Aggregate density forecast of models using disaggregate data - A copula approach

We propose a novel copula approach to producing density forecasts of economic aggregates combining models using disaggregate data. Our copula approach is more flexible compared to existing techniques, because it is applicable to any econometric model that produces density forecasts. We construct a set of Monte Carlo studies to investigate the properties of the suggested approach. In our empirical application, we use the Norwegian index for goods consumption (VKI) and the Norwegian consumer price index for underlying inflation (CPI-ATE). We find that the copula approach compares well to alternative methods using recursive out-of-sample estimation.publishedVersio

A SMARTer way to forecast

In this paper we describe the newly developed System for Model Analysis in Real Time (SMART) used for forecasting and model analysis in Norges Bank. While the long-term goal is to include all empirical models used in forecasting in Norges Bank, the emphasis in this paper will be on the empirical model systems for inflation and GDP. SMART builds on Norges Bank’s previous System for Averaging short-term Models (SAM), but with greater flexibility and a richer set of models. In addition, SMART contains a real-time database with a wide-ranging set of historical data, forecasts from empirical models, Norges Bank’s forecasts from Monetary Policy Reports (MPR) and forecasts from other institutions (e.g. Statistics Norway). Overall, SMART seems to provide good forecasts and will be a useful tool in the monetary policy process.publishedVersio

Conditional Forecasting with DSGE Models - a Conditional Copula Approach

DSGE models may be misspecified in many dimensions, which can affect their forecasting performance. To correct for these misspecifications we can apply conditional information from other models or judgment. Conditional information is not accurate, and can be provided as a probability distribution over different outcomes. These probability distributions are often provided by a set of marginal distributions. To be able to condition on this information in a structural model we must construct the multivariate distribution of the conditional information, i.e. we need to draw multivariate paths from this distribution. One way to do this is to draw from the marginal distributions given a correlation structure between the different marginal distributions. In this paper we use the theoretical correlation structure of the model and a copula to solve this problem. The copula approach makes it possible to take into account more flexible assumption on the conditional information, such as skewness and/or fat tails in the marginal density functions. This method may not only improve density forecasts from the DSGE model, but can also be used to interpret the conditional information in terms of structural shocks/innovations.publishedVersio

Markedsdisiplin av bankene : Sett i lys av finanskrisen

I denne masteroppgaven vil jeg prøve å finne svar på flere hypoteser når det gjelder markedsdisiplin av bankene. Det vil altså si om innskyterne tar forskjellige faresignaler ved en bank som tegn på at banken tar uønsket høy risiko, og dermed vil trekke ut innskuddene sine eller kreve kompensasjon i form av høyere rente. I mine data som jeg har tilgjengelig igjennom ORBOF, så kan jeg skille mellom innskudd som er garantert og innskudd som ikke er garantert av Bankenes sikringsfond. Og min første hypotese er at det er de som ikke har sine innskudd garantert av Bankenes sikringsfondet, som også er de innskyterne som utfører mest markedsdisiplin av bankene. Det kommer av at det er de som vil ha størst insentiv til å disiplinere bankene. Mitt datasett strekker seg fra 1.kvartal 2004 til 2 kvartal 2010, dermed har jeg også muligheten for å teste om markedsdisiplinen endret seg under og etter finanskrisens inntog. Min andre hypotese var da at bankene ville oppleve å bli disiplinert i større grad under og etter finanskrisen, fordi den bidro til å svekke innskyternes tillit til banksystemet, og at de av den grunn fikk større insentiv til å disiplinere bankene. I litteraturen på området så har mange brukt en såkalt ”redusert form” modell, hvor de avhengige variablene er innskuddsrenten og innskuddsveksten. Og de mener å finne evidens for at innskyterne disiplinerer bankene fordi disse to variablene virker å bli påvirket av forskjellige risikoindikatorer ved bankene. Men i og med at flere av disse variablene er vanskelige å skaffe seg informasjon om, i hvert fall i Norge, og at de antagelig også vil påvirke etterspørselen av innskudd, så viser jeg at denne modellen ikke estimerer de effektene vi er ute etter. Derfor vil jeg i denne oppgaven heller ta jeg opp tråden etter Karas et al. (2009), som ser mer direkte på tilbudsfunksjonen til innskyterne. Denne vil vise seg å være identifiserbar med de forutsetninger jeg antar i denne oppgaven. Og ved hjelp av denne modellen har jeg undersøkt om innskyterne responderer på mål på soliditeten til bankene, og om innskyterne disiplinerer bankene ved uvanlig høy rente. I den betydning at en uvanlig høy rente for en bank i forhold til resten av markedet, i seg selv er en indikator på problemer hos en bank. Jeg vil i denne oppgaven også legge større vekt på identifikasjonsproblemet enn det som er vanlig i litteraturen, derfor trekker jeg også inn etterspørselfunksjonen. Jeg danner altså en fullstendig modell for både tilbudet og etterspørselen etter innskudd, men som jeg vil vise i kapittel 5.3, så vil bare tilbudsfunksjonen være identifiserbar med mine forutsetninger. Mine resultater skal vise seg å forkaste begge mine hypoteser. I den forstand at det viser seg av resultatene å være evidens for at det er de med innskuddene garanterte, som også er de som bedriver mest markedsdisiplin. Både gjennom rentedisiplinering og soliditetsdisiplinering. Altså det motsatte av det man kunne forventet seg ut fra økonomisk teori. Når det gjelder finanskrisens påvirkning på markedsdisiplineringen, så viser mine resultater at de innskytere med garanterte innskudd økte soliditetsdisiplineringen, men reduserte rentedisiplineringen i forbindelse med finanskrisen. Mens resultatene viser det motsatte for de som ikke har sine innskudd garantert. For øvrig så har all estimering blitt utført i Stata 11. Mitt datasett inneholder konfidensiell informasjon om bankene i Norge, og kan derfor ikke publiseres

Conditional Forecasting with DSGE Models - a Conditional Copula Approach

DSGE models may be misspecified in many dimensions, which can affect their forecasting performance. To correct for these misspecifications we can apply conditional information from other models or judgment. Conditional information is not accurate, and can be provided as a probability distribution over different outcomes. These probability distributions are often provided by a set of marginal distributions. To be able to condition on this information in a structural model we must construct the multivariate distribution of the conditional information, i.e. we need to draw multivariate paths from this distribution. One way to do this is to draw from the marginal distributions given a correlation structure between the different marginal distributions. In this paper we use the theoretical correlation structure of the model and a copula to solve this problem. The copula approach makes it possible to take into account more flexible assumption on the conditional information, such as skewness and/or fat tails in the marginal density functions. This method may not only improve density forecasts from the DSGE model, but can also be used to interpret the conditional information in terms of structural shocks/innovations

Symbolic Stationarization of Dynamic Equilibrium Models

Dynamic equilibrium models are specified to track time series with unit root-like behavior. Thus, unit roots are typically introduced and the optimality conditions adjusted. This step requires tedious algebra and often leads to algebraic mistakes, especially in models with several unit roots. We propose a symbolic algorithm that simplies the step of rendering non-stationary models stationary. It is easy to implement and works when trends are stochastic or deterministic, exogenous or endogenous. Three examples illustrate the mechanics and the properties of the approach. A comparison with existing methods is provided

Banks’ Liquidity Situation During the Financial Turmoil in Autumn 2008

In autumn 2008, many banks encountered considerable difficulties with portfolio funding. This article describes the impact on Norwegian banks. The authors also look at the characteristic features of the banks that faced the biggest problems

Aggregate density forecast of models using disaggregate data - A copula approach

We propose a novel copula approach to producing density forecasts of economic aggregates combining models using disaggregate data. Our copula approach is more flexible compared to existing techniques, because it is applicable to any econometric model that produces density forecasts. We construct a set of Monte Carlo studies to investigate the properties of the suggested approach. In our empirical application, we use the Norwegian index for goods consumption (VKI) and the Norwegian consumer price index for underlying inflation (CPI-ATE). We find that the copula approach compares well to alternative methods using recursive out-of-sample estimation

Documentation of NEMO - Norges Bank’s Core Model for Monetary Policy Analysis and Forecasting

This paper explains the basic mechanisms of Norges Bank’s core model for monetary policy analysis and forecasting (NEMO). NEMO has recently been extended with an oil sector to incorporate important channels of shocks to the Norwegian economy. We show how the effects of a change in the oil price depends on whether the price change is due to demand or supply factors in the international economy. Other extensions of the model include a more detailed modeling of the foreign sector. The paper also uses NEMO to highlight important driving forces of the Norwegian economy after the fall in the oil price. We demonstrate that the model has a reasonable empirical fit compared to VAR models

Nowcasting Norwegian household consumption with debit card transaction data

We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate now cast of the first quarter of 2020, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic.publishedVersio