Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

Implications of ernbodied technological change for development economics

Employing a embodied technologic change model in which the time decision of scrapping old vintages of capital and adopt newer one is endogenous we show that the elasticity of substitutions among capital and labor plays a key role in determining the optimum life span of capital. In particular, for the CD case the life span of capital does not depend on the relative price of it. The estimation of the model's long-run investment function shows, for a Panel data set consisting of 125 economies for 25 years, that the price elasticity of investment is lower than one; we rejected the CD specification. Our calibration for the US suggests 0.4 for the technical elasticity of substitution. In order to get a theoretical consistent concept of aggregate capital we derive the relative price profile for a shadow second-hand market for capital. The shape of the model's theoretical price curve reproduces the empírical estimation of it. \lVe plug the calibrate version of the long-run solution of the model to a cross-section of economies data set to get the implied TFP, that is, the part of the productivity which is not explained by the model. We show that the mo dei represent a good improvement, comparing to the standard neoc!assical growth model with CD production function and disembodied technical change, in accounting the world diversity in productivity. In addition the model describes the fact that a very poor economy can experience fast growth based on capital accumulation until the point of becoming a middle income economy; from this point on it has to rely on TFP increase in order to keep growing.

Investment-specific technological change and the Brazilian macroeconomy

We studied the effects of changes in banking spreads on distributions of income, wealth and consumption as well as the welfare of the economy. This analysis was based on a model of heterogeneous agents with incomplete markets and occupational choice, in which the informality of firms and workers is a relevant transmission channel. The main finding is that reductions in spreads for firms increase the proportion of entrepreneurs and formal workers in the economy, thereby decreasing the size of the informal sector. The effects on inequality, however, are ambiguous and depend on wage dynamics and government transfers. Reductions in spreads for individuals lead to a reduction in inequality indicators at the expense of consumption and aggregate welfare. By calibrating the model to Brazil for the 2003-2012 period, it is possible to find results in line with the recent drop in informality and the wage gap between formal and informal workers.

A technological innovation for capsicums production in Peru: the mechatronics arms

Peru agricultural exports have increased in recent years due to (i) free trade agreements with many countries (United States, Canada, European Union, China, Thailand, Singapore, Japan, Chile, among others), (ii) an increasing international demand for healthy products, (iii) country´s economic development and (iv) more private investments in this sector (Velazco 2012). Also, if we can compare among Peru three main regions (Coast, Andean highlands and the Jungle), It is the Coast (western region) that has a developed agricultural production due to unique weather conditions, private investments, public infrastructure, transport costs and quality of land (Gomez, 2008). This country development is also related to the production of non-traditional products for export like asparagus, artichokes, capsicums, bananas, grapes, among others; produced by agro industrial companies and small farmers and that are mainly labor intensive (Gomez, 2008 and Velazco, 2012). This very successful export diversification and self-discovery process was the result of a combination of strong natural comparative advantages (mainly excellent agro climatic conditions) and a significant innovation effort. It meant the introduction and expansion of new products and markets, the entry of new firms, and experimental research and the adoption of new techniques and process technologies developed abroad (in irrigation, crop management, post-harvesting, sanitary control, storage and packing) to produce high-quality, niche (gourmet) and higher value-added products, in line with consumer trends in sophisticated food markets. In products such as asparagus, mango, organic coffee and capsicums, Peru has become a leading world exporter (OECD). For this reason one of the government main tasks for the next years is to meet urgent agriculture producer’s needs in the areas of technological Innovation and business management (MINAG). In this context, this thesis analyzes the applicability of a new technology – the mechatronic arms – specifically to capsicums production sector in Peru. We chose Capsicums production sector (paprika, chilli pepper) because is mainly labor intensive and is the sector where my family company (DIROSE SAC) operates. This innovation consists in a 40 arms mechatronic combine, and it was first created in order to improve the efficiency on the labor intensive phase of harvest for this kind of agriculture products. It is estimated that a laborer with brief training operating the machine would be equivalent to 40 people that not only would work during daytime, but also on the night shift as well. Also, using this new technology can allow a company to make additional crops that would increase their yields and annual revenues. This thesis was developed as a business plan to make this new product available for other agriculture companies that operates in the capsicums production sector in Peru; however, this new technology has the potential to be modified in order to be available to other kind of agriculture products, in Peru and other countries.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.

Automatic model selection for forecasting Brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Knowledge disclosure as a weapon in technological change battles: straight shot or backfire?

This work aims at evaluating how effective is knowledge disclosure in attenuating institutional negative reactions caused by uncertainties brought by firms’ new strategies that respond to novel technologies. The empirical setting is from an era of technological ferment, the period of the introduction of the voice over internet protocol (VoIP) in the USA in the early 2000’s. This technology led to the convergence of the wireline telecommu- nications and cable television industries. The Institutional Brokers’ Estimate System (also known as the I/B/E/S system) was used to capture reactions of securities analysts, a revealed important source of institutional pressure on firms’ strategies. For assessing knowledge disclosure, a coding technique and a established content analysis framework were used to quantitatively measure the non-numerical and unstructured data of transcripts of business events occurred at that time. Eventually, several binary response models were tested in order to assess the effect of knowledge disclosure on the probability of institutional positive reactions. The findings are that the odds of favorable institutional reactions increase when a specific kind of knowledge is disclosed. It can be concluded that knowledge disclosure can be considered as a weapon in technological changes situations, attenuating adverse institutional reactions to the companies’ strategies in environments of technological changes.