Outline of the remainder of this book Chapter 2 This introduces the classical linear regression model (CLRM). The ordinary least squares (OLS) estimator is derived and its interpretation discussed. The conditions for OLS optimality are stated and explained. A hypothesis testing framework is developed and examined in the context of the linear model. Examples employed include Jensen’s classic study of mutual fund performance measurement and tests of the ‘overreaction hypothesis’ in the context of the UK stock market. Chapter 3 This continues and develops the material of chapter 2 by generalising the bivariate model to multiple regression -- i.e. models with many variables. The framework for testing multiple hypotheses is outlined , and measures of how well the model fits the data are described. Case studies include modelling rental values and an application of principal components analysis to interest rate modelling. Chapter 4 Chapter 4 examines the important but often neglected topic of diagnostic testing . The consequences of violations of the CLRM assumptions are described, along with plausible remedial steps. Model-building philosophies are discussed, with particular reference to the general-to-specific approach. Applications covered in this chapter include the determination of sovereign credit ratings. Chapter 5 This presents an introduction to time series models, including their motivation and a description of the characteristics of financial data that they can and cannot capture. The chapter commences with a presentation of the features of some standard models of stochastic ( white noise, moving average, autoregressive and mixed ARMA ) processes. The chapter continues by showing how the appropriate model can be chosen for a set of actual data, how the model is estimated and how model adequacy checks are performed. The generation of forecasts from such models is discussed, as are the criteria by which these forecasts can be evaluated. Examples include model-building for UK house prices, and tests of the exchange rate covered and uncovered interest parity hypotheses. Chapter 6 This extends the analysis from univariate to multivariate models . Multivariate models are motivated by way of explanation of the possible existence of bi-directional causality in financial relationships, and the simultaneous equations bias that results if this is ignored. Estimation techniques for simultaneous equations models are outlined. Vector autoregressive(VAR) models , which have become extremely popular in the empirical finance literature, are also covered. The interpretation of VARs is explained by way of joint tests of restrictions, causality tests, impulse responses and variance decompositions. Relevant examples discussed in this chapter are the simultaneous relationship between bid--ask spreads and trading volume in the context of options pricing, and the relationship between property returns and macroeconomic variables. Chapter 7 The first section of the chapter discusses unit root processes and presents tests for non-stationarity in time series . The concept of and tests for cointegration, and the formulation of error correction models , are then discussed in the context of both the single equation framework of Engle--Granger, and the multivariate framework of Johansen. Applications studied in chapter 7 include spot and futures markets, tests for cointegration between international bond markets and tests of the purchasing power parity hypothesis and of the expectations hypothesis of the term structure of interest rates .
2.10 A special type of hypothesis test: the t-ratio 65 2.11 An example of the use of a simple t-test to test a theory in finance: can US mutual funds beat the market? 67 2.12 Can UK unit trust managers beat the market? 69 2.13 The overreaction hypothesis and the UK stock market 71 2.14 The exact significance level 74 2.15 Hypothesis testing in EViews -- example 1: hedging revisited 75 2.16 Estimation and hypothesis testing in EViews -- example 2: the CAPM 77 Appendix: Mathematical derivations of CLRM results 81 3 Further development and analysis of the classical linear regression model 88 3.1 Generalising the simple model to multiple linear regression 88 3.2 The constant term 89 3.3 How are the parameters (the elements of the β vector) calculated in the generalised case? 91 3.4 Testing multiple hypotheses: the F-test 93 3.5 Sample EViews output for multiple hypothesis tests 99 3.6 Multiple regression in EViews using an APT-style model 99 3.7 Data mining and the true size of the test 105 3.8 Goodness of fit statistics 106 3.9 Hedonic pricing models 112 3.10 Tests of non-nested hypotheses 115 Appendix 3.1: Mathematical derivations of CLRM results 117 Appendix 3.2: A brief introduction to factor models and principal components analysis 120
Based on federalism and redistributive politics, this study proposes that further fiscal devolution promotes economic development, but more so for urban residents than for rural residents. With more fiscal decentralization, the urban-rural income inequity will further rise. fiscal decentralization may not facilitate identity switches between rural and urban residents fiscal decentralization has positive effect on both groups in terms of their economic increase fiscal decentralization further increase income inequity across the groups
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