Towards an Empirically Founded Behavioural Macro
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Towards an empirically founded behavioural macro Exploiting the Internet as a Data Source for Social Science Nikos Askitas, IZA { Institute of Labor Economics November 24, 2017 Workshop Nowcasting & Big Data, Banco Central de la Rep´ublicaArgentina Table of contents 1. Introduction 2. The Internet 3. Unemployment and Health 4. Shares: referenda, Case/Shiller Index 5. Hourly Data: Traffic Jams, [Askitas, 2016a] 6. Toll Index 7. A couple of thoughts Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics1 Introduction • Emerging data contains so much more than just economics • Public debate is shifting from being about economics to being about (identity) politics. • All of the non-economics parts in the data feed back into the economy so they are essential even for economists. Behavioural Macro \Macro" is often perceived as short for macroeconomics but I mean it in a rather more general sense. Although I am a Mathematician working as an economist it would be a waste to not think of macroscience more generally. Many reasons to do so: • Economics is a derivative discipline founded on Math, Psychology, Sociology etc Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics2 • Public debate is shifting from being about economics to being about (identity) politics. • All of the non-economics parts in the data feed back into the economy so they are essential even for economists. Behavioural Macro \Macro" is often perceived as short for macroeconomics but I mean it in a rather more general sense. Although I am a Mathematician working as an economist it would be a waste to not think of macroscience more generally. Many reasons to do so: • Economics is a derivative discipline founded on Math, Psychology, Sociology etc • Emerging data contains so much more than just economics Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics2 • All of the non-economics parts in the data feed back into the economy so they are essential even for economists. Behavioural Macro \Macro" is often perceived as short for macroeconomics but I mean it in a rather more general sense. Although I am a Mathematician working as an economist it would be a waste to not think of macroscience more generally. Many reasons to do so: • Economics is a derivative discipline founded on Math, Psychology, Sociology etc • Emerging data contains so much more than just economics • Public debate is shifting from being about economics to being about (identity) politics. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics2 Behavioural Macro \Macro" is often perceived as short for macroeconomics but I mean it in a rather more general sense. Although I am a Mathematician working as an economist it would be a waste to not think of macroscience more generally. Many reasons to do so: • Economics is a derivative discipline founded on Math, Psychology, Sociology etc • Emerging data contains so much more than just economics • Public debate is shifting from being about economics to being about (identity) politics. • All of the non-economics parts in the data feed back into the economy so they are essential even for economists. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics2 Why Macro? { I The first answer is that this is the nature of the data I am working with. But a second deeper answer which vindicates us working with this type of data might go as follows. The standard reductionist model says I can understand your shopping habits from your molecules if I can deconstruct how they are put together into creating you and how they respond to environmental impulses (i.e. \once a micro level is fixed, macro levels are fixed too; supervenience"). The reason then why this approach is not followed is that our (e.g. computing) means cannot capture it all or that it is too complex. So we need \theory" or \models", so reductionism has it, as a means of speculatively filling the gaps and we hence confine ourselves to macro data as a kind of compromise. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics3 • "the most important part of learning is actually forgetting", [Tishby et al., 2000] (bottleneck theory in deep learning / artificial neural networks). • \macro beats micro in causal inference as measured by Effective Information", [Hoel et al., 2013] (e.g. stochasticity of hidden layers of a neural network vs the more sentient nature of the network itself: causality is determined at the micro but first at the macro level. Why Macro? { II While most of causal economics research is of the \micro" kind we keep coming up short in putting it together into a macro picture and in fact this might be the case not just because of complexity but for more profound reasons. • \Forgetting" information is fundamental for \remembering" it ([Askitas, 2018]) (e.g. the tangent space of a smooth manifold, the Taylor expansion of a function, digital vs. analogue technology, or impressionist paintings). Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics4 • \macro beats micro in causal inference as measured by Effective Information", [Hoel et al., 2013] (e.g. stochasticity of hidden layers of a neural network vs the more sentient nature of the network itself: causality is determined at the micro but first at the macro level. Why Macro? { II While most of causal economics research is of the \micro" kind we keep coming up short in putting it together into a macro picture and in fact this might be the case not just because of complexity but for more profound reasons. • \Forgetting" information is fundamental for \remembering" it ([Askitas, 2018]) (e.g. the tangent space of a smooth manifold, the Taylor expansion of a function, digital vs. analogue technology, or impressionist paintings). • "the most important part of learning is actually forgetting", [Tishby et al., 2000] (bottleneck theory in deep learning / artificial neural networks). Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics4 Why Macro? { II While most of causal economics research is of the \micro" kind we keep coming up short in putting it together into a macro picture and in fact this might be the case not just because of complexity but for more profound reasons. • \Forgetting" information is fundamental for \remembering" it ([Askitas, 2018]) (e.g. the tangent space of a smooth manifold, the Taylor expansion of a function, digital vs. analogue technology, or impressionist paintings). • "the most important part of learning is actually forgetting", [Tishby et al., 2000] (bottleneck theory in deep learning / artificial neural networks). • \macro beats micro in causal inference as measured by Effective Information", [Hoel et al., 2013] (e.g. stochasticity of hidden layers of a neural network vs the more sentient nature of the network itself: causality is determined at the micro but first at the macro level. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics4 Why Behavioural? In trying to forecast a time series it is (most of the time) very hard to beat an ARIMA model as seasoned forecasting practitioners know. On the other hand of course nothing will fail as miserably as an ARIMA model in predicting a break from past patterns. In those cases we need models which use ARIMA predictions as a baseline and are informed by behaviourally determined variables as captured by various measuring devices. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics5 Empirically Founded Of course in some sense all of our models are behavioural and empirically founded at least in the sense that we postulate behavioural determinants which we concoct and then we seek to validate with our data. We know however (see "rationality") that our assumptions have been too naive. I am thinking about behavioural data captured in real time in the internet hence empirically founded behavioural macro. Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics6 Empirically founded behavior: porn vs math 3 months worth of daily searches in the US 80 0 6 6 0 6 6 6 6 6 1 0 6 0 6 6 0 0 0 0 0 0 6 4 1 0 6 5 6 0 2 3 4 2 0 5 3 5 1 1 60 1 4 2 3 5 4 5 2 1 1 5 1 1 2 3 4 5 3 4 5 3 5 4 5 1 2 2 4 3 4 2 3 2 4 1 4 3 3 5 3 5 2 2 4 1 2 3 4 5 1 40 Google Searches for Porn Google Searches for Math 5 5 5 5 5 5 4 4 5 5 4 4 2 4 3 3 4 4 2 3 5 4 3 2 2 3 2 3 3 3 2 4 5 4 2 1 3 1 1 2 1 1 1 20 2 3 1 5 1 4 2 3 1 1 2 5 4 3 1 2 1 4 5 6 0 6 0 6 0 6 6 0 6 0 6 0 6 0 6 1 6 0 0 6 0 6 0 0 6 0 0 01aug2017 01sep2017 01oct2017 01nov2017 date Data from Google Trends and own calculations Nikos Askitas j IDSC { Research Data Center of IZA { Institute of Labor Economics7 Empirically founded behavior: traffic jams 7 days worth of hourly Stau Searches in Germany 7 100 80 6 7 7 8 7 15 6 60 16 6 6 14 17 5 8 8 16 8 6 17 7 16 40 13 17 15 1617 17 16 4 18 17 5 15 5 8 'Stau' (traffic jam) 12 9 15 15 12 131415 1718 3 18 5 9 11 1415 9 9 14 10 19 910 12 16 13 18 9 5 78 16 12 19 18 14 13 10 20 6 14 4 1012 19 17 11 10 13 101213 4 11 20 18 910 111213 4 1012 19 11 4 21 78 20 18 11 19 1 3 4 56 3 3 20 1921 24 2223 192122 4 21 20 2 2122 3 202123 2 2223 24123 20 2324123 222301 2122230 23012 2224 3 2 1 1 2 0 06nov2017 12:34:11 03nov2017 12:59:42 04nov2017 12:51:12 05nov2017 12:42:41 07nov2017 12:25:40 08nov2017 12:17:10 09nov2017 12:08:40 10nov201712:00:09 date Data from Google Trends and own calculations Nikos Askitas j IDSC { Research Data Center of IZA { Institute of