both an adverse sunspot shock and an adverse technology shock increase unemployment extremely persistently.

A Foundation System and a State System - Private-School Implications on Welfare and Education Expenditure

This paper examines the effects of two different education - financing systems: a foundation system and a state system on the level and distribution of resources devoted to education in the presence of private schools. We use political economy approach where households differ in their level of income, and the central tax rate used to nance education is determined by a majority vote. Our analysis focuses on implications of allowing for a private-school option. To evaluate the importance of private schools we develop a computational model and calibrate it using USA data. The results reveal that the private school option is very important quantitatively in terms of welfare, total resources spent on education and equity.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

The Stagnation Regime of the New Keynesian Model and Current US Policy

In Evans, Guse, and Honkapohja (2008) the intended steady state is locally but not globally stable under adaptive learning, and unstable deflationary paths can arise after large pessimistic shocks to expectations. In the current paper a modified model is presented that includes a locally stable stagnation regime as a possible outcome arising from large expectation shocks. Policy implications are examined. Sufficiently large temporary increases in government spending can dislodge the economy from the stagnation regime and restore the natural stabilizing dynamics. More specific policy proposals are presented and discussed.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Adaptive Continuous time Markov Chain Approximation Model to General Jump-Diusions

We propose a non-equidistant Q rate matrix formula and an adaptive numerical algorithm for a continuous time Markov chain to approximate jump-diffusions with affine or non-affine functional specifications. Our approach also accommodates state-dependent jump intensity and jump distribution, a flexibility that is very hard to achieve with other numerical methods. The Kolmogorov-Smirnov test shows that the proposed Markov chain transition density converges to the one given by the likelihood expansion formula as in Ait-Sahalia (2008). We provide numerical examples for European stock option pricing in Black and Scholes (1973), Merton (1976) and Kou (2002).

A New Model Of Trend Inflation

This paper introduces a new model of trend (or underlying) inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. The bounds placed on trend inflation mean that standard econometric methods for estimating linear Gaussian state space models cannot be used and we develop a posterior simulation algorithm for estimating the bounded trend inflation model. In an empirical exercise with CPI inflation we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model.

Fiscal Sustainability in a New Keynesian Model

Recent work on optimal monetary and fiscal policy in New Keynesian models suggests that it is optimal to allow steady-state debt to follow a random walk. Leith and Wren-Lewis (2012) consider the nature of the timeinconsistency involved in such a policy and its implication for discretionary policy-making. We show that governments are tempted, given inflationary expectations, to utilize their monetary and fiscal instruments in the initial period to change the ultimate debt burden they need to service. We demonstrate that this temptation is only eliminated if following shocks, the new steady-state debt is equal to the original (efficient) debt level even though there is no explicit debt target in the government’s objective function. Analytically and in a series of numerical simulations we show which instrument is used to stabilize the debt depends crucially on the degree of nominal inertia and the size of the debt-stock. We also show that the welfare consequences of introducing debt are negligible for precommitment policies, but can be significant for discretionary policy. Finally, we assess the credibility of commitment policy by considering a quasi-commitment policy which allows for different probabilities of reneging on past promises. This on-line Appendix extends the results of this paper.

Fiscal Sustainability in a New Keynesian Model

Recent work on optimal monetary and fiscal policy in New Keynesian models suggests that it is optimal to allow steady-state debt to follow a random walk. Leith and Wren-Lewis (2012) consider the nature of the timeinconsistency involved in such a policy and its implication for discretionary policy-making. We show that governments are tempted, given inflationary expectations, to utilize their monetary and fiscal instruments in the initial period to change the ultimate debt burden they need to service. We demonstrate that this temptation is only eliminated if following shocks, the new steady-state debt is equal to the original (efficient) debt level even though there is no explicit debt target in the government’s objective function. Analytically and in a series of numerical simulations we show which instrument is used to stabilize the debt depends crucially on the degree of nominal inertia and the size of the debt-stock. We also show that the welfare consequences of introducing debt are negligible for precommitment policies, but can be significant for discretionary policy. Finally, we assess the credibility of commitment policy by considering a quasi-commitment policy which allows for different probabilities of reneging on past promises. This on-line Appendix extends the results of this paper.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.